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首页> 外文期刊>Latin American Applied Research >Model reference adaptive control for mobile robots in trajectory tracking using radial basis function neural networks
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Model reference adaptive control for mobile robots in trajectory tracking using radial basis function neural networks

机译：基于径向基函数神经网络的移动机器人轨迹跟踪模型参考自适应控制

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This paper propose an Model Reference Adaptive Control (MRAC) for mobile robots for which stability conditions and performance evaluation are given. The proposed control structure combines a feedback linearization model, based on a kinematics nominal model, and a direct neural network-based adaptive dynamics control. The architecture of the dynamic control is based on radial basis functions neural networks (RBF-NN) to construct the MRAC controller. The parameters of the adaptive dynamic controller are adjusted according to a law derived using Lyapunov stability theory and the centers of the RBF are adapted using the supervised algorithm. The resulting MRAC controller is efficient and robust in the sense that it succeeds to achieve a good tracking performance with a small computational effort. Stability result for the adaptive neuro-control system is given. It is proved that control errors are ultimately bounded as a function of the approximation error of the RBF-NN. Experimental results showing the practical feasibility and performance of the proposed approach to mobile robotics are given.
机译：本文提出了一种用于移动机器人的模型参考自适应控制（MRAC），并给出了稳定性条件和性能评估。所提出的控制结构结合了基于运动学标称模型的反馈线性化模型和基于直接神经网络的自适应动力学控制。动态控制的体系结构基于径向基函数神经网络（RBF-NN）来构造MRAC控制器。自适应动态控制器的参数根据使用Lyapunov稳定性理论导出的定律进行调整，RBF的中心使用监督算法进行调整。最终的MRAC控制器高效且强大，因为它以较少的计算量就能成功实现良好的跟踪性能。给出了自适应神经控制系统的稳定性结果。事实证明，控制误差最终取决于RBF-NN的近似误差。实验结果表明了所提出的移动机器人方法的实际可行性和性能。

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