Intelligent fuzzy control

In this article, a stochastic optimization technique called fuzzy particle swarm optimization (FPSO) is presented to determine an optimum set of microstrip antenna arrays excitation weights (amplitude and phase), the use of the fuzzy controller allows to dynamically adjust its parameters such as, the inertia weight and acceleration coefficients in order to produce an optimal pattern of the antenna array able to approach a desired pattern. Simulation results are proposed to compare with published results to verify the effectiveness of the suggest method for both linear and planar array.

This paper presents the optimal fuzzy controller design for load frequency control (LFC) of two area power system using Adaptive Particle Swarm Optimization (APSO). Firstly, the controller is designed according to Fuzzy Logic rules such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC) used earlier was optimized with PSO so as to obtain optimal adjustment of the membership functions only. Finally, the FLC is completely optimized by Swarm Intelligence Algorithms. Digital simulation results demonstrate that in comparison with the FLC, the designed FLC-APSO speed controller gives better dynamic performance of the LFC, as well as without any overshoot. The proposed controller is compared against other controllers. The results show that the proposed controller outperforms other schemes.

Human-Computer Interaction with the traditional User Interface is done using a specified in advance script dialog "menu", mainly based on human intellect and unproductive use of navigation. This approach doesn't lead to making qualitative decision in control systems, where the situations and processes cannot be structured in advance. Any dynamic changes in the controlled business process (as example, in organizational unit of the information fuzzy control system) make it necessary to modify the script dialogue in User Interface. This circumstance leads to a redesign of the components of the User Interface and of the entire control system. In the Intelligent User Interface, where the dialog situations are unknown in advance, fuzzy structured and artificial intelligence is crucial, the redesign described above is impossible. To solve this and other problems, we propose the data, information and knowledge based technology of Smart/ Intelligent User Interface (IUI) design, which interacts with users and systems in natural and other languages, utilizing the principles of Situational Control and Fuzzy Logic theories, Artificial Intelligence, Linguistics, Knowledge Base technologies and others. The proposed technology of IUI design is defined by multi-agents of a) Situational Control and of data, information and knowledge, b) modelling of Fuzzy Logic Inference, c) Generalization, Representation and Explanation of knowledge, c) Planning and Decisionmaking, d) Dialog Control, e) Reasoning and Systems Thinking, f) Fuzzy Control of organizational unit in real-time, fuzzy conditions, heterogeneous domains, and g) multilingual communication under uncertainty and in Fuzzy Environment.

The traditional control systems are a set of hardware and software infrastructure domain and qualified personnel to facilitate the functions of analysis, planning, decision-making, management and coordination of business processes. Human interaction with the components of these systems is done using a specified in advance script dialogue "menu", mainly based on human intellect and unproductive use of navigation. This approach doesn't lead to making qualitative decision and effective control, where the situations and processes cannot be structured in advance. Any dynamic changes in the controlled business process make it necessary to modify the script dialogue. This circumstance leads to a redesign of the components of the entire control system. In the autonomous Fuzzy Control System, where the situations are unknown in advance, fuzzy structured and artificial intelligence is crucial, the redesign described above is impossible. To solve this problem, we propose the data, information and knowledge based technology of creation Situational, Intelligent Multi-agent Control System, which interacts with users and/ or agent systems in natural and other languages, utilizing the principles of Situational Control and Fuzzy Logic theories, Artificial Intelligence, Linguistics, Knowledge Base technologies and others. The proposed technology is defined by a) methods of situational fuzzy control of data, information and knowledge, b) modelling of fuzzy logic inference, c) generalization and explanation of knowledge, d) fuzzy dialogue control, e) machine translation, f) fuzzy decision-making, g) planning and h) fuzzy control of organizational unit in real-time under uncertainty, fuzzy conditions, heterogeneous domains, multilingual communication in Fuzzy Environment.

This paper presents the optimal fuzzy controller design for load frequency control (LFC) of two area power system using Adaptive Particle Swarm Optimization (APSO). Firstly, the controller is designed according to Fuzzy Logic rules such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC) used earlier was optimized with PSO so as to obtain optimal adjustment of the membership functions only. Finally, the FLC is completely optimized by Swarm Intelligence Algorithms. Digital simulation results demonstrate that in comparison with the FLC, the designed FLC-APSO speed controller gives better dynamic performance of the LFC, as well as without any overshoot. The proposed controller is compared against other controllers. The results show that the proposed controller outperforms other schemes.

