Proportional-integral-derivative (PID) control is the most widely used control law in industrial processes. Although various new controllers continue to emerge, PID controllers are still in a dominant position due to their simple structure, easy implementation, and good robustness. In the design and application of PID controllers, one of the core issues is parameter tuning. Accurately and effectively selecting the best tuning parameters of the PID is the key to achieving an effective PID controller. Therefore, this paper proposes a novel modified monkey-multiagent DRL (MM-MADRL) algorithm and uses it to tune PID parameters to improve the stability and performance of automatic parameter optimization. The MM-MADRL algorithm is a new version of the basic monkey group algorithm (MA) and the multiagent reinforcement learning algorithm known as the multiagent deep deterministic policy gradient (MADDPG). This paper selects a typical nonlinear quadcopter system for simulation; its principle and data are given below. MM-MADRL, the genetic algorithm (GA), particle swarm optimization (PSO), the sparse search algorithm (SSA) and differential evolution (DE) are used to adjust the parameters. The simulation results show that the overall performance of the MM-MADRL algorithm is better than that of the other algorithms.

Control systems are widely used in our lives, and good control can be achieved by obtaining the optimal tuning parameters of the control system. The number of parameters that need to be adjusted for different control systems varies. With an increase in tuning parameters, the difficulty of tuning grows. Therefore, this paper proposes an improved monkey multiagent DRL (IMM-MADRL) algorithm and selects 3 test functions to test the setting environment of 2-7 parameters. Thus, these parameters are adjusted. The IMM-MADRL algorithm is based on the modified monkey-multiagent DRL (MM-MADRL) algorithm, and its initialization method, position update method and somersault operation are further improved so that it can perform good parameter tuning for a control system with many parameters. The simulation part of this paper proves the advantage of the IMM-MADRL algorithm in a multiparameter control system.INDEX TERMS Incomplete differential PID controller, modified monkey-multiagent DRL (MM-MADRL) algorithm, Improved modified monkey-multiagent DRL (IMM-MADRL) algorithm, Optimization.

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