Papers by Randa Herzallah
Estimation of the probabilistic description of quantum physical systems
2022 IEEE International Conference on Quantum Computing and Engineering (QCE), Sep 1, 2022
arXiv (Cornell University), Jun 30, 2022
A new control method that considers all sources of uncertainty and noises that might affect the t... more A new control method that considers all sources of uncertainty and noises that might affect the time evolutions of quantum physical systems is introduced. Under the proposed approach, the dynamics of quantum systems are characterised by probability density functions (pdfs), thus providing a complete description of their time evolution. Using this probabilistic description, the proposed method suggests the minimisation of the distance between the actual pdf that describes the joint distribution of the time evolution of the quantum system and the external electric field, and the desired pdf that describes the system target outcome. We start by providing the control solution for quantum systems that are characterised by arbitrary pdfs. The obtained solution is then applied to quantum physical systems characterised by Gaussian pdfs and the form of the optimised controller is elaborated. Finally, the proposed approach is demonstrated on a real molecular system and two spin systems showing the effectiveness and simplicity of the method.
Dual adaptive control of nonlinear stochastic systems
ABSTRACT
Decentralised fully probabilistic design for stochastic networks with multiplicative noise
International Journal of Systems Science, May 12, 2023
In this paper, a novel decentralised control framework based on decentralised fully probabilistic... more In this paper, a novel decentralised control framework based on decentralised fully probabilistic design (DFPD) is proposed for a class of stochastic dynamic complex systems with multiple multiplicative noises. Compared with the existing conventional DFPD, the new procedure is improved by modifying the Riccati equation in order to deal with multiple multiplicative noises. Considering the stochastic nature of complex systems, the systems’ dynamical behaviours are fully charaterised by probabilistic state-space models. In this way, a complete description of the components of the subsystems is provided. In addition, probabilistic message passing architecture is introduced to provide communication between neighbouring subsystems and to harmonise the actions between the local nodes. To illuminate the effectiveness of the proposed framework, a three inverted pendulum system numerical example is presented and the results are compared with the conventional DFPD.
Asian Journal of Control, Nov 11, 2022
This paper develops a novel probabilistic framework for stochastic nonlinear and uncertain contro... more This paper develops a novel probabilistic framework for stochastic nonlinear and uncertain control problems. The proposed framework exploits the Kullback-Leibler divergence to measure the divergence between the distribution of the closed-loop behavior of a dynamical system and a predefined ideal distribution. To facilitate the derivation of the analytic solution of the randomized controllers for nonlinear systems, transformation methods are applied such that the dynamics of the controlled system becomes affine in the state and control input. Additionally, knowledge of uncertainty is taken into consideration in the derivation of the randomized controller. The derived analytic solution of the randomized controller is shown to be obtained from a generalized state-dependent Riccati solution that takes into consideration the stateand control-dependent functional uncertainty of the controlled system. The proposed framework is demonstrated on an inverted pendulum on a cart problem, and the results are obtained.
Pipeline leak detection using artificial neural network: Experimental study
Pipeline transportation of resources is considered a vital method due to low operational cost, an... more Pipeline transportation of resources is considered a vital method due to low operational cost, and simple design and implementation. However, potential leaks compromise the integrity of this method. Pipeline leaks consequences are major concerns due to resources loss, environmental impact and potential human injuries and fatalities. This paper investigates neural network based probabilistic decision support system for detecting the presence of leak in pipeline transportation systems. The probabilistic model correlates measurements of inlet and outlet pressures and flow to leak status. Several pipeline leak detection methods have been developed, nevertheless, noisy data, and changes in operational conditions are the main challenges that limit the performance of leak detection leading to high false alarms. ANN properties of noise-immunity characteristics, parallel structure and correspondingly fast processing and classification capabilities provide enhanced performance of leak detection.
arXiv (Cornell University), Sep 30, 2022
There is a fundamental limit to what is knowable about atomic and molecular scale systems. This f... more There is a fundamental limit to what is knowable about atomic and molecular scale systems. This fuzziness is not always due to the act of measurement. Other contributing factors include system parameter uncertainty, functional uncertainty that originates from input functions, and sensors noises to mention a few. This indeterminism has led to major challenges in the development of accurate control methods for atomic scale systems. To address the probabilistic and uncertain nature of these systems, this work proposes a novel control framework that considers the representation of the system quantum states and the quantification of its physical properties following a probabilistic approach. Our framework is fully probabilistic. It uses the Shannon relative entropy from information theory to design optimal randomised controllers that can achieve a desired outcome of an atomic scale system. Several experiments are carried out to illustrate the applicability and effectiveness of the proposed approach.
