Predator and Prey Modified Biogeography Based Optimization Approach (PMBBO) in Tuning a PID Controller for Nonlinear Systems (original) (raw)

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.

Novel Technique for PID Tuning by Linearized Biogeography-Based Optimization

Proportional Integral Derivative (PID) controller is commonly used in process control systems. Tuning PID controller parameters are a very challenging problem to improve performance and stability of a process. This paper presents a novel method for PID controller tuning problem using Linearized Biogeography-Based Optimization (LBBO) algorithm. Biogeography-Based Optimization (BBO) is an evolutionary optimization algorithm based on mathematical model of Biogeography; it permits of sharing the features among candidate solutions (habitats) represented by emigration and immigration. By using Matlab/Simulink and the squared error integral criterion as objective function. The algorithm is applied to benchmarks functions optimization design, and is then compared with Particle Swarm Optimization (PSO), BBO, and Modified Biogeography-Based Optimization (MBBO). Simulation results shown that the LBBO is an effective tuning method and has better performance compared with PSO, BBO, and MBBO.

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.

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.

Control of nonlinear systems using a hybrid APSO-BFO algorithm: An optimum design of PID controller

2011

This paper proposes a novel hybrid algorithm namely APSO-BFO which combines merits of Bacterial Foraging Optimization (BFO) algorithm and Adaptive Particle Swarm Optimization (APSO) algorithm to determine the optimal PID parameters for control of nonlinear systems. To balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search space through the APSO algorithm whereas the local search is performed by BFO algorithm. The proposed algorithm starts with APSO algorithm. In the proposed APSO, every particle dynamically adjusts inertia weight according to feedback taken from particles best memories. In this case, APSO algorithm is used to enhance global search ability and to increase convergence speed. When the change in fitness value is smaller than a predefined value, the searching process is switched to BFO to accelerate the search process and find an accurate solution. In this way, this hybrid algorithm may find an optimum solution more accurately. To demonstrate the effectiveness of the proposed algorithm, its results are compared with those obtained by Basic PSO (BPSO), Standard BFO (SBFO), BFO with PSO (PSO-BFO), BFO with GA (GA-BFO) and Differential Evolution with BFO (DE-BFO). The numerical simulations are shown the potential of proposed algorithm.

Optimization of PD-PI Controller Using Swarm Intelligence

2008

Sensitivity and Robustness is the primary issue while designing the controller for non-linear systems. One of the performance objectives for controller design is to keep the error between the controlled output and the set-point as small as possible. A comparison between Evolutionary Algorithms namely GAs (Genetic Algorithms), and Swarm Intelligence, i.e., PSO (Particle Swarm Optimization) and BG (Bacterial Foraging) has been carried out on the basis of performance indices: ITAE (Integral Time Absolute Error), ISE (Integral Square Error), IAE (Integral Absolute Error) and MSE (Mean Square Error) and settling time. In this paper, the idea of model generation and optimization is explored for PD-PI controller. Most commonly known, the highly nonlinear Inverted Pendulum system is used as a test system for this approach. The simulations are tabulated in section IV to analyze which technique gives promising results for the system.

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.

Tuning of PID controller using Fuzzy Logic controller & Bacterial Foraging Optimization Algorithm

Proportional integral derivative controller tuning is an area of interest for researchers in many disciplines of science and engineering. Work presented in this based on Fuzzy Logic Controller and Bacterial Foraging Optimization Techniques. The proposed techniques are applied to the problem of PID controller tuning and is compared. It is observed that Bacterial Foraging Optimization technique gives a better result.

PID Controller Tuning Using Hybrid Optimization Based On Bacterial Foraging and Clonal Selection

2006

This paper suggests novel hybrid optimization system (BF-CL) based on the bacterial foraging and clonal selection of immune system. A foraging strategy involves finding a patch of optimal condition (e.g., group of objective with conditions), deciding whether to enter it and search for optimal conditions, and when to leave the patch. There are predators and risks, energy required for optimization, and physiological constraints (sensing, memory, cognitive capabilities). Foraging scenarios can be modeled and optimal policies can be found using, for instance, dynamic programming. This approach provides us with novel hybrid model based on foraging behavior and clonal selection for a higher running time and with also a possible new connection between evolutionary forces in social foraging and distributed nongradient optimization algorithm design for global optimization over noisy surfaces for control system.

Swarm-Inspired Algorithms to Optimize a Nonlinear Gaussian Adaptive PID Controller

Energies, 2021

This work deals with metaheuristic optimization algorithms to derive the best parameters for the Gaussian Adaptive PID controller. This controller represents a multimodal problem, where several distinct solutions can achieve similar best performances, and metaheuristics optimization algorithms can behave differently during the optimization process. Finding the correct proportionality between the parameters is an arduous task that often does not have an algebraic solution. The Gaussian functions of each control action have three parameters, resulting in a total of nine parameters to be defined. In this work, we investigate three bio-inspired optimization methods dealing with this problem: Particle Swarm Optimization (PSO), the Artificial Bee Colony (ABC) algorithm, and the Whale Optimization Algorithm (WOA). The computational results considering the Buck converter with a resistive and a nonlinear load as a case study demonstrated that the methods were capable of solving the task. The...