Error-Bounded Reference Tracking MPC for Machines With Structural Flexibility

Yuan, Meng; Manzie, Chris; Good, M C; Shames, Iman; Gan, Lu; Keynejad, Farzad; Robinette, Troy

doi:10.1109/tie.2019.2949521

Cited by 9 publications

(7 citation statements)

References 30 publications

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“…Conventionally, the point-to-point movement or sinusoidal reference are used for performance validation in motion control considering that most of the paths are consist of a straight line or a circle [7]. The tracking error is used as the performance metrics to evaluate the tracking accuracy [34,35].…”

Section: Tracking Results and Comparisonmentioning

confidence: 99%

Tracking Control of Single-Axis Feed Drives Based on ADRC and Feedback Linearisation

Yuan

2021

Electronics

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The accuracy of manufacturing highly characterises the performance of industrial motion control. However, detrimental effects such as nonlinear coupling, model uncertainty and unknown external disturbances severely affect trajectory tracking performance. In this paper, we proposed an ADRC and feedback linearisation-based control algorithm for the high-precision trajectory tracking of feed drives. The controller was rigorously designed to ensure the convergence of observer states and tracking error. The applicability of the proposed approach was successfully demonstrated via high-fidelity simulation, and the numerical results based on different tracking methods were compared.

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Section: Tracking Results and Comparisonmentioning

confidence: 99%

Tracking Control of Single-Axis Feed Drives Based on ADRC and Feedback Linearisation

Yuan

2021

Electronics

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“…Þ remains in the error tube defined by R under the map (6). For the error system (15), one method of computing this mRPI set involves the iteration…”

Section: Error Tube and Constraint Satisfactionmentioning

confidence: 99%

Robust iterative learning model predictive control for repetitive motion of maglev planar motor

Zhang

2022

IET Electric Power Appl

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This paper presents a robust iterative learning model predictive control (RILMPC) scheme for repetitive trajectory tracking of a magnetically levitated (maglev) planar motor. The motivation lies in the improvement of tracking performance and disturbance rejection ability for the maglev system. Relying on the past error in the repetitive motion, the model discrepancy is persistently compensated with iterations to improve the prediction accuracy. The cost function that serves as the upper bound of the tracking error is then derived from the past control trajectory based on the Lipschitz property of disturbances. In the proposed RILMPC scheme, a minimal robust positive invariant set is introduced into the MPC optimisation to cope with unmodeled dynamics and disturbances. Furthermore, it is theoretically shown that the perturbed closed-loop system is stable, and the proposed RILMPC scheme is recursively feasible with perfect tracking performance under some conditions. Finally, comparative experiments carried out on a maglev planar motor demonstrate that the proposed control strategy possesses satisfactory transient/steady-state tracking performance and robustness against disturbances.

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“…(3) Model Predictive Control (MPC) MPC recursively solves the open-loop optimal control problem by using real-time state measurement as the initial condition [84]. It systematically incorporates the system state and controls the input constraints [26]. Due to the high flexibility in expressing various control problems, it allows MPC to digest any nonlinearity of the system model without any approximations.…”

Section: ) State-dependent Riccati Equation (Sdre) Controlmentioning

confidence: 99%

“…Generally, two factors need to be considered when establishing an AUV trajectory tracking model: (i) the complexity of the model will affect the calculation efficiency, and (ii) the accuracy of the model is the basis for an accurate analysis of the system [25]. Inspired by robot motion modeling, Fossen developed a six degree-of-freedom (DOF) motion model of a maritime vehicle in vector form [26]. This form can make it easier for researchers to understand the physical meaning of each part of the motion model, and greatly reduce the complexity of AUV motion controller derivation [27].…”

Section: Introductionmentioning

confidence: 99%

AUV Trajectory Tracking Models and Control Strategies: A Review

2021

JMSE

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Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.

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Error-Bounded Reference Tracking MPC for Machines With Structural Flexibility

Cited by 9 publications

References 30 publications

Tracking Control of Single-Axis Feed Drives Based on ADRC and Feedback Linearisation

Tracking Control of Single-Axis Feed Drives Based on ADRC and Feedback Linearisation

Robust iterative learning model predictive control for repetitive motion of maglev planar motor

AUV Trajectory Tracking Models and Control Strategies: A Review

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