Modified ACS algorithm-based nonlinear PID controller and its application to CIP-I intelligent leg

Authors:
Guanzheng Tan;Guanghui He;I. Dioubate Mamady
Affiliations:
School of Information Science and Engineering, Central South University, Hunan Province, China;School of Information Science and Engineering, Central South University, Hunan Province, China;School of Information Science and Engineering, Central South University, Hunan Province, China
Venue:
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
Year:
2007

Citing 2
Cited 1

Ant colony optimization and stochastic gradient descent

Artificial Life
Ant colony system: a cooperative learning approach to the traveling salesman problem

IEEE Transactions on Evolutionary Computation

Application of Ant Colony System to an Experimental Propeller Setup

ICIC '08 Proceedings of the 4th international conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications - with Aspects of Theoretical and Methodological Issues

Quantified Score

Hi-index	0.00

Visualization

Abstract

Based on the modified ant colony system (ACS) algorithm, a novel design method for nonlinear PID controller with on-line optimal self-tuning gains is proposed. In this method, first the regulative laws of the three PID controller gains are designed respectively, which are three nonlinear functions of system error and its variation rate, and then the proposed modified ACS algorithm is used to optimize the nine parameters in the three nonlinear functions. The designed controller is called the ACS-NPID controller and was used to control the CIP-I intelligent leg prosthesis. The simulation experiments demonstrated that the ACS-NPID controller has better control performance compared with other three linear PID controllers designed respectively by the differential evolution algorithm, the real-coded genetic algorithm, and the simulated annealing. The simulation results also verified that the modified ACS algorithm has good performance in convergence speed and solution variation characteristic.