Stability Constraints for Robust Model Predictive Control

Ottoni, Amanda G. S.; Takahashi, Ricardo H. C.; Raffo, Guilherme V.

doi:10.1155/2015/870189

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…The Lyapunov function guarantees the quadratic stability and the cost for the closed-loop system. An approach has been proposed in Ottoni et al (2015) for the robust stabilisation of systems with parametric uncertainty. Based on MPC strategies, constraints have been delivered on the control inputs.…”

Section: Introductionmentioning

confidence: 99%

Synthesise of MPC controller for uncertain systems subject to input and output constraints: application to anthropomorphic robot arm

Dakhli

Maherzi

Besbes

2020

IJAAC

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This paper proposes a synthesis of a dynamic controller under constraints. It is based on model predictive control (MPC) approach and resolution of a convex optimisation problem with linear matrix inequalities (LMI). The controller guarantees the closed-loop stability for polytopic time-varying uncertain systems. Conditions are provided for the controller design based on the parameter dependent Lyapunov functions (PDLF). A new demonstration is developed based on the relaxation technique, to include a slack variables G i . The new LMI's formulation offers an additional degree of freedom for the controller design. Input and output constraints are also taken into account during the design of the controller. This approach allows varying and adjusting the dynamic of system by taking into account input/output constraints.

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Section: Introductionmentioning

confidence: 99%

Synthesise of MPC controller for uncertain systems subject to input and output constraints: application to anthropomorphic robot arm

Dakhli

Maherzi

Besbes

2020

IJAAC

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“…Model predictive control (MPC), also known as receding horizon control (RHC), has received much attention in control societies for its ability to simultaneously handle constraints and time-varying behaviors as well as to well track a reference (see [1][2][3]). The basic concept of MPC is to solve an optimization problem over future time instants at the current time and to use the first one among the solutions as the current control input.…”

Section: Introductionmentioning

confidence: 99%

Model Predictive Control Algorithm Based on Off-Line Region Dependency

Kim

2016

Mathematical Problems in Engineering

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This paper presents an efficient MPC algorithm for uncertain time-varying systems with input constraints. The main advantage of this algorithm with respect to other published algorithms is to significantly enlarge the size of the stabilization set without regard to computational burdens. Specially, we introduce an off-line region-dependent MPC scheme to avoid the size limitation of the control horizon caused by huge on-line computational burdens. A numerical example is included to illustrate the validity of the result.

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Improved nonlinear model predictive control with inequality constraints using particle filtering for nonlinear and highly coupled dynamical systems

Ahsan,

Salah

2024

Nonlinear Engineering

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Motion planning and controller design are challenging tasks for highly coupled and nonlinear dynamical systems such as autonomous vehicles and robotic applications. Nonlinear model predictive control (NMPC) is an emerging technique in which sampling-based methods are used to synthesize the control and trajectories for complex systems. In this study, we have developed the sampling-based motion planning algorithm with NMPC through Bayesian estimation to solve the online nonlinear constrained optimization problem. In the literature, different filtration techniques have been applied to extract knowledge of states in the presence of noise. Due to the detrimental effects of linearization, the Kalman filter with NMPC only achieves modest effectiveness. Moving horizon estimation (MHE), on the other hand, frequently relies on simplifying assumptions and lacks an effective recursive construction. Additionally, it adds another optimization challenge to the regulation problem that has to be solved online. To address this problem, particle filtering is implemented for Bayesian filtering in nonlinear and highly coupled dynamical systems. It is a sequential Monte Carlo method that involves representing the posterior distribution of the state of the system using a set of weighted particles that are propagated through time using a recursive algorithm. For nonlinear and strongly coupled dynamical systems, the novel sampling-based NMPC technique is effective and simple to use. The efficiency of the suggested method has been assessed using simulated studies.

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Stability Constraints for Robust Model Predictive Control

Cited by 3 publications

References 38 publications

Synthesise of MPC controller for uncertain systems subject to input and output constraints: application to anthropomorphic robot arm

Synthesise of MPC controller for uncertain systems subject to input and output constraints: application to anthropomorphic robot arm

Model Predictive Control Algorithm Based on Off-Line Region Dependency

Improved nonlinear model predictive control with inequality constraints using particle filtering for nonlinear and highly coupled dynamical systems

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