Kumar Naveen
Department of Mathematics, National Institute of Technology Kurukshetra, Kurukshetra 136119, Haryana, India.
Department of Mathematics, National Institute of Technology Kurukshetra, Kurukshetra 136119, Haryana, India; Department of Applied Mathematics, Mahatma Jyotiba Phule Rohilkhand University Bareilly, Bareilly 243006, Uttar Pradesh, India.
ISA Trans. 2024 Oct;153:78-95. doi: 10.1016/j.isatra.2024.07.020. Epub 2024 Jul 18.
In this research, a new hybrid backstepping control strategy based on a neural network is proposed for tractor-trailer mobile manipulators in the presence of unknown wheel slippage and disturbances. To minimize the negative impacts of wheel slippage, the desired velocities of the tractor's wheels are computed with a proposed kinematic control model with an adaptive term. As the system's dynamical model contains unavoidable uncertainties, model-based backstepping control technique is unable to effectively manage these systems. Hence, the proposed controller blends a radial basis function neural network with the merits of a dynamical model-based backstepping approach. The neural networks are employed to approximate the non-linear unknown smooth function. To minimize the impact of external disturbances, and network reconstruction error an adaptive term is added to the control law. The Lyapunov theorem and Barbalat's lemma are employed to guarantee the stability of the control method. The tracking error is shown to be bounded and to rapidly converge to zero with the proposed method. To demonstrate the efficacy and validity of the control mechanism, comparison simulation results are presented.
在本研究中，针对存在未知车轮打滑和干扰的牵引车-挂车移动机械手，提出了一种基于神经网络的新型混合反步控制策略。为了将车轮打滑的负面影响降至最低，利用带有自适应项的运动学控制模型计算牵引车车轮的期望速度。由于系统动力学模型包含不可避免的不确定性，基于模型的反步控制技术无法有效管理这些系统。因此，所提出的控制器将径向基函数神经网络与基于动力学模型的反步方法的优点相结合。采用神经网络来逼近非线性未知光滑函数。为了将外部干扰和网络重构误差的影响降至最低，在控制律中添加了一个自适应项。利用李雅普诺夫定理和巴尔巴拉引理来保证控制方法的稳定性。结果表明，所提出的方法能使跟踪误差有界并迅速收敛到零。为了证明控制机制的有效性和正确性，给出了对比仿真结果。
