Trajectory Tracking

In this paper, a fourth-order dimensional nonlinear model is proposed for a diesel engine equipped with a Variable Geometry Turbocharger (VGT) and an Exhaust Gas Recirculation (EGR) valve. The referred model which takes into account the engine crankshaft speed dynamics and the air-path dynamics is a non-minimum phase unstable system. The fuel flow rate W f which is considered as input for the engine crankshaft subsystem acts as an external perturbation for the three-order dimensional non-minimum phase air-path subsystem. The global control objectives are to track desired values of engine speed, intake manifold pressure and compressor flow mass rate which can be suitably chosen according to low emission criterions. For the considered objectives two loop based dynamical feedback stabilization control is proposed: an inner loop and an outer loop. The inner loop considers a control based on Lyapunov function which realizes the desired engine speed trajectory tracking. The outer loop concerns EGR & VGT control. It is developed from a particular extended nonlinear air-path subsystem with its modified outputs. This outer loop ensures both the desired intake manifold pressure and the desired compressor mass flow rate trajectory tracking. The outer loop dynamical feedback stabilization control provides also the external fuel mass flow rate perturbation rejection. The robustness and efficiency of the proposed method are demonstrated by simulation results.

Accurate biomechanical models of the human body were essential for understanding, predicting, and simulating human movement. While complex, multi-segment models offered high fidelity, their computational cost often limited their practical application. This study focused on optimizing a simplified three-link lower limb model to enhance its dynamic performance by incorporating impact of ground reaction forces while maintaining computational efficiency. To address the model's limitations in capturing complex human movements, evolutionary algorithms were employed to refine its parameters. The Ant Lion optimizer, Cuckoo search, Dragonfly algorithm, and Fminsearch algorithm were utilized to determine optimal values for link lengths, mass distribution, and joint stiffness. The model performance was assessed by comparing simulated lower limb with ground reaction forces, joint torques, and angles through experimental data from dynamic tasks. The fitness functions were multi-objective in nature to simultaneously minimize the three lower limb angle prediction errors. The Ant Lion optimizer demonstrated a significant advantage in terms of convergence rate and dynamic model parameter optimization with minimal root mean square error. While the Fminsearch algorithm caused overfitting of parameters, making it unsuitable for the current application, it could be useful in hybrid optimization techniques. This research aimed to identify the most suitable optimization algorithm for improving model accuracy and to explore the trade-offs between model simplicity and dynamic fidelity.

In this study, we aim at developing a mechanical device to support humans rehabilitation motion of their wrist joint instead of or to help a physical therapist. Pneumatic parallel manipulator is introduced as the mechanical equipment from a view that pneumatic actuators bring minute force control property owing to the air compressibility and parallel manipulator's feature of multiple degrees of freedom is suitable for a complex motion of human wrist joint. Impedance control system is introduced to realize several rehabilitation modes. The validity of the proposed system is confirmed through some experiments.

This investigation is centrally focused on the comprehensive evolution and enhancement of FOODIEBOT, an adaptive service automaton with a wide range of functionalities. Its capabilities encompass sophisticated image processing methods, seamlessly integrated via mobile applications (APP) and web interfaces, tailored specifically for intricate object manipulation in dining hall settings.During its developmental phase, the precise calibration of PID controller coefficients emerged as an essential requirement. The model underwent meticulous scrutiny through detailed simulations using MATLAB software. Following this phase, its operational efficiency navigating through circular, elliptical, spiral, and octagonal trajectories underwent rigorous examination, utilizing optimization methodologies like BAS, PSO, POA, and EO. The exposition emphasizes the diverse dispersion of optimized coefficients within each algorithmic framework. The pinnacle of this effort involved a comprehensive evaluation of pathway performance, amalgamating insights from each optimization paradigm. The discussion extensively delineates both simulated and realtime performance metrics of the robot, validating the accuracy and reliability of simulation in deriving PID controller values.In the comprehensive evaluation of methodologies and the robotic system's effectiveness, the BAS technique excels in operational efficiency. This method consistently outperforms its counterparts in execution time, primarily due to its meticulous optimization of particle count. The comparative analysis across various trajectories reveals intriguing insights. The EO approach showcases outstanding accuracy in Path 1, while the POA method achieves optimal precision in Path 3. Impressively, the BAS technique demonstrates unparalleled accuracy in Path 4. Furthermore, in terms of solution optimization, the BAS method consistently displays the shortest execution times across all traversed pathways. When examining maximum velocity along these routes, the PSO method excels in Paths 1, 3, and 4, consistently achieving the highest speeds. Notably, Path 2 uniquely displays the peak velocity attained by the POA method.This article presents comprehensive insights into the constituent elements of the robotic system's design. The inquiry delves into the intricate nuances of optimization methodologies, elucidating their profound impact on the service automaton's performance across diverse orientations. The pragmatic implications underscore the critical role of temporal considerations in the judicious selection of these methodologies. The observed congruence between simulated and practical performance serves as a definitive validation, affirming the precision of simulation computations and the subsequent derivation of PID controller values.