A simultaneous study of load frequency control and automatic voltage regulation is considered. The singlearea and two-area power systems have been considered with a combined model of AGC and AVR for analysis of frequency and voltage deviation. The primary aspect of the design is to keep real power control as to preserve frequency of the system in a prescribed limit. Reactive power control is used to keep the voltage magnitude of synchronous generator at a tolerable limit. The transients produced in dynamic responses are controlled using intelligent and soft-computing technique. The fuzzy logic controller and particle swarm optimization based PID controllers are considered. The steady state error and settling time of the dynamic responses are observed and found to be reduced very effectively. The frequency and terminal voltage results of a single-area power system with proposed PSO-PID controller is compared to the responses of system with controllers presented in literature and found to be more effective. The comparison of fuzzy logic controller with PSO-PID is done by observing the nature of two-area responses. The result obtained by soft computing algorithm and intelligent controller is analyzed on the basis of settling-time, undershoot and ISE Error.

Nowadays, unmanned underwater vehicle (UUV) is created to reduce human intervention in deep-water application. UUV can help human to make an underwater application that commonly used in deep water industries. During operation, the UUV undergoes a complex multi-axis motion trajectories that are highly nonlinear because the subsystems in the UUV are ill-defined and strongly coupled to each other. The conventional controller such as Proportional, Integral and Derivative (PID) and Proportional and Derivative (PD) have a problem to control nonlinear operation. The conventional controller hardly to achieve zero overshoot. Implementation of the controller on the UUV using Fuzzy Logic Controller (FLC) itself poses its own level of complexity. Consequently, implementation of FLC also requires fast and high-performance processors. The objectives of this paper are to study the effect of the tuning membership function to improved performances of the FLC for depth control using actual underwater...

In this paper, an improved particle swarm optimization (IPSO) based load frequency control (LFC) of a single area power system is presented. Although the Particle Swarm Optimization approaches have several advantages, they can still have drawbacks like local optimal trapping due to premature convergence(i.e. exploration problem). This paper proposes an improved PSO framework adopting a crossover operation scheme to increase exploitation capability of PSO.The study has been realized for control of a single area interconnected power system with IPSO optimized self-tuning PID controller. The comparison between a conventional Proportional-Integral (PI) controller and the proposed PSO based controller showed that the proposed controller can generate a better transient response for a step load change. For this application, MATLAB-Simulink software is used.

A simultaneous study of load frequency control and automatic voltage regulation is considered. The singlearea and two-area power systems have been considered with a combined model of AGC and AVR for analysis of frequency and voltage deviation. The primary aspect of the design is to keep real power control as to preserve frequency of the system in a prescribed limit. Reactive power control is used to keep the voltage magnitude of synchronous generator at a tolerable limit. The transients produced in dynamic responses are controlled using intelligent and soft-computing technique. The fuzzy logic controller and particle swarm optimization based PID controllers are considered. The steady state error and settling time of the dynamic responses are observed and found to be reduced very effectively. The frequency and terminal voltage results of a single-area power system with proposed PSO-PID controller is compared to the responses of system with controllers presented in literature and found to be more effective. The comparison of fuzzy logic controller with PSO-PID is done by observing the nature of two-area responses. The result obtained by soft computing algorithm and intelligent controller is analyzed on the basis of settling-time, undershoot and ISE Error.

In this paper, an improved particle swarm optimization (IPSO) based load frequency control (LFC) of a single area power system is presented. Although the Particle Swarm Optimization approaches have several advantages, they can still have drawbacks like local optimal trapping due to premature convergence(i.e. exploration problem). This paper proposes an improved PSO framework adopting a crossover operation scheme to increase exploitation capability of PSO.The study has been realized for control of a single area interconnected power system with IPSO optimized self-tuning PID controller. The comparison between a conventional Proportional-Integral (PI) controller and the proposed PSO based controller showed that the proposed controller can generate a better transient response for a step load change. For this application, MATLAB-Simulink software is used.

This paper presents the optimal fuzzy controller design for load frequency control (LFC) of two area power system using Adaptive Particle Swarm Optimization (APSO). Firstly, the controller is designed according to Fuzzy Logic rules such that the systems are fundamentally robust. Secondly, the Fuzzy Logic controller (FLC) used earlier was optimized with PSO so as to obtain optimal adjustment of the membership functions only. Finally, the FLC is completely optimized by Swarm Intelligence Algorithms. Digital simulation results demonstrate that in comparison with the FLC, the designed FLC-APSO speed controller gives better dynamic performance of the LFC, as well as without any overshoot. The proposed controller is compared against other controllers. The results show that the proposed controller outperforms other schemes.

