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

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 uncertainties in 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 indicate that the system with the proposed POARC not only achieves good control accuracy and smooth movement but also maintains robustness to disturbances.

气动肌肉并联关节的位姿轨迹跟踪控制

针对多输入多输出的气动肌肉并联关节,建立包含任务空间负载动态方程、容腔压力动态方程和高速开关阀平均流量方程的多阶动态系统数学模型。为保证气动肌肉并联关节系统良好动态特性的同时具有高精度的位姿轨迹跟踪,采用基于非连续投影算法的自适应鲁棒控制策略。该策略通过自适应参数估计来消除因气动肌肉并联关节系统动态数学模型的参数未知而引起的较大参数不确定,通过鲁棒反馈来消除因气动肌肉的伸缩力模型误差、摩擦力时变和关节系统的不可知干扰等引起的严重非线性不确定,且控制器基于反推设计,对多输入多输出的多阶耦合动态系统具有很好的适用性。试验结果表明:所研究的气动肌肉并联关节阶跃响应的静态误差小于0.09°,连续轨迹跟踪的标准误差小于0.15°,且具有较强的自适应性和鲁棒性。