This paper proposes a new path-following control algorithm for the machine tool servo systems. The controller first decomposes the normal tracking error from the advancing tangential error. A dynamic decoupling procedure is then proposed to the advancement mechanism and the trajectory tracking mechanism in the machine tool servo. A dynamic decoupling controller is propose to operate on the decomposed tangential and a normal tracking error. The normal control minimizes the perpendicular tracking error while the tangential control maintains a desired feed rate. The method is applied on an experimental X-Y table, and the results show good tracking and trajectory following characteristics. The new algorithm enables the design of a non-overshooting controller along the path. This would allow no-overcutting process for the machine tool operation.

A robust nonlinear control law that achieves trajectory tracking control for unmanned aerial vehicles (UAVs) equipped with synthetic jet actuators (SJAs) is presented in this paper. A key challenge in the control design is that the dynamic characteristics of SJAs are nonlinear and contain parametric uncertainty. The challenge resulting from the uncertain SJA actuator parameters is mitigated via innovative algebraic manipulation in the tracking error system derivation along with a robust nonlinear control law employing constant SJA parameter estimates. A key contribution of the paper is a rigorous analysis of the range of SJA actuator parameter uncertainty within which asymptotic UAV trajectory tracking can be achieved. A rigorous stability analysis is carried out to prove semiglobal asymptotic trajectory tracking. Detailed simulation results are included to illustrate the effectiveness of the proposed control law in the presence of wind gusts and varying levels of SJA actuator parameter uncertainty.

A robust nonlinear control law that achieves trajectory tracking control for unmanned aerial vehicles (UAVs) equipped with synthetic jet actuators (SJAs) is presented in this paper. A key challenge in the control design is that the dynamic characteristics of SJAs are nonlinear and contain parametric uncertainty. The challenge resulting from the uncertain SJA actuator parameters is mitigated via innovative algebraic manipulation in the tracking error system derivation along with a robust nonlinear control law employing constant SJA parameter estimates. A key contribution of the paper is a rigorous analysis of the range of SJA actuator parameter uncertainty within which asymptotic UAV trajectory tracking can be achieved. A rigorous stability analysis is carried out to prove semiglobal asymptotic trajectory tracking. Detailed simulation results are included to illustrate the effectiveness of the proposed control law in the presence of wind gusts and varying levels of SJA actuator param...

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Performances of flexible link multibody systems, in terms of accuracy and repeatability, can be negatively affected by link flexibility that causes unwanted vibration. Therefore, advanced controllers are necessary for vibration suppression. The synthesis of such controllers typically relies on the knowledge of all the system state variables, whose direct measurement is complicated, or at least of a meaningful set of the most relevant ones. For such a reason, these state variables should be estimated through state observers. In particular, state observers based on reduced-order dynamic models should be employed, to reduce the computational effort. This paper shows some preliminary results on state estimation in flexible-link multibody systems based on nonlinear, reduced-order dynamic model formulated through independent coordinates. Reduction is performed through a modified Craig-Bampton strategy. Numerical simulations of a six-bar planar mechanism show that the proposed observer delivers accurate estimates of both the rigid and elastic variables.

Wave Based Control (WBC) has been proven to be very effective in combining position and oscillation control of underactuated flexible multibody systems [1]. This paper proposes an extension of the method aimed at ensuring accurate path tracking of a load suspended by a planar Cartesian crane, even in the presence of non ideal actuators and sensors. The basic configuration of WBC [1] uses the measured mechanical wave coming back from the flexible system to the actuator, the so called returning wave, to accomplish both position control and active swing damping. In Cartesian cranes, two independent controllers for the X and Y axis set the trolley position references, q T , to be the sum of two components (q denotes both the X and Y motion). The first component is set to half the load displacement reference, q L , and is the launch wave. The second component is the measured returning wave, denoted bq . The addition of this second component performs active vibration damping while moving ...