The traditional control systems are a set of hardware and software infrastructure domain and qualified personnel to facilitate the functions of analysis, planning, decision-making, management and coordination of business processes. Human interaction with the components of these systems is done using a specified in advance script dialogue "menu", mainly based on human intellect and unproductive use of navigation. This approach doesn't lead to making qualitative decision and effective control, where the situations and processes cannot be structured in advance. Any dynamic changes in the controlled business process make it necessary to modify the script dialogue. This circumstance leads to a redesign of the components of the entire control system. In the autonomous Fuzzy Control System, where the situations are unknown in advance, fuzzy structured and artificial intelligence is crucial, the redesign described above is impossible. To solve this problem, we propose the dat...

Human-Computer Interaction with the traditional User Interface is done using a specified in advance script dialog "menu", mainly based on human intellect and unproductive use of navigation. This approach doesn't lead to making qualitative decision in control systems, where the situations and processes cannot be structured in advance. Any dynamic changes in the controlled business process (as example, in organizational unit of the information fuzzy control system) make it necessary to modify the script dialogue in User Interface. This circumstance leads to a redesign of the components of the User Interface and of the entire control system. In the Intelligent User Interface, where the dialog situations are unknown in advance, fuzzy structured and artificial intelligence is crucial, the redesign described above is impossible. To solve this and other problems, we propose the data, information and knowledge based technology of Smart/ Intelligent User Interface (IUI) design, which interacts with users and systems in natural and other languages, utilizing the principles of Situational Control and Fuzzy Logic theories, Artificial Intelligence, Linguistics, Knowledge Base technologies and others. The proposed technology of IUI design is defined by multi-agents of a) Situational Control and of data, information and knowledge, b) modelling of Fuzzy Logic Inference, c) Generalization, Representation and Explanation of knowledge, c) Planning and Decision-making, d) Dialog Control, e) Reasoning and Systems Thinking, f) Fuzzy Control of organizational unit in real-time, fuzzy conditions, heterogeneous domains, and g) multilingual communication under uncertainty and in Fuzzy Environment.

The traditional control systems are a set of hardware and software infrastructure domain and qualified personnel to facilitate the functions of analysis, planning, decision-making, management and coordination of business processes. Human interaction with the components of these systems is done using a specified in advance script dialogue "menu", mainly based on human intellect and unproductive use of navigation. This approach doesn't lead to making qualitative decision and effective control, where the situations and processes cannot be structured in advance. Any dynamic changes in the controlled business process make it necessary to modify the script dialogue. This circumstance leads to a redesign of the components of the entire control system. In the autonomous Fuzzy Control System, where the situations are unknown in advance, fuzzy structured and artificial intelligence is crucial, the redesign described above is impossible. To solve this problem, we propose the data, information and knowledge based technology of creation Situational, Intelligent Multi-agent Control System, which interacts with users and/ or agent systems in natural and other languages, utilizing the principles of Situational Control and Fuzzy Logic theories, Artificial Intelligence, Linguistics, Knowledge Base technologies and others. The proposed technology is defined by a) methods of situational fuzzy control of data, information and knowledge, b) modelling of fuzzy logic inference, c) generalization and explanation of knowledge, d) fuzzy dialogue control, e) machine translation, f) fuzzy decision-making, g) planning and h) fuzzy control of organizational unit in real-time under uncertainty, fuzzy conditions, heterogeneous domains, multi-lingual communication in Fuzzy Environment.

This research paper presents decentralized control scheme for Load Frequency Control in a multi-area Power System by appreciating the performance of the methods in a single area power system. A number of modern control techniques as well as the classical methods and optimization algorithm are adopted to implement a reliable stabilizing controller. A serious attempt has been undertaken aiming at investigating the load frequency control problem in a power system consisting of two power generation unit and multiple variable load units. The robustness and reliability of the various control schemes is examined through simulations and Mat-Lab programming. The design is determined an optimization problem and a novel cost function is derived for increasing the performance of convergence to the solution. To optimize the parameters of the cost functions and the PID-controller, the Ziegler-Nichols method of PID tuning also used here. The results show that the proposed control method is provided better performance for dynamic responses of the power system.