Optimization of RTD-A Controller Parameters Based on Empirical Bat Algorithm

During recent twenty years, the research of predictive control has become mature and has strong application value. It was over this period that the RTD-A controller was proposed as a new predictive controller with a simple structure and easy implementation. The four parameters of the controller directly affect the robustness of the system, the traceability of the set points, the suppression of interference and the global convergence, making the control effect of RTD-A better than that of the classic controller. However, it is found in the controller parameter modulation of the industrial field that although the four parameters are clear, the coupling phenomenon between the parameters is reflected in the system of set point tracking and interference suppression, which increases the difficulty of parameter modulation. Based on this, this paper proposes offline intelligent parameter optimization for the process of setpoint tracking and interference suppression in a finite step range. Finally, it is proved by simulation experiments that the RTD-A controller using the intelligent optimization algorithm has better setpoint tracking performance, stronger anti-interference ability and better robustness than the RTD-A controller without the optimization algorithm.