Computationally efficient model predictive control for a class of linear parameter‐varying systems

Cavanini, Luca; Cimini, Gionata; Ippoliti, Gianluca

doi:10.1049/iet-cta.2017.1096

Cited by 25 publications

(7 citation statements)

References 33 publications

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“…To reduce the computational load of the MPC control module, a Linear Parameter Varying (LPV) modelling approach was adopted [10,11]. The LPV-MPC can be treated as a linear problem, although it can represent a good approximation of a nonlinear model and the system matrices are parameter dependent and change with time.…”

Section: Introductionmentioning

confidence: 99%

Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios

et al. 2021

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A control system for driving an Autonomous Underwater Vehicle (AUV) performing docking operations in presence of tidal current disturbances is proposed. The nonlinear model of the vehicle has been modelled in a Linear Parameter-Varying (LPV) form. This is suitable for the design of the control system using a model-based approach. The LPV model was used for a Model Predictive Control (MPC) design for computing the set of forces and moments driving the nonlinear vehicle model. The LPV-MPC control action is mapped into the reference signals for the actuators by using a Thrust Allocation (TA) algorithm. This was based on the nonlinear models for the actuators and their position and orientation on the vehicle’s hull. The structural decomposition of MPC and TA reduces the computational burden involved in computing the control law on-line on an embedded control board. Both MPC and TA algorithms use the vehicle’s linear and angular positions, and velocities that are estimated by an LPV based Kalman Filter (KF). The proposed control system has been tested in different docking scenarios using various tidal current disturbances acting on the vehicle as an unmeasured disturbance. The simulation results show the controller is effective in controlling the AUV over the range of control scenarios meeting the constraints and specifications.

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Section: Introductionmentioning

confidence: 99%

Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios

et al. 2021

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“…For the desire of handling in constraint situation as well as extending the control requirement, model predictive control (MPC) approach is developed [8] - [29]. Because the principle of MPC technique is considered with two steps, including establishing the predictive model and solving the optimization problem, the computational effort is an obstacle.…”

Section: Introductionmentioning

confidence: 99%

“…Because the principle of MPC technique is considered with two steps, including establishing the predictive model and solving the optimization problem, the computational effort is an obstacle. The quadratic programming (QP) method is introduced to address this issue [8,9]. For implementing MPC technique in perturbed systems, due to the difficulty in considering the predictive model, the Neural Network-based MPC was investigated by using Neural Network for estimating the predictive model [12].…”

Section: Introductionmentioning

confidence: 99%

Robust model predictive kinematic tracking control with terminal region for wheeled robotic systems

Nam

Quang

2021

Automatika

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This paper addresses the nonlinear model predictive control (MPC) for wheeled mobile robots (WMRs) under external disturbance. The decoupling technique is utilized based on the nonholonomic constraint description for separating the WMR model. This method is able to achieve the under-actuated kinematic sub-system without disturbance and fully-actuated dynamic subsystem in presence of disturbance. Thanks to the decoupling technique, the disturbance is lumped into dynamic sub-system. The novelty lies in that the MPC-based tracking control with fixed initial point guarantees the stability based on a new establishment of terminal region and equivalent terminal controller. The feasibility problem is demonstrated to lead the tracking problem using theoretical analysis. Moreover, the control structure is inserted more the robust nonlinear dynamic controller. The effectiveness and advantages of the proposed control scheme are verified by numerical simulations using Yamip tool.

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“…Model predictive control (MPC) is a main concern for control design applied in different systems such as linear or nonlinear [1][2][3], continuous or discrete [4], and monovariable or multivariable [5]. Actually, MPC is a common technique for the dynamical systems' stabilization.…”

Section: Introductionmentioning

confidence: 99%

Constrained Uncertain System Stabilization with Enlargement of Invariant Sets

2020

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An enhanced method able to perform accurate stability of constrained uncertain systems is presented. The main objective of this method is to compute a sequence of feedback control laws which stabilizes the closed-loop system. The proposed approach is based on robust model predictive control (RMPC) and enhanced maximized sets algorithm (EMSA), which are applied to improve the performance of the closed-loop system and achieve less conservative results. In fact, the proposed approach is split into two parts. The first is a method of enhanced maximized ellipsoidal invariant sets (EMES) based on a semidefinite programming problem. The second is an enhanced maximized polyhedral set (EMPS) which consists of appending new vertices to their convex hull to minimize the distance between each new vertex and the polyhedral set vertices to ensure state constraints. Simulation results on two examples, an uncertain nonisothermal CSTR and an angular positioning system, demonstrate the effectiveness of the proposed methodology when compared to other works related to a similar subject. According to the performance evaluation, we recorded higher feedback gain provided by smallest maximized invariant sets compared to recently studied methods, which shows the best region of stability. Therefore, the proposed algorithm can achieve less conservative results.

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Computationally efficient model predictive control for a class of linear parameter‐varying systems

Cited by 25 publications

References 33 publications

Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios

Linear Parameter-Varying Model Predictive Control of AUV for Docking Scenarios

Robust model predictive kinematic tracking control with terminal region for wheeled robotic systems

Constrained Uncertain System Stabilization with Enlargement of Invariant Sets

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