Performances of flexible link multibody systems, in terms of accuracy and repeatability, can be negatively affected by link flexibility that causes unwanted vibration. Therefore, advanced controllers are necessary for vibration suppression. The synthesis of such controllers typically relies on the knowledge of all the system state variables, whose direct measurement is complicated, or at least of a meaningful set of the most relevant ones. For such a reason, these state variables should be estimated through state observers. In particular, state observers based on reduced-order dynamic models should be employed, to reduce the computational effort. This paper shows some preliminary results on state estimation in flexible-link multibody systems based on nonlinear, reduced-order dynamic model formulated through independent coordinates. Reduction is performed through a modified Craig-Bampton strategy. Numerical simulations of a six-bar planar mechanism show that the proposed observer del...

Modern control schemes adopted in multibody systems take advantage of the knowledge of a large set of measurements of the most important state variables to improve system performances. In the case of flexible-link multibody systems, however, the direct measurement of these state variables is not usually possible or convenient. Hence, it is necessary to estimate them through accurate models and a reduced set of measurements ensuring observability. In order to cope with the large dimension of models adopted for flexible multibody systems, this paper exploits model reduction for synthesizing reduced-order nonlinear state observers. Model reduction is done through a modified Craig-Bampton strategy that handles effectively nonlinearities due to large displacements of the mechanism and through a wise selection of the most important coordinates to be retained in the model. Starting from such a reduced nonlinear model, a nonlinear state observer is developed through the extended Kalman filt...

This paper develops and validates experimentally a feedback strategy for the reduction of the link deformations in rest-to-rest motion of mechanisms with flexible links, named Delayed Reference Control (DRC). The technique takes advantage of the inertial coupling between rigid-body motion and elastic motion to control the undesired link deformations by shifting in time the position reference through an action reference parameter. The action reference parameter is computed on the fly based on the sensed strains by solving analytically an optimization problem. An outer control loop is closed to compute the references for the position controllers of each actuator, which can be thought of as the inner control loop. The resulting multiloop architecture of the DRC is a relevant advantage over several traditional feedback controllers: DRC can be implemented by just adding an outer control loop to standard position controllers. A validation of the proposed control strategy is provided by ap...

We give a new convenient parametrization of linear controllers that solve the problem of signal invariance (or disturbance cancellation) for MIMO plants. As an example of application of the obtained results we consider the trajectory tracking problem for non-holonomic wheeled transport robots.

The propulsors of organisms from paramecia to dolphins have ball-and-socket jointed bases that allow large-amplitude, low-friction swings. Their olivo-cerebellar control also remains unchanged. Yet, the propulsive surfaces of small animals vary widely from flagellar filaments (0 < Re < 5) to flapping fins (Re > 20) with an intermediate range of Reynolds number (5 < Re < 20) where both types are present in the same swimming animal. Analysis suggests that these unsteady surfaces are mechanical oscillators coupled to their nonlinear wakes. A low-friction-driven oscillator that can interact with the oscillators of models or live swimming and flying animals could help us understand the hydro-structural events prompting the evolution of such surfaces at specific Re values. A gearless underdamped (in air) hemispherical motor oscillator is described where energetic efficiency increases by a factor of eight as the forces drop by a factor of ten from 10 N. The electrical effici...

In this paper, a simple neuron-based adaptive controller for trajectory tracking is developed for nonholonomic mobile robots without velocity measurements. The controller is based on structural knowledge of the dynamics of the robot and the odometric calculation of robot position only. The wheel actuator dynamics is also taken into account. An approximation network approximates a nonlinear function involving robot dynamic parameters so that no knowledge of those parameters is required. The proposed controller is robust not only to structured uncertainty such as mass variation but also to unstructured one such as disturbances. The real-time control of mobile robot is achieved through the online learning of the approximation network. The system stability and the boundness of tracking errors are proved using Lyapunov stability theory. Computer simulations with circular as well as square reference trajectories are presented that confirm the simplicity and effectiveness of the proposed tracking control law. The efficacy of the proposed control law is tested experimentally on a differentially driven mobile robot. Both simulation and experimental results are described in detail.

Trajectory tracking is an important aspect of autonomous vehicles. The idea behind trajectory tracking is the ability of the vehicle to follow a predefined path with zero steady state error. The difficulty arises due to the nonlinearity of vehicle dynamics. Therefore, this paper proposes a stable tracking control for an autonomous vehicle. An approach that consists of steering wheel control and lateral control is introduced. This control algorithm is used for a non-holonomic navigation problem, namely tracking a reference trajectory in a closed loop form. A proposed future prediction point control algorithm is used to calculate the vehicle's lateral error in order to improve the performance of the trajectory tracking. A feedback sensor signal from the steering wheel angle and yaw rate sensor is used as feedback information for the controller. The controller consists of a relationship between the future point lateral error, the linear velocity, the heading error and the reference...

