Novel D-SLP Controller Design for Nonlinear Feedback Control

• Main Authors: Jirapun Pongfai, Wudhichai Assawinchaichote, Peng Shi, Xiaojie Su
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Dragonfly algorithm (DA); gradient descent method; Markov chain modeling; nonlinear control; nonlinear estimation; permanent magnet synchronous motor (PMSM)
• Online Access: https://ieeexplore.ieee.org/document/9139927/

Novel nonlinear feedback control based on the dragonfly swarm learning process (D-SLP) algorithm is proposed in this paper. This approach improves the performance, stability and robustness of designing the nonlinear system controller. The D-SLP algorithm is the combination of the dragonfly algorithm (DA) and swarm learning process (SLP) algorithm by applying the DA to the learning process of the SLP algorithm. Furthermore, the estimation of the nonlinear term by using gradient descent is proposed in the process of the D-SLP algorithm. The learning rate is adjusted according to the stable learning rate, which is derived according to the Lyapunov stability theorem. To show the superior performance and robustness of the proposed control method, it is compared with the simulation of designing the controller based on a permanent magnet synchronous motor (PMSM) control system with the online autotuning parameter of a PID controller and LQR controller with two case studies. The conventional SLP algorithm and DA are used to autotune the PID controller, while an artificial bee colony algorithm and a flower pollination algorithm (ABC-FPA) autotune the LQR controller. From the simulation results, the proposed control method can provide a better response than the other control method. Additionally, the global convergence of the D-SLP algorithm is analyzed according to Markov chain modeling and proved to correspond with the policy of global convergence for stochastic search algorithms.