Evolutionary Algorithm-Based Friction Feedforward Compensation for a Pneumatic Rotary Actuator Servo System

Abstract

The friction interference in the pneumatic rotary actuator is the primary factor affecting the position accuracy of a pneumatic rotary actuator servo system. The paper proposes an evolutionary algorithm-based friction-forward compensation control architecture for improving position accuracy. Firstly, the basic equations of the valve-controlled actuator are derived and linearized in the middle position, and the transfer function of the system is further obtained. Then, the evolutionary algorithm-based friction feedforward compensation control architecture is structured, including that the evolutionary algorithm is used to optimize the controller coefficients and identify the friction parameters. Finally, the contrast experiments of four control strategies (the traditional PD control, the PD control with friction feedforward compensation without evolutionary algorithm tuning, the PD control with friction feedforward compensation based on the differential evolution algorithm, and the PD control with friction feedforward compensation based on the genetic algorithm) are carried out on the experimental platform. The experimental results reveal that the evolutionary algorithm-based friction feedforward compensation greatly improves the position tracking accuracy and positioning accuracy, and that the differential evolution-based case achieves better accuracy. Also, the system with the friction feedforward compensation still maintains high accuracy and strong stability in the case of load.

Discovering Stick-Slip-Resistant Servo Control Algorithm Using Genetic Programming

“…Having a parametric friction model, the common approach is to identify its parameters using an optimization technique. Many classic and intelligent methods are used: distinguishing inertia torque and friction torque [ 10 ], iterative minimisation of the error between a model and experiment result [ 11 ], gradient-based convex optimization [ 12 ], genetic algorithms [ 13 ], accelerated evolutionary programming [ 6 ], and particle swarm optimization hybridized with neural dynamic programming [ 14 ]. Approaches involving online adaptative estimation of the motor parameters including friction model coefficients [ 15 ] exist.…”

“…Similar optimization techniques are applied for tuning servo controllers, e.g., linear quadratic regulator [ 17 ] and different variants of genetic algorithms [ 18 , 19 ]. Sometimes, the same method is used for the friction model identification and controller tuning, e.g., glowworm swarm optimization [ 8 ], genetic algorithm, and differential evolution [ 13 ]. Asymptotic tracking control for nonaffine systems with disturbances [ 20 ] is an advanced method proposed for rejection of unknown disturbances.…”

A Novel Genetic Algorithm for the Synthetical Sensor-Weapon-Target Assignment Problem

“…This is because heuristic algorithms search for solutions using specific heuristic information, which traps them into the local optimal easily. The second category is the evolutionary algorithm (EA) [29,30], such as GA and PSO. As EA-based algorithms use many flexible mechanisms that fully consider the prior knowledge, they can achieve a satisfactory solution.…”

Nonlinear Model Establishment and Experimental Verification of a Pneumatic Rotary Actuator Position Servo System

“…Intelligent control based on a complex model follows the traditional thinking of electromechanical system research, which has been understood and used by researchers. In previous studies [7][8][9], the valve-controlled actuator system model was usually linearized to a third-order transfer function by the method of intermediate position linearization. This type of linearized model is simple in structure, and the system accuracy often depends on the control effect of the intelligent algorithm.…”