In this paper, we propose a novel control structure that can achieve fast, safe and precise landing of a VTOL (vertical takeoff and landing) UAV onto a vertically oscillating landing pad. The control structure consists of three modules to achieve these goals: a motion estimation module, a trajectory generation module and a tracking control module. In the tracking control module, an ARC (Adaptive Robust Controller) is designed to robustly adapt the nonlinear ground effect to enable a quadrotor accurately track a given reference trajectory. In the trajectory generation module, a time-optimal reference trajectory for the quadrotor is generated such that it converges from the initial height precisely to the platform height with zero relative velocity (for smooth landing). The landing time duration is as short as possible, and physical safety constraints (position, velocity, acceleration bounds etc.) are satisfied during the entire landing process. The above two modules use the motion information of the quadrotor and the platform in absolute coordinate system (inertial frame). In the motion estimation module, we estimate the UAV and platform positions online from only the measurement of the relative distance between the UAV and the platform, as well as the inertia measurement of the UAV. An UKF (unscented Kalman Filter) is constructed and the estimated parameters are fed to the other two modules in real time. Comparative simulation and experimental results are presented to validate the performances of the proposed control structure.

This paper proposes a differential flatness-based method for maneuvering a quadrotor so that its position follows a specified velocity vector field. Existing planning and control algorithms often give a 2D or 3D velocity vector field to be followed by a robot. However, quadrotors have complex nonlinear dynamics that make vector field following difficult, especially in aggressive maneuvering regimes. This paper exploits the differential flatness property of a quadrotor's dynamics to control its position along a given vector field. Differential flatness allows for the analytical derivation of control inputs in order to control the 12D dynamical state of the quadrotor such that the 2D or 3D position of the quadrotor follows the flow specified by a given vector field. The method is derived mathematically, and demonstrated in numerical simulations and in experiments with a quadrotor robot for three different vector fields.

New methodologies for Fault Tolerant Control (FTC) are proposed in order to compensate actuator faults in nonlinear systems. These approaches are based on the representation of the nonlinear system by a Takagi-Sugeno model. Two control laws are proposed requiring simultaneous estimation of the system states and of the occurring actuator faults. The first approach concerns the stabilization problem in the presence of actuator faults. In the second, the system state is forced to track a reference trajectory even in faulty situation. The control performance depends on the estimation quality; indeed, it is important to accurately and rapidly estimate the states and the faults. This task is then performed with an Adaptive Fast State and Fault Observer (AFSFO) for the first case, and a Proportional-Integral Observer (PIO) in the second. Stability conditions are established with Lyapunov theory and expressed in a Linear Matrix Inequality (LMI) formulation to ease the design of FTC. Furthermore, relaxed stability conditions are given with the use of Polya's theorem. Some simulation examples are given in order to illustrate the proposed approaches.

This communication addresses the problem of fault detection and isolation (FDI) for nonlinear systems. Despite a lot of works in this area, model-based FDI for nonlinear systems still remains a difficult task. These two last decades have witnessed growing popularity of Takagi-Sugeno models. This attractivity relies essentially on two advantages: (i) that kind of models can be considered as universal approximator i.e. they offer a good flexibility at the modeling stage; (ii) their structure based on the blending of linear models is able to fully take advantages of the advances of modern control theory. It is then rather natural to trying to extent the well-established model-based FDI methods for linear systems to the systems that can be represented by Takagi-Sugeno models.

El artículo presenta el diseño de un controlador lineal cuadrático gaussiano (LQG) para regular los ángulos de cabeceo, elevación y viaje de un prototipo de un helicóptero de tres grados de libertad (3 DOF). El controlador LQG se diseña a partir de un modelo lineal y se ajustan los parámetros del regulador en base a simulaciones realizadas con el modelo no lineal, evaluando la respuesta transitoria del sistema en lazo cerrado para diferentes valores de la señal de referencia, garantizando obtener la mayor variación de los ángulos alrededor del punto de equilibrio. La validación experimental de la estrategia de control se realiza sobre un prototipo construido por los autores, la respuesta transitoria de los datos simulados se compara con los datos experimentales, para los tres grados de libertad del helicóptero, observando que el modelo matemático se ajusta a la dinámica del prototipo y se cumplen las condiciones de diseño.

In variational quantum algorithms (VQAs), the most common objective is to find the minimum energy eigenstate of a given energy Hamiltonian. In this paper, we consider the general problem of finding a sufficient control Hamiltonian structure that, under a given feedback control law, ensures convergence to the minimum energy eigenstate of a given energy function. By including quantum non-demolition (QND) measurements in the loop, convergence to a pure state can be ensured from an arbitrary mixed initial state. Based on existing results on strict control Lyapunov functions, we formulate a semidefinite optimization problem, whose solution defines a non-unique control Hamiltonian, which is sufficient to ensure almost sure convergence to the minimum energy eigenstate under the given feedback law and the action of QND measurements. A numerical example is provided to showcase the proposed methodology.

