Partially Internal Thermally Coupled Air Separation Column (P-ITCASC) is a highly energy-efficient and costeffective technology. However, its complex dynamics resulting from thermal coupling pose a challenge to the op...
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Partially Internal Thermally Coupled Air Separation Column (P-ITCASC) is a highly energy-efficient and costeffective technology. However, its complex dynamics resulting from thermal coupling pose a challenge to the operating stability of this technology. This article, therefore, proposes an adaptive model predictive control (AMPC) scheme for the P-ITCASC process. The controller incorporates an auto-regressive and exogenous inputs (ARX) model, a linear time-varying Kalman filter, and a recursive polynomial model estimator (RPME) algorithm. Within RPME, ARX polynomialmodels are identified and utilized to estimate the time-varying parameters and update the prediction model of the process. The process states are observed through the Kalman filter, and the constrained receding horizon optimization problem is solved using quadratic programming. This control scheme ensures the closed-loop system's feasibility and stability in the presence of output/input constraints. An adaptive generic model control, model predictive control, and adaptive internal model control schemes were also designed for benchmarking study. Numerical simulations show that AMPC is more efficient in handling nonlinear dynamics and maintaining product concentration to desired set points compared to other control schemes.
There is a growing need for effective and adaptive robot-assisted rehabilitation platforms for post-stroke patients which can facilitate considerably their sensorimotor control performance, and also ensure safety for ...
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ISBN:
(纸本)9781479940844
There is a growing need for effective and adaptive robot-assisted rehabilitation platforms for post-stroke patients which can facilitate considerably their sensorimotor control performance, and also ensure safety for the patients. A 3-DOF adaptive robot-assisted rehabilitation platform is proposed in this work which uses at its core a hybrid impedance control framework to track simultaneously both desired force and position trajectory, while regulating the apparent impedance of the robot as seen by the patient to ensure robot-patient compliant motion. To make the system adaptive to the patient recovery process, the impedance characteristic of the patient's impaired limb is modeled as a parameter of recovery, and is estimated online using a recursive polynomial model estimator. A hybrid automata is then implemented to specify different apparent robot impedances to track the recovery process. Preliminary simulation results showed good response of the proposed framework to the changing patient's arm impedance profile as well as good trajectory tracking of force and position in task space.
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