SSRN Electronic Journal
There is a fundamental limit to what is knowable about atomic and molecular scale systems. This f... more There is a fundamental limit to what is knowable about atomic and molecular scale systems. This fuzziness is not always due to the act of measurement. Other contributing factors include system parameter uncertainty, functional uncertainty that originates from input functions, and sensors noises to mention a few. This indeterminism has led to major challenges in the development of accurate control methods for atomic scale systems. To address the probabilistic and uncertain nature of these systems, this work proposes a novel control framework that considers the representation of the system quantum states and the quantification of its physical properties following a probabilistic approach. Our framework is fully probabilistic. It uses the Shannon relative entropy from information theory to design optimal randomised controllers that can achieve a desired outcome of an atomic scale system. Several experiments are carried out to illustrate the applicability and effectiveness of the proposed approach.
A new control method that considers all sources of uncertainty and noises that might affect the t... more A new control method that considers all sources of uncertainty and noises that might affect the time evolutions of quantum physical systems is introduced. Under the proposed approach, the dynamics of quantum systems are characterised by probability density functions (pdfs), thus providing a complete description of their time evolution. Using this probabilistic description, the proposed method suggests the minimisation of the distance between the actual pdf that describes the joint distribution of the time evolution of the quantum system and the external electric field, and the desired pdf that describes the system target outcome. We start by providing the control solution for quantum systems that are characterised by arbitrary pdfs. The obtained solution is then applied to quantum physical systems characterised by Gaussian pdfs and the form of the optimised controller is elaborated. Finally, the proposed approach is demonstrated on a real molecular system and two spin systems showi...
Deep learning-based networks for automated recognition and classification of awkward working postures in construction using wearable insole sensor data
Automation in Construction, 2022
Accurate QRS detection plays a pivotal role in the diagnosis of heart diseases and the estimation... more Accurate QRS detection plays a pivotal role in the diagnosis of heart diseases and the estimation of heart rate variability and respiration rate. The investigation of R-peak detection is a continuing concern in computer-based ECG analysis because current methods are still inaccurate and miss heart beats. This paper presents a different algorithm to the state-of-the-art Empirical Mode Decomposition based algorithms for R-peak detection. Although our algorithm is based on Empirical Mode Decomposition, it uses an adaptive threshold over a sliding window combined with a gradient-based and refractory period checks to differentiate large Q peaks and reject false R peaks. The performance of the algorithm was tested on multiple databases including the MIT-BIH Arrhythmia database, Preterm Infant Cardio-Respiratory Signals database and the Capnobase dataset, achieving a detection rate over 99%. Our modified approach outperforms other published results using Hilbert or derivative-based methods...
Generalised Fully Probabilistic Controller Design for Nonlinear Affine Systems
2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP), 2019
This paper demonstrates the extension of the Fully Probabilistic Design control method to nonline... more This paper demonstrates the extension of the Fully Probabilistic Design control method to nonlinear discrete-time stochastic dynamical systems which are affine in the input signal and are also affected by multiplicative noises. As nonlinear systems do not usually have a closed form analytic control solution, many current control methods are mostly based on linearising the system equations first and then deriving the analytic control solution. To address this problem, this paper proposes a new method which does not require the linearisation of the nonlinear system equations. This will be achieved by expressing these nonlinear equations in a different variation that will be affine in the state as well as control input, thus yielding a quadratic in the state optimal performance index. This transformation of the nonlinear system equations to an affine form in the state will result into a state dependent Riccati Equation. The derived state dependent Riccati equation is a generalisation o...
An Efficient Message Passing Algorithm for Decentrally Controlling Complex Systems
International Journal of Control, 2021
Scientific Reports
This paper proposes a unified probabilistic control framework for a class of stochastic systems w... more This paper proposes a unified probabilistic control framework for a class of stochastic systems with both control input and state time delays. Both of the stochastic nature and time delays in the system dynamics are considered simultaneously, thus providing a comprehensive and rigorous control methodology. The problem is formulated in a fully probabilistic framework, where the system dynamics and its controller are fully characterised by arbitrary probabilistic models. In this framework, the Kullback–Leibler Divergence between the actual joint probability density function of the system dynamics and controller and a predefined ideal joint probability density function is used to characterise the discrepancy between the two distributions and derive the randomised controller. Time delays in the control input and system state are taken into consideration in the optimisation process for the derivation of the optimal randomised controller. Besides, the analytic control solution of the time...
