A Novel Immune Genetic Algorithm-Based PID Controller Design and Its Application to CIP-I Intelligent Leg

A novel immune genetic algorithm with elitism (IGAE) is presented. The IGAE has two features. The first is that the similarity and expected reproduction probability of antibody can be adjusted dynamically in the process of population evolution to balance the population diversity and the convergence speed of the algorithm. The second is that with the elitism strategy this algorithm is able to find the globally optimal solution. Based on the IGAE, an optimal design method of PID controller is proposed. The PID controller designed by the IGAE, called the IGAE- PID controller, was used to control the motion of the CIP-I leg, an intelligent leg prosthesis. The simulation experiments demonstrated that the controller has good control performance. Compared with the other three PID controllers designed respectively by the immune clonal selection algorithm, the canonical genetic algorithm with the elitism strategy, and the standard simulated annealing algorithm, the IGAE-PID controller exhibited better or equivalent control performance. Moreover, the simulation results also verified that the IGAE has better performance in convergence speed and computation efficiency.

[1]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[2]  Antonio Visioli,et al.  Fuzzy logic based set-point weight tuning of PID controllers , 1999, IEEE Trans. Syst. Man Cybern. Part A.

[3]  C. A. Murthy,et al.  Genetic Algorithm with Elitist Model and Its Convergence , 1996, Int. J. Pattern Recognit. Artif. Intell..

[4]  Dipankar Dasgupta,et al.  Artificial Immune Systems in Intrusion Detection , 2005 .

[5]  Junghui Chen,et al.  Applying neural networks to on-line updated PID controllers for nonlinear process control , 2004 .

[6]  Fernando José Von Zuben,et al.  Learning and optimization using the clonal selection principle , 2002, IEEE Trans. Evol. Comput..

[7]  Günter Rudolph,et al.  Convergence analysis of canonical genetic algorithms , 1994, IEEE Trans. Neural Networks.

[8]  Alex Alves Freitas,et al.  AISEC: an artificial immune system for e-mail classification , 2003, IEEE Congress on Evolutionary Computation.

[9]  Guanzheng Tan,et al.  Design of PID controller with incomplete derivation based on ant system algorithm , 2004 .

[10]  Fang Sheng,et al.  Genetic algorithm and simulated annealing for optimal robot arm PID control , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[11]  Jason Brownlee,et al.  IIDLE: An Immunological Inspired Distributed Learning Environment for Multiple Objective and Hybrid Optimisation , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[12]  Dipankar Dasgupta,et al.  Artificial Immune Systems in Intrusion Detection , 2005 .