A New Projected Active Set Conjugate Gradient Approach for Taylor-Type Model Predictive Control: Application to Lower Limb Rehabilitation Robots With Passive and Active Rehabilitation

Abstract

In this paper, a three-order Taylor-type numerical differentiation formula is firstly utilized to linearize and discretize constrained conditions of model predictive control (MPC), which can be generalized from lower limb rehabilitation robots. Meanwhile, a new numerical approach that projected an active set conjugate gradient approach is proposed, analyzed, and investigated to solve MPC. This numerical approach not only incorporates both the active set and conjugate gradient approach but also utilizes a projective operator, which can guarantee that the equality constraints are always satisfied. Furthermore, rigorous proof of feasibility and global convergence also shows that the proposed approach can effectively solve MPC with equality and bound constraints. Finally, an echo state network (ESN) is established in simulations to realize intention recognition for human–machine interactive control and active rehabilitation training of lower-limb rehabilitation robots; simulation results are also reported and analyzed to substantiate that ESN can accurately identify motion intention, and the projected active set conjugate gradient approach is feasible and effective for lower-limb rehabilitation robot of MPC with passive and active rehabilitation training. This approach also ensures computational when disturbed by uncertainties in system.

Development of a Rehabilitation Chair Design Based on a Functional Technology Matrix and Multilevel Evaluation Methods

“…Han Jianhai designed an active rehabilitation training system based on virtual reality technology, aiming to increase patients' interest in rehabilitation at a low price, but more severe patients are unable to control their limbs autonomously and need hardware products for more reliable rehabilitation [29]. Tian Shi presented a numerical method for solving the active set conjugate gradient method, which is feasible and effective for MPC lower limb rehabilitation robots trained in both passive and active rehabilitation [30]. Khaled M. Goher presented the design and development of a prototype of a reconfigurable wheelchair for rehabilitation and self-assistance, where the user can use an adjustable back support with two linear actuators to adjust the posture of the upper body [31].…”

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals

“…Experimental results demonstrate that the NSZNN controller is still feasible, effective and superior under different noise interference. Besides, the developed ZNN-based models will be exploited to the performance of the related applications, for instance, lower limb rehabilitation robot (Shi et al, 2020 ), industrial robot (Li et al, 2017 ), and redundant manipulators (Xie et al, 2022 ) and so on.…”