In this paper a new framework has been applied to the design of controllers which encompasses non... more In this paper a new framework has been applied to the design of controllers which encompasses nonlinearity, hysteresis and arbitrary density functions of forward models and inverse controllers. Using mixture density networks, the probabilistic models of both the forward and inverse dynamics are estimated such that they are dependent on the state and the control input. The optimal control strategy is then derived which minimizes uncertainty of the closed loop system. In the absence of reliable plant models, the proposed control algorithm incorporates uncertainties in model parameters, observations, and latent processes. The local stability of the closed loop system has been established. The efficacy of the control algorithm is demonstrated on two nonlinear stochastic control examples with additive and multiplicative noise.
Robust Probabilistic Control for Linear Stochastic Systems with Functional Uncertainty
2019 Tenth International Conference on Intelligent Control and Information Processing (ICICIP), 2019
This paper proposes a cautious randomised controller that is derived such that it minimises the d... more This paper proposes a cautious randomised controller that is derived such that it minimises the discrepancy between the joint distribution of the system dynamics and a predefined ideal joint probability density function (pdf). This distance is known as the Kullback-Leibler divergence. The developed methodology is demonstrated on a class of uncertain stochastic systems that can be characterised by Gaussian density functions. The density function of the dynamics of the system is assumed to be unknown, therefore estimated using the generalised linear neural network models. The analytic solution of the randomised cautious controller is obtained by evaluating the multi-integrals in the Kulback-Leibler divergence cost function. The derived cautious controller minimises to high accuracy the expected value of the Kullback-Leibler divergence taking into consideration the covariance of the dynamics estimated probability density functions.
Control of stochastic systems involving non Gaussian statistics
Control algorithms for stochastic uncertain nonlinear systems have been recently developed. In th... more Control algorithms for stochastic uncertain nonlinear systems have been recently developed. In these methods, functional uncertainty is restricted to follow a Gaussian type density functions. This paper proposes a novel control algorithm for stochastic uncertain nonlinear systems involving non Gaussian statistics. The considered system is subjected to a non Gaussian random input and the purpose of the control input design is to make the mean of the output probability density function of the system, tracks a predefined desired output. Non Gaussian probability density functions in this paper are assumed to be unknown, therefore, estimated using mixture density networks. A simulated example is used to demonstrate the use of the proposed algorithm and encouraging results have been obtained.
IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2021
There is insufficient current understanding of how to apply fully decentralized control to networ... more There is insufficient current understanding of how to apply fully decentralized control to networks of sparsely coupled nonlinear dynamical subsystems subject to noise to track a desired state. As exemplars, this class of problem is motivated by practical requirements of creating decentralized power grids robust to cascade failures, the digital transformation of Industry 4.0 managing IoT connectivity reliably, and controlling transport flow in smart cities by computing at the edge. We demonstrate that an approach utilizing probability theory to characterize and exploit the uncertainty in locally received information, and locally optimized messages passed between neighboring subsystems is sufficient to implicitly infer global knowledge. Thus, control of a global state could be realized through decentralized control signals applied only to local subsystems using only local information without any reference to a global current state. Given a global system that can be decomposed into a ...
IEEE Transactions on Automatic Control, 2021
This paper studies model reference adaptive control (MRAC) for a class of stochastic discrete tim... more This paper studies model reference adaptive control (MRAC) for a class of stochastic discrete time control systems with time delays in the control input. In particular, a unified fully probabilistic control framework is established to develop the solution to the MRAC, where the controller is the minimiser of the Kullback-Leibler Divergence (KLD) between the actual and desired joint probability density functions of the tracking error and the controller. The developed framework is quite general, where all the components within this framework, including the controller and system tracking error, are modelled using probabilistic models. The general solution for arbitrary probabilistic models of the framework components is first obtained and then demonstrated on a class of linear Gaussian systems with time delay in the main control input, thus obtaining the desired results. The contribution of this paper is twofold. First, we develop a fully probabilistic design framework for MRAC, referred to as MRFPD, for stochastic dynamical systems. Second, we establish a systematic pedagogic procedure that is based on deriving explicit forms for the required predictive distributions for obtaining the causal form of the randomised controller when input delays are present.
This paper is concerned with synchronization of complex stochastic dynamical networks in the pres... more This paper is concerned with synchronization of complex stochastic dynamical networks in the presence of noise and functional uncertainty. A probabilistic control method for adaptive synchronization is presented. All required probabilistic models of the network are assumed to be unknown therefore estimated to be dependent on the connectivity strength, the state and control values. Robustness of the probabilistic controller is proved via the Liapunov method. Furthermore, based on the residual error of the network states we introduce the definition of stochastic pinning controllability. A coupled map lattice with spatiotemporal chaos is taken as an example to illustrate all theoretical developments. The theoretical derivation is complemented by its validation on two representative examples.
Uploads
Papers by Randa Herzallah