Advanced model predictive control framework for autonomous intelligent mechatronic systems: A tutorial overview and perspectives

AbstractRemote center of motion (RCM) constraint has attracted many research interests as one of the key challenges for robot-assisted minimally invasive surgery (RAMIS). Although it has been addressed by many studies, few of them treated the motion constraint with an independent workspace solution, which means they rely on the kinematics of the robot manipulator. This makes it difficult to replicate the solutions on other manipulators, which limits their population. In this paper, we propose a novel control framework by incorporating model predictive control (MPC) with the fuzzy approximation to improve the accuracy under the motion constraint. The fuzzy approximation is introduced to manage the kinematic uncertainties existing in the MPC control. Finally, simulations were performed and analyzed to validate the proposed algorithm. By comparison, the results prove that the proposed algorithm achieved success and satisfying performance in the presence of external disturbances.

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Controlling Hand Movements Relying on Tactile Illusions: A Model Predictive Control Framework

10.1109/whc49131.2021.9517188 ◽

2021 ◽

Author(s):

Gemma Carolina Bettelani ◽

Simone Fani ◽

Alessandro Moscatelli ◽

Paolo Salaris ◽

Matteo Bianchi

Keyword(s):

Model Predictive Control ◽

Predictive Control ◽

Hand Movements ◽

Control Framework

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An Economic Model Predictive Control Approach for Wind Power Smoothing and Tower Load Mitigation

Volume 2: Control and Optimization of Connected and Automated Ground Vehicles; Dynamic Systems and Control Education; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Energy Systems; Estimation and Identification; Intelligent Transportation and Vehicles; Manufacturing; Mechatronics; Modeling and Control of IC Engines and Aftertreatment Systems; Modeling and Control of IC Engines and Powertrain Systems; Modeling and Management of Power Systems ◽

10.1115/dscc2018-9032 ◽

2018 ◽

Author(s):

Mohamed M. Alhneaish ◽

Mohamed L. Shaltout ◽

Sayed M. Metwalli

Keyword(s):

Optimal Control ◽

Optimal Control Problem ◽

Model Predictive Control ◽

Wind Power ◽

Predictive Control ◽

Economic Model ◽

Control Algorithm ◽

Fatigue Load ◽

Economic Model Predictive Control ◽

Control Framework

An economic model predictive control framework is presented in this study for an integrated wind turbine and flywheel energy storage system. The control objective is to smooth wind power output and mitigate tower fatigue load. The optimal control problem within the model predictive control framework has been formulated as a convex optimal control problem with linear dynamics and convex constraints that can be solved globally. The performance of the proposed control algorithm is compared to that of a standard wind turbine controller. The effect of the proposed control actions on the fatigue loads acting on the tower and blades is studied. The simulation results, with various wind scenarios, showed the ability of the proposed control algorithm to achieve the aforementioned objectives in terms of smoothing output power and mitigating tower fatigue load at the cost of a minimal reduction of the wind energy harvested.

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Experimental Validation of Graph-Based Hierarchical Control for Thermal Management

Journal of Dynamic Systems Measurement and Control ◽

10.1115/1.4040211 ◽

2018 ◽

Vol 140 (10) ◽

Cited By ~ 5

Author(s):

Herschel C. Pangborn ◽

Justin P. Koeln ◽

Matthew A. Williams ◽

Andrew G. Alleyne

Keyword(s):

Fluid Flow ◽

Model Predictive Control ◽

Thermal Management ◽

Predictive Control ◽

Experimental Validation ◽

Hierarchical Control ◽

Electrical Energy ◽

Management Systems ◽

Conservation Of Mass ◽

Control Framework

This paper proposes and experimentally validates a hierarchical control framework for fluid flow systems performing thermal management in mobile energy platforms. A graph-based modeling approach derived from the conservation of mass and energy inherently captures coupling within and between physical domains. Hydrodynamic and thermodynamic graph-based models are experimentally validated on a thermal-fluid testbed. A scalable hierarchical control framework using the graph-based models with model predictive control (MPC) is proposed to manage the multidomain and multi-timescale dynamics of thermal management systems. The proposed hierarchical control framework is compared to decentralized and centralized benchmark controllers and found to maintain temperature bounds better while using less electrical energy for actuation.

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