IEEE Trans Cybern. 2013 Feb;43(1):170-9. doi: 10.1109/TSMCB.2012.2202900. Epub 2012 Jul 3.
As a model is only an abstraction of the real system, unmodeled dynamics, parameter variations, and disturbances can result in poor performance of a conventional controller based on this model. In such cases, a conventional controller cannot remain well tuned. This paper presents the control of a spherical rolling robot by using an adaptive neuro-fuzzy controller in combination with a sliding-mode control (SMC)-theory-based learning algorithm. The proposed control structure consists of a neuro-fuzzy network and a conventional controller which is used to guarantee the asymptotic stability of the system in a compact space. The parameter updating rules of the neuro-fuzzy system using SMC theory are derived, and the stability of the learning is proven using a Lyapunov function. The simulation results show that the control scheme with the proposed SMC-theory-based learning algorithm is able to not only eliminate the steady-state error but also improve the transient response performance of the spherical rolling robot without knowing its dynamic equations.
由于模型只是真实系统的抽象，因此未建模的动态、参数变化和干扰可能会导致基于该模型的传统控制器性能不佳。在这种情况下，传统控制器无法保持良好的调谐。本文提出了一种使用自适应神经模糊控制器结合滑模控制（SMC）理论学习算法对球形滚动机器人进行控制的方法。所提出的控制结构由一个神经模糊网络和一个传统控制器组成，该控制器用于在紧凑的空间内保证系统的渐近稳定性。使用 SMC 理论推导出神经模糊系统的参数更新规则，并使用李雅普诺夫函数证明了学习的稳定性。仿真结果表明，该控制方案能够消除稳态误差，并且能够提高球形滚动机器人的瞬态响应性能，而无需了解其动态方程。
