Abstract
Clamping force control system is essential for clamping tasks that require high precision. In this paper, Active Disturbance Rejection Controller (ADRC) is applied for clamping force control system, aiming to achieve higher control precision. Furthermore, the CPSO-ADRC system is proposed and implemented by optimizing the critical parameters of ordinary ADRC using chaos particle swarm optimization (CPSO) algorithm. To verify the effectiveness of CPSO-ADRC, Particle Swarm Optimization- (PSO-) ADRC is introduced as a comparison. The simulation results show that the CPSO-ADRC can effectively improve the control quality with faster dynamic response and better command tracking performance compared to ordinary ADRC and PSO-ADRC.
Highlights
Clamping mechanism has been widely used for workpiece processing in the industrial production [1]
This paper is organized as follows: Section 2 presents the work of establishing mathematical model of clamping force control; Sections 3 and 4 describe the Active disturbance rejection controllers (ADRCs) and chaos particle swarm optimization (CPSO)-ADRC design for clamping force control, respectively; Section 5 shows simulation experiments to verify the effectiveness of CPSO-ADRC with Particle Swarm Optimization- (PSO-)ADRC as comparison and Section 6 proposes some concluding remarks and prospects
This study focused on improving the performance of active disturbance rejection controllers for clamping force control system via online optimization
Summary
Clamping mechanism has been widely used for workpiece processing in the industrial production [1]. Though ADRC has many merits compare to conventional PID controller, very limited existence of its application can be found due to its complicated parameter adjustment. Among the optimization algorithms, Genetic Algorithm (GA) [6, 7] and Particle Swarm Optimization (PSO) [8, 9] have been widely used in the optimization process to determine optimal ADRC parameters. The adaptive genetic algorithm (AGA) was proposed to solved the adjustment problem towards ADRC systems with overabundant number of parameters [11]. Improved PSO algorithms showed superior performances on ADRC parameter optimization. A chaos particle swarm optimization (CPSO) algorithm was introduced to conduct optimization and getting optimal parameters for ADRC of an antisynchronizing different chaotic systems [13]. In order to obtain better performance for clamping force control, further parameter optimization of ADRC has been conducted. This paper is organized as follows: Section 2 presents the work of establishing mathematical model of clamping force control; Sections 3 and 4 describe the ADRC and CPSO-ADRC design for clamping force control, respectively; Section 5 shows simulation experiments to verify the effectiveness of CPSO-ADRC with PSO-ADRC as comparison and Section 6 proposes some concluding remarks and prospects
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