PID Controller Tuning Optimization with BFO Algorithm in AVR System

This study presents the design and tuning of Proportional Integral Controller (PID) for Automatic Voltage Regulator (AVR) system to improve the dynamic performance and robustness of the system. The PID controller is the very commonly used compensating controller which is used in higher order system. This controller widely used in many different areas like Chemical process control, Aerospace, Automation and Electrical Drives etc. There are various soft computing techniques which are used for tuning of PID controller to control the voltage in AVR system. Tuning of PID parameters is important because, these parameters have a great effect on the stability and performance of the control system. Bacterial Foraging Optimization (BFO) techniques is one of the important techniques to tune the PID parameter in AVR system. Numerical solution based on the proposed PID control of an AVR system for nominal system parameters and step reference voltage input validates the good performance

Evolutionary algorithms based tuning of PID controller for an AVR system

In this paper, an evolutionary algorithm based optimization algorithm is proposed with new objective function to design a PID controller for the automatic voltage regulator (AVR) system. The new objective function is proposed to improve the transient response of the AVR control system and to obtain the optimal values of controller gain. In this paper, particle swarm optimization (PSO) and cuckoo search (CS) algorithms are proposed to tune the parameters of a PID controller for the control of AVR system. Simulation results are capable and illustrate the effectiveness of the proposed method. Numerical and simulation results based on the proposed tuning approach on PID control of an AVR system for servo and regulatory control show the excellent performance of PSO and CS optimization algorithms

Chaotic multi-objective optimization based design of fractional order PI{\lambda}D{\mu} controller in AVR system

In this paper, a fractional order (FO) PI{\lambda}D\mu controller is designed to take care of various contradictory objective functions for an Automatic Voltage Regulator (AVR) system. An improved evolutionary Non-dominated Sorting Genetic Algorithm II (NSGA II), which is augmented with a chaotic map for greater effectiveness, is used for the multi-objective optimization problem. The Pareto fronts showing the trade-off between different design criteria are obtained for the PI{\lambda}D\mu and PID controller. A comparative analysis is done with respect to the standard PID controller to demonstrate the merits and demerits of the fractional order PI{\lambda}D\mu controller.Comment: 30 pages, 14 figure

Optimal PID Controller Design for AVR System

99學年度翁慶昌研究獎補助論文[[abstract]]In this paper, a real-valued genetic algorithm (RGA) and a particle swarm optimization (PSO) algorithm with a new fitness function method are proposed to design a PID controller for the Automatic Voltage Regulator (AVR) system. The proposed fitness function can let the RGA and PSO algorithm search a high-quality solution effectively and improve the transient response of the controlled system. The proposed algorithms are applied in the PID controller design for the AVR system. Some simulation and comparison results are presented.We can see that the proposed RGA and PSO algorithm with this new fitness function can find a PID control parameter set effectively so that the controlled AVR system has a better control performance.[[notice]]補正完畢[[incitationindex]]EI[[booktype]]紙

Robustness Study of Fractional Order PID Controller Optimized by Particle Swarm Optimization in AVR System

In this paper a novel design method for determining fractional order PID (PIλDµ) controller parameters of an AVR system using particle swarm optimization algorithm is presented. This paper presents how to employ the particle swarm optimization to seek efficiently the optimal parameters of PIλDµ controller. The robustness study is made for this controller against parameter variation of AVR system. This work has been simulated in MATLAB environment with FOMCON (Fractional Order Modeling and Control) tool box.The proposed PSOPIλDµ controller has superior performance and robust compared to GA tuned PIλDµ controller. The results are also compared with PSO tuned PID controller

Optimization of PID Controller Based on PSOGSA for an Automatic Voltage Regulator System

AbstractThis paper presents an optimal Proportional Integral Derivate (PID) controller design for an Automatic Voltage Regulator (AVR) system using a new hybrid devised from the Particle Swarm Optimization and the Gravitational Search Algorithm (PSOGSA). The transient response analysis and bode analysis were considered to show the effectiveness of the design technique. Moreover, the comparison of the results between the proposed approach and other techniques such as the Ziegler-Nichols (ZN) tuning method, the Particle Swarm Optimization (PSO) tuning method and the Many Optimizing Liaisons (MOL) tuning method have been given. According to the analysis, the proposed PSOGSA algorithm gives better results than other techniques for the AVR system

Design of Intelligent PID Controller for AVR System Using an Adaptive Neuro Fuzzy Inference System

This paper presents a hybrid approach involving signal to noise ratio (SNR) and particle swarm optimization (PSO) for design the optimal and intelligent proportional-integral-derivative (PID) controller of an automatic voltage regulator (AVR) system with uses an adaptive neuro fuzzy inference system (ANFIS). In this paper determined optimal parameters of PID controller with SNR-PSO approach for some events and use these optimal parameters of PID controller for design the intelligent PID controller for AVR system with ANFIS.  Trial and error method can be used to find a suitable design of anfis based an intelligent controller. However, there are many options including fuzzy rules, Membership Functions (MFs) and scaling factors to achieve a desired performance. An optimization algorithm facilitates this process and finds an optimal design to provide a desired performance. This paper presents a novel application of the SNRPSO approach to design an intelligent controller for AVR. SNR-PSO is a method that combines the features of PSO and SNR in order to improve the optimize operation. In order to emphasize the advantages of the proposed SNR-PSO PID controller, we also compared with the CRPSO PID controller. The proposed method was indeed more efficient and robust in improving the step response of an AVR system and numerical simulations are provided to verify the effectiveness and feasibility of PID controller of AVR based on SNRPSO algorithm.DOI:http://dx.doi.org/10.11591/ijece.v4i5.652

Sugeno fuzzy PID tuning, by genetic-neutral for AVR in electrical power generation

We report a novel design method for determining the optimal proportional-integral-derivative (PID) controller parameters of an automatic voltage regulator (AVR) system, using a combined genetic algorithm (GA), radial basis function neural network (RBF-NN) and Sugeno fuzzy logic approaches. GA and a RBF-NN with a Sugeno fuzzy logic are proposed to design a PID controller for an AVR system (GNFPID). The problem for obtaining the optimal AVR and PID controller parameters is formulated as an optimization problem and RBF-NN tuned by GA is applied to solve the optimization problem. Whereas, optimal PID gains obtained by the proposed RBF tuning by genetic algorithm for various operating conditions are used to develop the rule base of the Sugeno fuzzy system and design fuzzy PID controller of the AVR system to improve the system's response (~0.005 s). The proposed approach has superior features, including easy implementation, stable convergence characteristic, good computational efficiency and this algorithm effectively searches for a high-quality solution and improve the transient response of the AVR system (7E-06). Numerical simulation results demonstrate that this is faster and has much less computational cost as compared with the real-code genetic algorithm (RGA) and Sugeno fuzzy logic. The proposed method is indeed more efficient and robust in improving the step response of an AVR system