Iterative Feedback Tuning Control and Optimization of Pneumatic Muscle Actuators

Pneumatic muscle actuators (PMA)have advantages such as low costs, flexibility, and mechanical properties similar to those of biological muscles, and these advantages make them widely used as equipment in many fields such as medicine and bionic robotics. Considering the problem of highly nonlinear and time-varying features and the difficulty in determining the control parameters, this paper presents a data-driven optimal control method based on iterative feedback tuning (IFT). Based on the input and output data, the method applies the Gauss-Newton approximation algorithm to iteratively tune PID parameters by defining the tracking performance criterion function, and the optimal value of the weighting factor is obtained by introducing an auxiliary factor to further speed up the convergence of the IFT algorithm. The simulation results show that this method can effectively improve the tracking performance and system robustness, compared with the traditional PID parameter tuning method such as Ziegler-Nichols tuning method.