Deterministic Adaptive Robust Control With a Novel Optimal Gain Design Approach for a Fuzzy 2-DOF Lower Limb Exoskeleton Robot System

To enhance the lower limbs' rehabilitation training of stroke patients, in this article, a deterministic adaptive robust control with control gain parameters optimized by a novel cooperative game theory is proposed for the two-degree-of-freedom (DOF) lower limb exoskeleton robot system (LLERs) with uncertainties and external disturbances. On the one hand, the deterministic adaptive robust control put forward will guarantee the uniform boundedness and uniform ultimate boundedness of gait constraint deviation and parameter estimation error. On the other hand, for uncertainties and external disturbance (possibly fast time-varying), which will arise in the two-DOF LLERs inevitably, we suppose that these uncertainties and disturbances are bounded, and their bounds are lying within the fuzzy sets, which can be characterized by membership functions; with such descriptions and control performance analysis, a novel cooperative game with two players participated will be formulated to seek the Pareto optimal solutions for the control gains of deterministic adaptive robust control proposed, and furthermore, the existence of optimal solutions is also shown by invoking a numerical technique. Eventually, the simulation results presented have verified the effectiveness of our proposed methodology on improving rehabilitation training of lower limbs.

Automated summary

In this article, a deterministic adaptive robust control method with control gain parameters optimized by a novel cooperative game theory is proposed for the two-degree-of-freedom lower limb exoskeleton robot system with uncertainties and external disturbances. The control approach guarantees the boundedness of gait constraint deviation and parameter estimation error, and a novel cooperative game is formulated to seek Pareto optimal solutions for the control gains. Numerical technique is used to show the existence of optimal solutions, and simulation results confirm the effectiveness of the proposed methodology in improving lower limb rehabilitation training.