首页> 外文期刊>Arabian Journal for Science and Engineering. Section A, Sciences >Optimal Sliding Mode Controller for an Active Transfemoral Prosthesis Using State-Dependent Riccati Equation Approach
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Optimal Sliding Mode Controller for an Active Transfemoral Prosthesis Using State-Dependent Riccati Equation Approach

机译:使用状态依赖性Riccati方程方法的有源发生器假体的最佳滑动模式控制器

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摘要

In this paper, a nonlinear robust optimal controller is designed for an active transfemoral prosthesis using sliding mode control and state-dependent Riccati equation control. In this method, unlike most nonlinear control methods, the Jacobian approach is not used for model linearization and system's state space equations are used directly. This is a reason for the accuracy and flexibility of the proposed controller in design compared to other conventional methods. Since the proposed approach is energy-driven, the main objective of this research is to optimize the energy consumption of the robot/prosthesis system, reduce the effects of disturbances, noise, and parametric uncertainties, and desirable trajectory tracking of the vertical displacement in hip and thigh and knee angles. To this aim, the integral state control technique and the boundary layer are used for reconciliation between tracking performance and control signal chattering. In this study, the performance of the controller is assessed for nominal system and uncertain system with±30% parameters' uncertainty by considering the saturation bounds of control signals. The simulation results demonstrate a decrease in the control effort, good robustness in the presence of uncertainty, and desirable tracking performance compared to a robust adaptive impedance control approach.
机译:在本文中,设计了一种非线性鲁棒优化控制器,用于使用滑动模式控制和状态相关的Riccati方程控制有源经帧性假体。在此方法中,与大多数非线性控制方法不同,Jacobian方法不用于模型线性化,直接使用系统的状态空间方程。与其他传统方法相比,这是所提出的控制器的准确性和灵活性的原因。由于拟议的方法是能源驱动的,本研究的主要目的是优化机器人/假体系的能耗,降低干扰,噪声和参数的不确定性的影响,以及所需的术语阶段垂直位移的轨迹跟踪和大腿和膝盖角度。为此目的,积分状态控制技术和边界层用于跟踪性能和控制信号抖动之间的对和解。在本研究中,通过考虑控制信号的饱和界限,评估控制器的性能,以±30%参数的不确定性评估标称系统和不确定的系统。仿真结果表明,与鲁棒的自适应阻抗控制方法相比,控制努力的降低,在存在不确定度和期望的跟踪性能方面的良好鲁棒性。

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