Novel Technique for PID Tuning by Linearized Biogeography-Based Optimization (original) (raw)
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Application of Biogeography based optimization in tuning a PID controller for nonlinear systems
2012 IEEE International Conference on Complex Systems (ICCS), 2012
This paper is dedicated to present the newly developed evolutionary algorithm: Biogeography based optimization (BBO). It is based on the migration of information between habitats like in Biogeography. The BBO is then used to tune a PID controller of nonlinear systems where the parameters are optimized. Simulations of the proposed algorithm are carried out over an inverted pendulum and second on mass-spring damper system. Performances of the BBO are compared to those of genetic algorithm in PID tuning problem and the BBO gives acceptable results even best then GA.
International Journal of Intelligent Systems and Applications, 2014
In this paper an enhanced approach based on a modified biogeography optimization with predator and prey behavior (PMBBO) is presented. The approach uses several predators with new proposed prey's movement formula. The potential of using a modified predator and prey model is to increase the diversification along the optimization process so to avoid local optima and reach the optimal solution quickly. The proposed approach is used in tuning the gains of PID controller for nonlinear systems (Mass spring damper and an inverted pendulum) and has given remarkable results when compared to genetic algorithm and classical BBO.
The proportional-integral-derivative (PID) controllers were the most popular controllers of this century because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, PID controllers are poorly tuned in practice with most of the tuning done manually which is difficult and time consuming. The computational intelligence has purposed genetic algorithms (GA) and particle swarm optimization (PSO) as opened paths to a new generation of advanced process control. These advanced techniques to design industrial control systems are, in general, dependent on achieving optimum performance with the controller when facing with various types of disturbance that are unknown in most practical applications. This paper presents a comparison study of using two algorithms for the tuning of PID-controllers for processes which represents a subsystem of complex industrial processes, known to be non-linear and time variant. Simulation results showed that the PID control tuned by PSO provides an adequate closed loop dynamic for the Ball and Hoop system experiment in wide range operations.
PROPORTIONAL–INTEGRAL–DERIVATIVE (PID) CONTROLLER TUNING USING PARTICLE SWARM OPTIMIZATION ALGORITHM
The proportional-integral-derivative (PID) controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, manual tuning of these controllers is time consuming, tedious and generally lead to poor performance. This tuning which is application specific also deteriorates with time as a result of plant parameter changes. This paper presents an artificial intelligence (AI) method of particle swarm optimization (PSO) algorithm for tuning the optimal proportional-integral derivative (PID) controller parameters for industrial processes. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. Ziegler-Nichols, tuning method was applied in the PID tuning and results were compared with the PSO-Based PID for optimum control. Simulation results are presented to show that the PSO-Based optimized PID controller is capable of providing an improved closed-loop performance over the Ziegler-Nichols tuned PID controller Parameters. Compared to the heuristic PID tuning method of Ziegler-Nichols, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of DC motor.
The proportional-integral-derivative (PID) controllers are the most popular controllers used in industry because of their remarkable effectiveness, simplicity of implementation and broad applicability. However, manual tuning of these controllers is time consuming, tedious and generally lead to poor performance. This tuning which is application specific also deteriorates with time as a result of plant parameter changes. This paper presents an artificial intelligence (AI) method of particle swarm optimization (PSO) algorithm for tuning the optimal proportional-integral derivative (PID) controller parameters for industrial processes. This approach has superior features, including easy implementation, stable convergence characteristic and good computational efficiency over the conventional methods. Ziegler-Nichols, tuning method was applied in the PID tuning and results were compared with the PSO-Based PID for optimum control. Simulation results are presented to show that the PSO-Based optimized PID controller is capable of providing an improved closed-loop performance over the Ziegler-Nichols tuned PID controller Parameters. Compared to the heuristic PID tuning method of Ziegler-Nichols, the proposed method was more efficient in improving the step response characteristics such as, reducing the steady-states error; rise time, settling time and maximum overshoot in speed control of DC motor.
