Abstract
In this paper, an adaptive single neuron Proportional–Integral–Derivative (PID) controller based on the extended Kalman filter (EKF) training algorithm is proposed. The use of EKF training allows online training with faster learning and convergence speeds than backpropagation training method. Moreover, the propose adaptive PID approach includes a back-calculation anti-windup scheme to deal with windup effects, which is a common problem in PID controllers. The performance of the proposed approach is shown by presenting both simulation and experimental tests, giving results that are comparable to similar and more complex implementations. Tests are performed for a four wheeled omnidirectional mobile robot. Tests show the superiority of the proposed adaptive PID controller over the conventional PID and other adaptive neural PID approaches. Experimental tests are performed on a KUKA® Youbot® omnidirectional platform.
Highlights
Proportional–Integral–Derivative (PID) controllers are still among the most popular controllers used in the industry [1,2,3]
PID, backpropagation trained adaptive neural PID controller (BP-PIDNN) [4], and an adaptive neuron PID controller trained with Hebbian learning rule (HR-PIDNN) [19]
In Section 4.1.1, the proposed extended Kalman filter (EKF) single neuron PID controller is compared with a conventional PID controller with a back-calculation anti-windup method [29]
Summary
Proportional–Integral–Derivative (PID) controllers are still among the most popular controllers used in the industry [1,2,3]. Improvements and tuning mechanisms were proposed in the literature for conventional PID controllers Those techniques are mainly offline methodologies, and in most cases, they require a model of the system, which is commonly not available [1,2,3]. Among these techniques, adaptive neural PID controllers are presented as an option due to neural networks characteristic that allows them to adapt themselves to changes in operating conditions and environmental parameters, giving the controller the capability of adapting its parameters online [5,6,7]. Adaptive control techniques are important to solve problems in robotics research, such as control of robot manipulators [8,9], control of mobile robots [10,11] and formation control [12], control of underwater vehicles [13,14], control for teleoperation systems [15] and industrial applications [16,17]
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