Engine electrification is a critical technology in the promotion of engine fuel efficiency, among which the electrified turbocharger is regarded as the promising solution in engine downsizing. By installing electrical devices on the turbocharger, the excess energy can be captured, stored, and re-used. The electrified turbocharger consists of a variable geometry turbocharger (VGT) and an electric motor (EM) within the turbocharger bearing housing, where the EM is capable in bi-directional power transfer. The VGT, EM, and exhaust gas recirculation (EGR) valve all impact the dynamics of air path. In this paper, the dynamics in an electrified turbocharged diesel engine (ETDE), especially the couplings between different loops in the air path is analyzed. Furthermore, an explicit principle in selecting control variables is proposed. Based on the analysis, a model-based multi-input multi-output (MIMO) decoupling controller is designed to regulate the air path dynamics. The dynamics analysis and controller are successfully validated through experiments and simulations.

Many construction tasks need time and effort from people. Thus, modern technology is one of its purposes to aid task completion. These include grouting floor tile joints. It takes time and effort to complete this process. Traditional methods for grouting floor tile joints between tiles are inefficient and require the worker to stay on his knees for extended periods, which can cause health issues. Thus, mobile robots are needed to automate floor grouting. This study describes the design and development of a mobile robot model to grout floor tile joints uniformly and effectively. Compared to manual approaches, the proposed robot can clean tiles quickly and precisely. The robot fills based on user-defined workspace coordinates. Set the robot at the start location to begin grouting. The robot then follows the user-defined code and coordinates to fill the requirement. After grout filling, the robot returned to the starting position to clean. This model was evaluated and exhibited faster, more accurate grouting and a shorter injection process than manual approaches.

We define the term semantic trajectories of moving objects as a sequence of spatiotemporal points with associated semantic information. Spatio-temporal points are directly generated by sensors that capture the position of a moving object in time. In this paper, we work on a marine mammal trajectory case study. We consider the seals' trajectories to understand their behavior within groups and identify their activities simultaneously in the same place. We define the activities of mobile objects in the form of rules given by the domain expert. To gain knowledge of trajectories, we use the GALACTIC platform, which is a new platform based on Formal Concept Analysis (FCA) for computing a concept lattice from heterogeneous and complex data. Data in GALACTIC are described by predicates according to their types. Here, we will use interval sequence plugins to analyse the trajectories of seals, where interval sequences are represented by a set of time intervals in which a seal performs an activity in a geographical zone. Finally, the results show the simultaneous activities of a group of seals.

Cars are a prevalent mode of transportation for both people and goods, with B-class hatchbacks being particularly popular in Indonesia. However, road traffic crashes remain a major concern, contributing millions of deaths annually, primarily due to human error. Autonomous vehicles offer a promising solution to mitigate these issues by reducing reliance on human control. In particular, Level 3 autonomous vehicles enhance road safety, enable independent mobility, reduce traffic congestion, and allow drivers to engage in non-driving tasks. This study proposes an autonomous vehicle model that employs a trajectory tracking approach using Model Predictive Control (MPC), a robust and widely adopted control strategy in autonomous systems. A three-degree-of-freedom (3-DOF) vehicle dynamic model was developed and analyzed through co-simulation using Car-Sim and Simulink to evaluate its performance during a double-lane change maneuver. The simulation results demonstrate that the vehicle accurately follows the reference trajectory and exhibits excellent dynamic performance. The roll angle remained consistently low, ranging between 0.024 and 0.026 radians-well below the rollover threshold of 0.14 radians-demonstrating strong roll stability. The slip angle varied between-0.013 and 0.0135 radians, nearly 12 times lower than the critical limit, indicating optimal traction and directional control. Lateral acceleration ranged from-3.59 m/s² to 3.41 m/s², and yaw rate remained within-7.78°/s to 7.25°/s, both well within safe operational bounds. These findings confirm that the proposed MPC-based control framework enables precise path tracking, robust stability, and reliable handling performance in dynamic driving scenarios.

Differential flatness, a property of some dynamic systems which has been recognized only recently, has made possible the development of new tools to control complex nonlinear dynamic systems. Many dynamic non linear systems have been proved to be differentially flat. In this paper, it is shown that the inertial position coordinates of an aircraft can be considered as differential flat outputs for its flight guidance dynamics. Since this differential flatness property is implicit, a neural network is introduced, as a numerical device, to deal with the inversion of the guidance dynamics. Then, once conveniently structured and trained, a neural network is able to generate in real time directives to conventional autopilot systems so that a reference trajectory can be tracked. Numerical results relative to the training of the neural network and to trajectory tracking are displayed and discussed.

