In this paper, a novel general regression neural network-based data-driven model-free nonlinear predictive functional control approach is proposed for a class of unknown single-input/single-output discrete-time nonlinear systems. The historical measurable data are used to construct an explicit model for control design and estimate system time-varying parameters online, making the approach model free. By using basis functions to structuralize system input, online computational load (OCL) of the controller can be reduced dramatically compared with existing model-free predictive control approaches. Moreover, the prediction function is realized by a general regression neural network-based system parameter predictive algorithm, bringing the controller good robustness and strong capability against sudden changes from prescribed references and external circumstances simultaneously. Simulation results show that, by using much less OCL, the proposed approach-based closed-loop systems can achieve even smaller overshoot and shorter response time than the existing model-free predictive control approaches.

General regression neural network-based data-driven model-free predictive functional control for a class of discrete-time nonlinear systems

Traditional dynamics model of manipulator is based on rigid body dynamics model, but there are some limitations in accuracy and efficiency in the actual operation process. Taking flexible joint manipulator as the research object, the traditional PID control and the neural network-based PID control are compared and studied respectively. According to the simulation results, it can be found that the application of neural network in the PID control can realize the effective control of complex controlled objects and the online self- tuning of the PID parameters. It makes the system response faster and control accuracy higher, to reduce the tracking error effectively.

Chen Jie

Papers

Flexible Joint Manipulator Controlling Algorithm Based on Neural Network Improved PID