An Improved PID Controller for the Compliant Constant-Force Actuator Based on BP Neural Network and Smith Predictor
Guojin Pei, Ming Yu, Yaohui Xu, Cui Ma, Houhu Lai, Fokui Chen, Hui Lin
Abstract
A compliant constant-force actuator based on the cylinder is an important tool for the contact operation of robots. Due to the nonlinearity and time delay of the pneumatic system, the traditional proportional–integral–derivative (PID) method for constant force control does not work so well. In this paper, an improved PID control method combining a backpropagation (BP) neural network and the Smith predictor is proposed. Through MATLAB simulation and experimental validation, the results show that the proposed method can shorten the maximum overshoot and the adjustment time compared with traditional the PID method.
Topics & Concepts
PID controllerControl theory (sociology)Overshoot (microwave communication)Artificial neural networkMATLABConstant (computer programming)BackpropagationActuatorNonlinear systemComputer scienceControl engineeringTime constantEngineeringControl (management)Artificial intelligencePhysicsTemperature controlTelecommunicationsQuantum mechanicsProgramming languageElectrical engineeringOperating systemHydraulic and Pneumatic SystemsIterative Learning Control SystemsAdvanced Sensor and Control Systems