In this paper, an operator-based nonlinear vibration control of a flexible arm experimental system using piezoelectric actuator with hysteresis is discussed. The piezoelectric actuator exhibits hysteretic effects in the output displacement responses, which can cause inaccuracy and undesirable oscillations, where Prandtl-Ishlinskii(PI) model is used to describe the hysteresis. In order to compensate the hysteretic effects, using the model, and based on the concept of Lipschiz operators and robust right coprime factorization, nonlinear vibration controllers are proposed to the experimental system. Further, a tracking operator design method is given to ensure the tracking performance of the considered system. Finally, simulation results and experimental results are presented to support the effectiveness of the proposed design method.

NSLS-II, the successor to NSLS (National Synchrotron Light Source) at Brookhaven National Lab, is scheduled to be open to users worldwide by 2015 as a world-class advanced synchrotron light source because of its unique features: its half-mile-circumference (792 m) Storage Ring provides the highest beam intensity (500 mA) at medium-energy (3 GeV) with sub-nm-rad horizontal emittance (down to 0.5 nm-rad) and diffraction-limited vertical emittance at a wavelength of 1 Å (<8 pm-rad). As the eyes of NSLS-II accelerators to observe fascinating particle beams, beam diagnostics and controls systems are designed to monitor and diagnose the electron beam quality so that NSLS-II could be tuned up to reach its highest performance. The design and implementation of NSLS-II diagnostics and controls are described. Preliminary commissioning results of NSLS-II accelerators, including Linac, Booster, and Storage Ring, are presented. INTRODUCTION The construction of NSLS-II (NSLS-2) began in Mar. 20...

Model-based trajectory tracking has become a widely used technique for automated driving system applications. A critical design decision is the proper selection of a vehicle model that achieves the best trade-off between real-time capability and robustness. Blending different types of vehicle models is a recent practice to increase the operating range of model-based trajectory tracking control applications. However, current approaches focus on the use of longitudinal speed as the blending parameter, with a formal procedure to tune and select its parameters still lacking. This work presents a novel approach based on lateral accelerations, along with a formal procedure and criteria to tune and select blending parameters, for its use on model-based predictive controllers for autonomous driving. An electric passenger bus traveling at different speeds over urban routes is proposed as a case study. Results demonstrate that the lateral acceleration, which is proportional to the lateral for...

Introduction: The Bachelor in Secondary Education (BSE) major in Science is a four-year program that has a good number of chemistry and physics courses to enable the graduates to teach physical sciences courses. After the implementation of the new teacher education curricular program, the BSE major in Science program had its first batch of graduates in the academic year 2021-2022. Yet, studies on the implementation of this new curricular offering have not yet been carried out.

Trajectory tracking control for wheeled mobile robots (WMRs) faces significant challenges in real-world applications due to actuator faults, longitudinal and lateral slippage. This study proposes an innovative dual-loop control structure combining adaptive sliding mode control (ASMC) and backstepping control (BC), supported by robust fault observers, to address these challenges. The dynamic loop employs ASMC to handle model uncertainties and disturbances, while the kinematic loop integrates BC with fault information provided by the observers, enabling real-time error compensation. Simulation results show that the proposed method significantly reduces tracking errors and improves stabilization time compared to traditional SMC and ASMC controllers. The system exhibits enhanced fault tolerance and disturbance rejection, maintaining stability under both normal and faulty conditions. The effectiveness of this approach is demonstrated through simulations and theoretical analysis, ensuring system stability using Lyapunov stability theory. The proposed method enhances robustness, adaptability, and stability of WMRs, contributing significantly to the field of mobile robotics under adverse conditions.

This paper presents a new method for fault tolerant control of nonlinear systems described by Takagi-Sugeno fuzzy systems with unmeasurable premise variables. The idea is to use a reference model and design a new control law to minimize the state deviation between a healthy reference model and the eventually faulty actual model. This scheme requires the knowledge of the system states and of the occurring faults. These signals are estimated from a Proportional-Integral Observer (PIO) or Proportional-Multi-Integral Observer (PMIO). The fault tolerant control law is designed by using the Lyapunov method to obtain conditions which are given in Linear Matrix Inequality formulation (LMI). Finally, an example is included.

