Key result
EAMPC improves efficiency and reduces computation cost versus min-max predictive control for artificial muscle tracking.
Why the study?
Traditional model predictive control incurs high computation cost and time performance demands when tracking pneumatic artificial muscle antagonistic joint trajectories.
Answers are grounded in this paper and cite their sources.
The proposed EAMPC method improves computational efficiency and reduces time performance for pneumatic artificial muscle antagonistic joint trajectory tracking.
In this paper, we present an explicit solution to adaptive model predictive control for a pneumatic artificial muscle (PAM) antagonistic joint trajectory tracking. Compared with the traditional model predictive control, the explicit model predictive control may pre-calculate the form of optimization solution offline, which saves the computation cost and reduce the time performance of the system. Meanwhile, we utilize an adaptive method to estimate the disturbance arbitrarily, instead of maximizing the disturbance directly. Finally, the simulation results and running time of the explicit adaptive model predictive control (EAMPC) method and the min-max model predictive control method are compared, which shows the efficiency of the proposed algorithm.
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Zhou et al. (2022) studied Pneumatic artificial muscle (PAM) antagonistic joint trajectory tracking. Explicit adaptive model predictive control (EAMPC) vs. Min-max model predictive control method was evaluated on Simulation results and running time. Explicit adaptive model predictive control (EAMPC) improved efficiency and reduced computation cost compared to min-max model predictive control for pneumatic artificial muscle trajectory tracking.
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