【摘要】 为提升气动肌肉(pneumatic muscle, PM)拮抗关节系统在饱和约束条件下的控制性能，提出一种基于饱和约束项建立代价函数的Takagi-Sugeno (T-S)模糊模型预测控制(model predictive control, MPC)策略。首先，在PM动力学模型基础上，建立T-S模糊模型，对其进行离散化后构建T-S模糊预测模型。其次，考虑PM的饱和输入约束，将饱和约束转换成饱和偏差函数，引入代价函数中，建立融合了输出误差项、控制输入项以及饱和输入约束项的代价函数，运用序列二次迭代法对其进行最优控制律的迭代求解。最后，为验证控制方法的有效性，对所提出算法进行仿真与实物实验验证。结果表明：该方法可在预测时域内有效约束控制输入，避免超越气压物理限值；验证了所设计控制器在动态响应速度与跟踪精度方面的有效提升。更多还原

【Abstract】 To improve the control performance of pneumatic muscle(PM) antagonistic joint systems under input saturation constraints, a Takagi-Sugeno(T-S) fuzzy model predictive control(MPC) strategy based on a cost function with saturation constraints was proposed. Firstly, based on the dynamic model of the pneumatic artificial muscle, a T-S fuzzy model was established through fuzzy approximation. After discretization, the T-S fuzzy prediction model was constructed. Secondly, the saturation constraint of the pneumatic muscle input was considered. The saturation constraint was converted into a saturation deviation function and introduced into the cost function. A cost function integrating output error, control input, and saturation constraint terms was established. The sequential quadratic programming method was used to iteratively solve the optimal control law. Finally, to verify the effectiveness of the control method, simulations and physical experiments were conducted. The results show that the method effectively constrains control input within the prediction horizon and avoids exceeding the physical pressure limits. The designed controller significantly improves dynamic response speed and tracking accuracy.更多还原