This paper investigates the problem of fault tolerant control (FTC) design for nonlinear Takagi-Sugeno (T-S) models with measurable premise variables. The idea is to synthesize a fault tolerant controller ensuring state trajectory tracking. Based on Lyapunov theory, new less conservative approaches a r e proposed in term of Linear Matrix Inequality (LMI). A P I observer is needed to estimate simultaneously the faults and the faulty system states in order to reconfigure the FTC la w. A numerical example is considered to compare the conservatism of the proposed FTC approaches with the existing one and to illustrate the effectiveness of the FTC technique vs. the classical controller design methodology.

The advent of autonomous vehicles necessitates a redefinition of road safety regulations, considering a controller can possess better driving skills than an average person. The work presented here partly focuses on a vehicle dynamics model development to help imitate and control vehicle drifting maneuvers. As we see, a professional driver drifting through the traffic while keeping the car safe, it can be utilized to avoid accidents at high speeds, if required. Although drifting can produce higher yaw rates than the regular driving regime, these control capabilities have not yet been exploited in the current automotive control systems. Therefore, this report focuses on developing a vehicle dynamics model to simulate the drifting of an autonomous vehicle that utilizes this high yaw rate property. Drifting control capabilities will increase the vehicle's ability to avoid collision scenarios where higher than typical yaw rates are required.
The other part of the report uses vehicle kinematics equations to generate feasible path planning algorithms for any autonomous vehicle. If we constrain these vehicle kinematics equations for linearly varying curvature of the path, they are called Clothoid curves. This linearly varying curvature is analogous to having a continuous lateral acceleration on the vehicle while cornering, i.e., avoiding jerks. Here, Clothoids are used for the interpolation of waypoints along the path. A mission planner defines waypoints a few meters apart; then, GPS coordinates are used to check whether the vehicle is following these waypoints correctly. Therefore, waypoint interpolation is required for the continuous feed of track coordinates to the controller to make faster corrections for the cross-track error and not to wait every time for a low-rate GPS signal. Also, the Clothoid curves are used in road construction, thus also providing the ability to create a road model along with a vehicle model which can further be used for better state estimation.

The control of a mobile robot using a linear model with uncertainty for design purposes is investigated. The uncertainty arises from the variation of the operation point of the mobile robot. Robust Control Theory is used for the controller design, which allows dealing with systems whose parameters may vary between certain bounds. The proposed controller has shown, in experimentation tests, an acceptable performance and an easy and simple practical implementation. Also, an application of the proposed controller to a leader-following problem is shown; in it, the relative position between robots is obtained through a laser.

In this paper, a trajectory tracking control for a nonholonomic mobile robot by the integration of a kinematic controller and torque controllers is investigated. The proposed neural torque controllers (PNTCs) are based on a Gaussian radial basis function neural network (RBFNN) modeling technique, which are used to compensate the mobile robot dynamics, and bounded unknown disturbances. Also, the PNTCs are not dependent of the robot dynamics neither requires the off-line training process. The stability analysis and the convergence of tracking errors, as well as the learning algorithm for weights are guaranteed with basis on Lyapunov theory. In addition, the simulations results shows the efficiency of the PNTCs.

Norm-Optimal Iterative Learning Control has potential to significantly increase the accuracy of many trajectory tracking tasks which can be found in industry. The algorithm can achieve very low levels of tracking error and the number of iterations required to reach minimal error is small compared to many other Iterative Learning Control Algorithms. However, in the current format, the algorithm is not attractive to industry because it requires a large number of calculations to be performed at each sample instant. This implies that control hardware must be very fast which is expensive, or that the sample frequency must be reduced which can result in reduced performance. To remedy these problems, a revised version, Fast Norm-Optimal Iterative Learning Control is proposed which is significantly simpler and faster to implement. The new version is tested both in simulation and in practice on a three axis industrial gantry robot.

In this paper, discrete time inverse optimal trajectory tracking for a class of non-linear positive systems is proposed. The scheme is developed for MIMO (multi-input, multi-output) ane systems. This approach is adapted for glycemic control of type 1 diabetes mellitus (T1DM) patients. The control law calculates the insulin delivery rate in order to prevent hyperglycemia levels. A neural model is obtained from an on-line neural identifier, which uses a recurrent neural network, trained with the extended Kalman filter (EKF); this neural model has an ane form, which permits the applicability of inverse optimal control scheme. The proposed algorithm is tuned to follow a desired trajectory; this trajectory reproduces the glucose absorption of a healthy person. Simulation results illustrate the aplicability of the control law in biological processes.

The purpose of this communication is to present a new nonlinear control structure for trajectory tracking taking explicitly into account actuators saturation. Here trajectory tracking by a four rotor aircraft is considered. After introducing the flight dynamics equations for the four rotor aircraft, a trajectory tracking control structure based on a two layer non linear inverse approach is adopted and a supervision layer is introduced to take into account the possible actuators saturation.

