Self-tuning of Fuzzy Neural PID Parameter Based on Chaotic Ant Colony Optimization

Considering the linear limitation of the traditional PID controller, a scheme combining the fuzzy system with BP neural network was proposed, that was, and the fuzzy neural PID controller. At the same time, in order to optimize the accuracy of parameter, this paper combined the chaos optimization with ant colony algorithm, namely, chaotic ant swarm (CAS). It, which took the error integral performance index as the objective function and the value range of design parameters and the minimum gain and phase margin as the constraint condition, established the optimization mathematical model, further optimized and trained the weight and bias error of the fuzzy BP neural network, and reduced the error caused by the fuzzy neural network used for function approximation. Comparing the traditional fuzzy PID controller and the PID controller based on chaos genetic algorithm (GA) with the PID controller designed in this paper, the simulation results show that the dynamic performance and steady-state accuracy of the system are improved well.