Application of Chaos Embedded PSO for PID Parameter Tuning

Abstract

Proportional-Integral-Derivative (PID) control is the most common method applied in the industry due to its simplicity. On the other hand, due to its difficulties, parameter tuning of the PID controllers are usually performed poorly. Generally, the design objectives are obtained by adjusting the controller parameters repetitively until the desired closed-loop system performance is achieved. This allows researchers to use more advanced and even some heuristic methods to achieve the optimal PID parameters. This paper focuses on application of the chaos embedded particle swarm optimization algorithm (CPSO) for PID controller tuning, and demonstrates how to employ the CPSO method to find optimal PID parameters in details. The method is applied to optimal PID parameter tuning for three typical systems with various ordered, and comparisons with the conventional PSO and the Ziegler-Nichols methods are performed. The numerical results from the simulations verify the performance of the proposed scheme.

Snow Leopard Optimization Algorithm: A New Nature-Based Optimization Algorithm for Solving Optimization Problems

“…The main reason for the design of numerous optimization algorithms by researchers is to provide quasi-optimal solutions that are more appropriate and closer to the global optimal solution. In this regard, optimization algorithms have been applied to solve optimization problems in different branches of science such as microwave for design of rectangular microstrip antennas [13], electromagnetic prob-lems [14], Proportional-Integral-Derivative (PID) control [15], Flexible Job Planning (FJSP) problem [16], force analysis and optimization of kinematic parameters [17], simultaneous optimization of distributed generation [18], optimization of complex system reliability [19], design of model-based fuzzy controllers for networked control systems [20], design of the stable robot controller [21], and the Maki-Thompson rumor model [22].…”

Tracking strategy for photovoltaic solar systems in high latitudes

“…The application program of PDPSO is almost identical to the traditional PSO, but it provides faster response time. In 2012, Altinoz et al [19] used chaotic PSO to look for PID parameters. After one PSO search in the logistic chaos map, the positions of particles were put in chaos to look for better positions, so as to prevent PSO from earlier convergence.…”

Design and Study on Sliding Mode Extremum Seeking Control of the Chaos Embedded Particle Swarm Optimization for Maximum Power Point Tracking in Wind Power Systems

“…In terms of the control parameter U 0 and integrated absolute error (IAE) in the SMESC proposed in this paper, the CEPSO [19,20] is used to adjust the system required U 0 and IAE appropriately, to optimize the control parameters, and to validate the reliability and robustness of the control algorithm proposed in this paper. The simulation results are compared by using HCS, extremum seeking control (ESC) [21][22][23] and sliding mode ESC (SMESC) [24,25].…”