Optimal Tuning of PID Controller Using Genetic Algorithm and Swarm Techniques
In order to control the systems, a few control strategies must deal with the effects of non-linearties or uncertainties. As earlier utilized control techniques based on mathematical models have been primarily concentrated on stability robustness against the ill-effects of control mechanism, they are limited in their ability to amend the transient responses. These conventional techniques failed to tune the non-linear and non-minimum phase systems. Therefore, a few modern algorithms have been introduced here such as; Bacteria Foraging Optimization, Particle Swarm Optimization and Genetic Algorithm which have been proved an appropriate aid to improve the transient responses of systems perturbed by non-linearties or unknown mathematical characteristics. This Paper presents designing a PID controller by selection of PID parameters using Bacterial Foraging Optimization, Particle Swarm Optimization (PSO) and Genetic Algorithm. Here, the closed loop step response of the PID controller has been compared for the above mentioned three optimization algorithms.
Evaluations of Optimum Value of PID Controller Gains Using Hybrid Bacterial Swarm Optimization
In the control system problems it is challenging to find the PID parameters in the initial stage, and there fine tuning during the system run condition. In this paper we have test the application of hybrid bacterial foraging and particle swarm optimization algorithm named as bacterial swarm optimization BSO) based PID parameter tuning of close loop controller. The objective is dependent on globally minimal error squared error integral criteria of the step response of second order and higher order plants cascaded with PID controller by our proposed method. In this algorithm parameters are found by evolutionary methods with consideration of the globally optimal solution for control applications. The Kp, Ki and Kd gains are calculated by the PSO and (BSO) methods for plants with all the poles of the transfer function located in the left half of the s-plane. The performance of both algorithms is analyzed on transfer functions of a second order and higher order plants.
Design of Optimum Pid Controller for Nonlinear Process Using Evolutionary Algorithms
2014
In past decades, PID controller was mostly preferred because of its robust behavior in a spread over range of operating conditions. However the conventional PID tuning procedure is not suitable for getting the optimized controller parameters under the identified operating regions. Therefore, an optimum Particle Swarm Optimization (PSO) based Proportional-Integral-Derivative (PID) controller is proposed for controlling the nonlinear process with optimized PID parameters settings. The PSO computation technique has many advantages like high quality solution, less computation time and good convergence characteristics. The tuning of PID parameters through optimization algorithms provides high quality solution for nonlinear processes. The performance indices such as ISE, IAE and ITSE were studied to evaluate the proposed controller performances. The proposed PID controller is presented to a nonlinear Continuous Stirrer Tank Reactor (CSTR) process for controlling the concentration by manip...
Optimal Pid Tuning by Using Bacteria Foraging Optimization Algorithm
2012
Recently, many algorithms used to make tuning for PID controller parameters for different applications. PID Controller gives us control of both transient and steady state response, and decreases the characteristics elements such as overshoot, steady state error, settling time, and rising time, and makes the system stable and robust. But the problem is how to choose the PID parameters to achieve a desired response. In this work we will present the Bacteria Foraging Optimization Algorithm (BFOA) as a novel algorithm using the social foraging behaviour for Escherichia coli bacteria to make tuning for PID parameters for an experimental plant system. Comparing with Practical Swarm Optimization (PSO), this proposed algorithm was more efficient in improving the step response characteristic for this system.
IJERT-Evaluations of Optimum Value of PID Controller Gains Using Hybrid Bacterial Swarm Optimization
International Journal of Engineering Research and Technology (IJERT), 2013
https://www.ijert.org/evaluations-of-optimum-value-of-pid-controller-gains-using-hybrid-bacterial-swarm-optimization https://www.ijert.org/research/evaluations-of-optimum-value-of-pid-controller-gains-using-hybrid-bacterial-swarm-optimization-IJERTV2IS110994.pdf In the control system problems it is challenging to find the PID parameters in the initial stage, and there fine tuning during the system run condition. In this paper we have test the application of hybrid bacterial foraging and particle swarm optimization algorithm named as bacterial swarm optimization (BSO) based PID parameter tuning of close loop controller. The objective is dependent on globally minimal error squared error integral criteria of the step response of second order and higher order plants cascaded with PID controller by our proposed method. In this algorithm parameters are found by evolutionary methods with consideration of the globally optimal solution for control applications. The Kp, Ki and Kd gains are calculated by the PSO and (BSO) methods for plants with all the poles of the transfer function located in the left half of the s-plane. The performance of both algorithms is analyzed on transfer functions of a second order and higher order plants.