This paper considers the problem of fixed wing unmanned air vehicles following straight lines and orbits. To account for ambient winds, we use a path following approach as opposed to trajectory tracking. The unique feature of this paper is that we explicitly account for roll angle constraints and flight path angle constraints. The guidance laws are derived using the theory of nested saturations.

This article deals with the trajectory tracking prob- lem for the angular velocity of a dc-motor shaft using a Buck- Boost-converter as the switch regulated electronic drive. The main result of our proposed control scheme is that a linear time-varying controller results from the consideration of the exact tracking error dynamics and static passive output feedback controller design. The measuring of the angular velocity is not really necessary and the control law is synthesized using only a linear time-varying combination of the converter current and voltage variables. The voltage reference trajectory for the converter is generated exploiting a partial differential flatness property of the combined system. The reference trajectories of the average control and the input current are calculated via stored energy considerations and planning for the initial and final stationary regimes. The discrete switching control realization of the designed continuous feedback control law is accomplished by means of a traditional PWM-modulation scheme. Experimental results are provided.

In this paper the three axes slewing maneuver and the vibration suppression of a flexible spacecraft are studied. The satellite has a central rigid body and two flexible appendages. An adaptive-robust control scheme is used to achieve the satellite's large angle trajectory tracking and suppress the vibration of the appendages. The appendages are considered as the Euler-Bernoulli beams, and the piezoelectric layers, which are attached to both sides of the appendages, are used as the actuators. For mathematical modeling, the Lagrange-Rayleigh-Ritz technique is utilized. The adaptive-robust control method is robust against parameter uncertainties and disturbances. The number of system parameters that are estimated by the adaptive law are reduced; this makes the proposed controller suitable for online control. Finally, the system behavior is simulated and the effects of varying parameters are studied. The simulation results show the excellent performance of the controller.

The article proposes flatness-based control and a Kalman Filter-based disturbance observer for solving the control problem of a robotic exoskeleton under time-delayed exogenous disturbances. A two-link lower-limb robotic exoskeleton is used as a case study. It is proven that this robotic system is differentially flat. The robot is considered to be subject to unknown contact forces at its free-end which in turn generate unknown disturbance torques at its joints. It is shown that the dynamic model of the robotic exoskeleton can be transformed into the input-output linearized form and equivalently into the linear canonical Brunovsky form. This linearized description of the exoskeleton's dynamics is both controllable and observable. It allows for designing a stabilizing feedback controller with the use of the pole-placement (eigenvalues assignment) method. Moreover, it allows for solving the state estimation problem with the use of Kalman Filtering (the use of the Kalman Filter on the flatness-based linearized model of nonlinear dynamical systems is also known as Derivative-free nonlinear Kalman Filtering). Furthermore, (i) by extending the state vector of the exoskeleton after considering as additional state variables the additive disturbance torques which affect its joints and (ii) by redesigning the Kalman Filter as a disturbance observer, one can achieve the real-time estimation of the perturbations that affect this robotic system. Finally, by including in the controller of the exoskeleton additional terms that compensate for the estimated disturbance torques, the perturbations' effects can be eliminated and the precise tracking of reference trajectories by the joints of this robot can be ensured.

The use of robotic limb exoskeletons is growing fast either for rehabilitation purposes or in an aim to enhance human ability for lifting heavy objects or for walking for long distances without fatigue. The paper proposes a nonlinear optimal control approach for a lower-limb robotic exoskeleton. The method has been successfully tested so far on the control problem of several types of robotic manipulators and this paper shows that it can also provide an optimal solution to the control problem of limb robotic exoskeletons. To implement this control scheme, the state-space model of the lower-limb robotic exoskeleton undergoes first approximate linearization around a temporary operating point, through first-order Taylor series expansion and through the computation of the associated Jacobian matrices. To select the feedback gains of the H-infinity controller an algebraic Riccati equation is solved at each time-step of the control method. The global stability properties of the control loo...

In this work another perturbation estimation sliding mode based control algorithm is introduced for a class of robotic systems in the presence of structured and unstructured uncertainties and external disturbances. The effects of these uncertainties are combined into a single quantity. The full order plant with the actuator voltages as control inputs is assumed in control design. The decentralized control scheme with only the partial state feedback is applied. A modification of the switching functions with perturbation estimation is introduced. The salient feature of this approach is that the perturbations are effectively treated by a computationally straightforward procedure. The proposed controller is applied on a minimal configuration direct drive robot mechanism.