Abstract
The higher goal of rehabilitation robot is to aid a person to achieve a desired functional task (e.g., tracking trajectory) based on assisted-as-needed principle. To this goal, a new adaptive inverse optimal hybrid control (AHC) combining inverse optimal control and actor-critic learning is proposed. Specifically, an uncertain nonlinear rehabilitation robot model is firstly developed that includes human motor behavior dynamics. Then, based on this model, an open-loop error system is formed; thereafter, an inverse optimal control input is designed to minimize the cost functional and a NN-based actor-critic feedforward signal is responsible for the nonlinear dynamic part contaminated by uncertainties. Finally, the AHC controller is proven (through a Lyapunov-based stability analysis) to yield a global uniformly ultimately bounded stability result, and the resulting cost functional is meaningful. Simulation and experiment on rehabilitation robot demonstrate the effectiveness of the proposed control scheme.
Highlights
Rehabilitation robots are becoming increasingly common in upper extremity rehabilitation [1], because they are able to provide intensive rehabilitation consistently for a longer duration, and able to offer object assessment for patient’s motor performance
To overcome the above-mentioned difficulties and to develop an AC-based controller for the highly nonlinear rehabilitation robots (RRs), a new adaptive inverse optimal hybrid control (AHC) combining inverse optimal control and AC learning is proposed in this paper, where the inverse optimal controller guarantees the RRs’ optimality by minimizing a meaningful cost function; the effort of solving the HJB equation is totally avoided; the AC-based learning method helps the robot select the meaningful cost function despite being uncertain in RRs
For the rehabilitation robot system (1)–(3), let the proposed identification scheme in (15) along with the weight update law in (17) be used to identify the human motor behavior dynamics in (2), and let the action-critic controller given in (15) and (30) along with the weight update laws for the action and critic NN given in (18) and (32) and the inverse optimal control u01 in (29), respectively, ensure that all system signals are bounded under closed-loop operation and that the position tracking error is regulated in the sense that
Summary
Rehabilitation robots are becoming increasingly common in upper extremity rehabilitation [1], because they are able to provide intensive rehabilitation consistently for a longer duration, and able to offer object assessment for patient’s motor performance. Relevant studies show these NN-based intelligent RRs controllers can Mathematical Problems in Engineering provide a better performance than the above-mentioned traditional adaptive control Their results are limited to uniformly bounded (UUB) stability because of the inevitable NN reconstruction error [22]. To overcome the above-mentioned difficulties and to develop an AC-based controller for the highly nonlinear RRs, a new adaptive inverse optimal hybrid control (AHC) combining inverse optimal control and AC learning is proposed in this paper, where the inverse optimal controller guarantees the RRs’ optimality by minimizing a meaningful cost function; the effort of solving the HJB equation is totally avoided; the AC-based learning method helps the robot select the meaningful cost function despite being uncertain in RRs. In addition, understanding the human motor behavior is essential to assisted-as-needed rehabilitation training.
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