A novel optimal PID controller autotuning design based on the SLP algorithm

Abstract

A novel optimal proportional integral derivative (PID) autotuning controller design based on a new algorithm approach, the “swarm learning process” (SLP) algorithm, is proposed. It improves the convergence and performance of the autotuning PID parameter by applying the swarm and learning algorithm concepts. Its convergence is verified by two methods, global convergence and characteristic convergence. In the case of global convergence, the convergence rule of a random search algorithm is employed to judge, and Markov chain modelling is used to analyse. The superiority of the proposed method, in terms of characteristic convergence and performance, is verified through the simulation based on the automatic voltage regulator and direct current motor control system. Verification is performed by comparing the results of the proposed model with those of other algorithms, that is, the ant colony optimization with a new constrained Nelder–Mead algorithm, the genetic algorithm (GA), the particle swarm optimization (PSO) algorithm, and a neural network (NN). According to the global convergence analysis, the proposed method satisfies the convergence rule of the random search algorithm. With respect to the characteristic convergence and performance, the proposed method provides a better response than the GA, the PSO, and the NN for both control systems.

PID Controller Autotuning Design by a Deterministic Q-SLP Algorithm

“…5. The superiority of the performance and convergence time are verified by comparison with the simulation results of the traditional SLP algorithm [14], the WOA [27] and IPSO [47] based on the CPC system. This paper is organized into 6 sections: Section 2 presents the PID controller and objective design.…”

“…The SLP algorithm was proposed by [14]. It applies the concepts of the swarm algorithm and learning algorithm.…”

“…The associate editor coordinating the review of this manuscript and approving it for publication was Huiping Li. parameters, the proportional gain (K P ), integral gain (K I ) and derivative gain (K D ), is integral to system control [4], [10]. In 1942, a method of tuning the PID parameter was proposed by Ziglor-Nichlor [14]. After that, many methods were proposed, for example, the Cohen-Coon, phase and gain margin methods.…”

“…Although many AI methods have been proposed to autotune the PID parameter, the challenges of long execution time and convergence persist. Therefore, [14] proposed the novel algorithm called the swarm learning process algorithm to improve the convergence and performance in autotuning the PID parameter. This algorithm is motivated by student learning in the classroom and applies the concepts of the swarm algorithm and learning algorithm.…”

“…Furthermore, the optimization of the proposed method is proven based on the Bellman equation. Finally, to show the superiority of the proposed method in terms of convergence and performance, a comparison with the simulation results of the traditional SLP algorithm [14], the WOA [25] and IPSO [45] based on the central position control (CPC) system is provided. Therefore, the contributions of this paper can be summarized as follows: 1.…”