Model Predictive Control for Attitude Tracking of Rehabilitation Exoskeleton Robots

In this paper, an attitude tracking controller is proposed for lower-limb rehabilitation exoskeleton robots with parameter uncertainties and external disturbances using model predictive control (MPC). Firstly, the exoskeleton dynamics model is converted into a fully-actuated (FA) system model. Then, the above system undergoes discretization through a zero-order hold mechanism. Lastly, a MPC approach is performed to obtain the instantaneous optimal control signal inputs in the sense of FA system with parameter uncertainties and external disturbances, aiming to minimize trajectory error. For comparison, a classical PID controller is designed as the benchmark. A numerical simulation example is designed to evaluate the proposed controller. The results show that the proposed MPC control method exhibits performance improvement, which is reflected in greater tracking precision and tougher robustness compared with the benchmark controller.