Self-tuning of a Neuro-Adaptive PID Controller for a SCARA Robot Based on Neural Network

• Published in: Revista IEEE América Latina Vol. 16; no. 5; pp. 1364 - 1374
• Main Authors: Freire, E.O ., Rossomando, Francisco Guido, Soria, Carlos Miguel
• Format: Journal Article
• Language: English; Portuguese
• Published: Los Alamitos IEEE 01.05.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Adaptive control; Back propagation networks; Control systems design; Error correction; MIMO (control systems); MIMO communication; MIMO systems; Neural networks; nonlinear control; Nonlinear dynamics; Process control; Proportional integral derivative; RNA; Robot arms; Robots; Robustness; Tracking control; Tracking errors
• ISSN: 1548-0992; 1548-0992
• DOI: 10.1109/TLA.2018.8408429

In this paper a MIMO (Multiple-Input-Multiple-Output) adaptive neural PID (AN-PID) controller that can be applied to a nonlinear dynamics is proposed, and its use is shown in the control of a SCARA robot for two degrees of freedom. The AN-PID controller, including a neural network of the dynamic perceptron type, is designed. The proposed controller uses a RBF network to identify the model and back propagates the control error to the AN-PID controller, unlike other controllers, that use direct methods to back propagate such error. With these properties, an AN-PID controller corrects the tracking errors due to the uncertainties and variations in the robot arm dynamics. It is robust and with adaptive capacity in order to achieve a suitable control performance. Experimental results on the SCARA robot were obtained to illustrate the effectiveness of the proposed control strategy, including comparison with a classical PID. By using Lyapunov's discrete-time theory, it was demonstrated that the control error is semi-global uniformly ultimate bounded (SGUUB).