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Pressure observer based adaptive robust trajectory tracking control of a parallel manipulator driven by pneumatic muscles

机译:基于压力观测器的气动机械臂并联机器人的自适应鲁棒轨迹跟踪控制

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This paper presents a pressure observer based adaptive robust controller (POARC) for posture trajectory tracking of a parallel manipulator driven by three pneumatic muscles without pressure sensors. Due to model errors of the static forces and friction forces of pneumatic muscles, simplified average flow rate characteristics of valves, unknown disturbances of entire system, and unmeasured pressures, there exist rather severe parametric uncertainties, nonlinear uncertainties and dynamic uncertaintiesin modeling of the parallel manipulator. A nonlinear pressure observer is constructed to estimate unknown pressures on the basis of a single-input-single-output (SISO) decoupling model that is simplified from the actual multiple-input-multiple-output (MIMO) coupling model of the parallel manipulator. Then, an adaptive robust controller integrated with the pressure observer is developed to accomplish high precision posture trajectory tracking of the parallel manipulator. The experimental results indicatethat the system with the proposed POARC not only achieves good control accuracy and smooth movement but also maintains robustness to disturbances.
机译:本文提出了一种基于压力观测器的自适应鲁棒控制器(POARC),用于跟踪由三个没有压力传感器的气动肌肉驱动的并联机械手的姿态轨迹。由于气动肌肉的静力和摩擦力的模型误差,阀门的平均流量特性简化,整个系统的未知扰动以及无法测量的压力,在并联机械手的建模中存在相当严重的参数不确定性,非线性不确定性和动态不确定性。构造了一个非线性压力观测器,以根据单输入单输出(SISO)解耦模型估算未知压力,该模型从并行操纵器的实际多输入多输出(MIMO)耦合模型简化了。然后,开发了一种与压力观测器集成在一起的自适应鲁棒控制器,以完成对并联机械手的高精度姿态轨迹跟踪。实验结果表明,采用提出的POARC的系统不仅具有良好的控制精度和平稳的运动,而且保持了对干扰的鲁棒性。

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