Abstract
Trajectory tracking control is indispensable for a wheeled mobile robot to achieve successful navigation. The classical tracking control systems that are used in wheeled mobile robots do not compensate for the parameter uncertainties and external disturbances. For control strategy, this paper presents a novel hybrid approach, combining a neural network-based kinematic controller and a model reference adaptive control. The controller parameters are adaptively determined online using neural networks. The adaptively tuned kinematic controller ensures a fast convergence to the desired trajectory. The model reference adaptive controller retains the desired tracking performance when parameter and model uncertainties occur. The Lyapunov stability method is used to obtain the adaptive gains which guarantee the asymptotic stability of the error dynamics, where the error is the difference between the outputs of the reference model and the actual plant. The performance of the proposed controller is compared with that of the PID controller, kinematic controller, and adaptive dynamic controller using different performance analysis indices such as integral absolute error, integral squared error, and mean absolute error. Simulation studies demonstrate that the proposed controller achieves high tracking accuracy and fast convergence as compared to the PID, kinematic, and adaptive dynamic controllers considering parameter uncertainties and slip disturbances. The outcomes of the simulation studies also illustrate that the proposed controller achieves the best transient performance. Experiments using real-world tests based on a two-wheeled differential drive robot architecture have elucidated the feasibility of the developed controller regarding tracking accuracy, total control effort, and robustness against uncertainties.
Highlights
In the past few decades, the usage of unmanned ground vehicles (UGVs) is increasing rapidly because of their high performance in various applications such as military, agricultural imaging, surveillance, transport, and logistics
Motivated by the above findings, this work focuses on solving the trajectory tracking control problem of wheeled mobile robot which is under the influence of parameter uncertainties, measurement noises, and external disturbances, using a neural network based controller which is adaptive and nonlinear
A comparative assessment of the performance of the proposed neural network based adaptive controller is prepared by comparing it with PID controller, A
Summary
In the past few decades, the usage of unmanned ground vehicles (UGVs) is increasing rapidly because of their high performance in various applications such as military, agricultural imaging, surveillance, transport, and logistics. Authors of [17] proposed a backstepping controller based on an adaptive Elman neural network which could approximate the uncertainties leading to improved controller performance This method has the problem of over-fitting. Motivated by the above findings, this work focuses on solving the trajectory tracking control problem of wheeled mobile robot which is under the influence of parameter uncertainties, measurement noises, and external disturbances, using a neural network based controller which is adaptive and nonlinear. Simulation results and real world experiments illustrate that the neural network based adaptive controller brings significant improvement in terms of tracking accuracy, control input, and total control effort as compared to the PID, kinematic, and adaptive dynamic controllers.
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