Faulty of Information Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China; Engineering Research Center of Digital Community, Ministry of Education, Beijing 100124, China.
Faulty of Information Technology, Beijing University of Technology, Beijing 100124, China; Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing 100124, China.
ISA Trans. 2019 Mar;86:201-214. doi: 10.1016/j.isatra.2018.10.043. Epub 2018 Nov 3.
This paper presents a novel neural network adaptive sliding mode control (NNASMC) method to design the dynamic control system for an omnidirectional vehicle. The omnidirectional vehicle is equipped with four Mecanum wheels that are actuated by separate motors, and thus has the omnidirectional mobility and excellent athletic ability in a narrow space. Considering various uncertainties and unknown external disturbances, kinematic and dynamic models of the omnidirectional vehicle are established. The inner-loop controller is designed based the sliding mode control (SMC) method, while the out-loop controller uses the proportion integral derivative (PID) method. In order to achieve the stable and robust performance, the artificial neural network (ANN) based adaptive law is introduced to model and estimated the various uncertainties disturbances. Stability and robustness of the proposed control method are analyzed using the Lyapunov theory. The performance of the proposed NNASMC method is verified and compared with the classical PID controller and SMC controller through both the computer simulation and the platform experiment. Results validate the effectiveness and robustness of the NNASMC method in presence of uncertainties and unknown external disturbances.
本文提出了一种新颖的神经网络自适应滑模控制（NNASMC）方法，用于设计全方位车辆的动态控制系统。该全方位车辆配备了四个由单独电机驱动的麦克纳姆轮，因此具有全方位移动性和在狭窄空间内的卓越运动能力。考虑到各种不确定性和未知的外部干扰，建立了全方位车辆的运动学和动力学模型。基于滑模控制（SMC）方法设计了内环控制器，而外环控制器则使用比例积分微分（PID）方法。为了实现稳定和鲁棒性能，引入了基于人工神经网络（ANN）的自适应律来建模和估计各种不确定性干扰。利用 Lyapunov 理论分析了所提出控制方法的稳定性和鲁棒性。通过计算机仿真和平台实验对所提出的 NNASMC 方法的性能进行了验证和比较，结果验证了在存在不确定性和未知外部干扰的情况下，NNASMC 方法的有效性和鲁棒性。
