The synthesis of robust model predictive control with QP formulation

Li, Dewei; Xi, Yugeng

doi:10.1504/ijmic.2011.040484

Cited by 14 publications

(4 citation statements)

References 16 publications

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“…In this section, we use the Laguerre orthonormal bases to reduce the parametric complexity of the MIMO ARX multiple models defined by (9) [15,18,20]. is choice is due to the capability of Laguerre base on parametric reduction and for the classical recurrent representation.…”

Section: Expansion Of Mimo Arx Multiple Models On Laguerre Orthonormamentioning

confidence: 99%

“…e goal of this work is to synthesize fault tolerant control for nonlinear multiple-input multiple-output (MIMO) systems via model predictive control (MPC) based on reduced complexity multiple models. Model-based predictive control is a well-established online control strategy which iteratively computes control signals by solving an optimization problem over a future time horizon under certain process constraints [6][7][8][9]. is optimization uses a prediction model of the future plant behavior.…”

Section: Introductionmentioning

confidence: 99%

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Fault Tolerant Control for MIMO Nonlinear Systems via MPC Based on MIMO ARX‐Laguerre Multiple Models

Mbarek

Bouzrara

2019

Mathematical Problems in Engineering

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In this article, we propose a fault tolerant control for multiple-input multiple-output (MIMO) nonlinear systems via model predictive control. The MIMO nonlinear systems are approximated by MIMO ARX-Laguerre multiple models. The latter is obtained by expanding a discrete-time MIMO ARX multiple model parameters on Laguerre orthonormal bases. The resulting model ensures an efficient complexity reduction with respect to the classical MIMO ARX multiple models. This parametric complexity reduction still subjects to an optimal choice of the Laguerre poles defining Laguerre bases. The parameter and structure identifications of the MIMO ARX-Laguerre multiple models are achieved by the recursive method and a metaheuristic algorithm, respectively. The proposed model is built from the system input/output observations and is used to synthesize a MIMO nonlinear fault tolerant control algorithm via MPC. So, we develop a fault detection and isolation (FDI) scheme based on the proposed model. The scheme of the fault detection is applied at every step of MPC control calculation, where we determine the actuator faults and we use it in the MPC optimization problem to determine the new control with respect to the actuator faults. The proposed strategy is tested on numerical simulation and validated on the real system.

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Section: Expansion Of Mimo Arx Multiple Models On Laguerre Orthonormamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fault Tolerant Control for MIMO Nonlinear Systems via MPC Based on MIMO ARX‐Laguerre Multiple Models

Mbarek

Bouzrara

2019

Mathematical Problems in Engineering

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“…An MPC control scheme is commonly used when the tracking of a reference trajectory is the primary goal and it makes possible to constrain the control signal [28][29][30] within the allowed range of the system. Since MPC is designed according to the linear PTS model, the generation of the control action is less complicated than for nonlinear MPC.…”

Section: Fig 191 Schematic View Of An Afmmentioning

confidence: 99%

Advanced Control of Atomic Force Microscope for Faster Image Scanning

Rana

Pota

Petersen

2013

Applied Methods and Techniques for Mechatronic Systems

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In atomic force microscopy (AFM), the dynamics and nonlinearities of its nanopositioning stage are major sources of image distortion, especially when imaging at high scanning speed. This chapter discusses the design and experimental implementation of an observer-based model predictive control (OMPC) scheme which aims to compensate for the effects of creep, hysteresis, cross-coupling, and vibration in piezoactuators in order to improve the nanopositioning of an AFM. The controller design is based on an identified model of the piezoelectric tube scanner (PTS) for which the control scheme achieves significant compensation of its creep, hysteresis, cross-coupling, and vibration effects and ensures better tracking of the reference signal. A Kalman filter is used to obtain full-state information about the plant. The experimental results illustrate the use of this proposed control scheme.

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“…Its conclusion part is expressed by linear equations to construct linear combinations of each rule, so that the output of a nonlinear system has better linear characterization and can approach to a random non-linear system with arbitrary precision (Johansen and Babuska 2003). T-S fuzzy model is used to establish the predictive model of a locomotive brake control system and this can improve model verity and control accuracy (Baocang 2011;Li and Xi 2011). Genetic algorithm (GA) is proposed by Holland, which is a parallel global optimization algorithm.…”

Section: Brake Control System Configurationmentioning

confidence: 99%

Modelling and control design for an electro-pneumatic braking system in trains with multiple locomotives

Liu

Liu²,

Huang

et al. 2012

IJMIC

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This paper focuses on the modelling and control design for an electro-pneumatic braking system used in multi-locomotives in order to achieve accurately and steadily braking control of heavy haul trains. To deal with various time delays, a T-S fuzzy model based on satisfaction degree is proposed to simplify the cylinder model construction and a fuzzy clustering algorithm with forgetting factors is deployed to achieve parameter selflearning in order to improve the fuzzy control accuracy. Then a fuzzy genetic algorithm is adopted as the rolling optimization method to reduce the effect of coupling noise, system disturbance and communication random interference such that the system robustness and controller response capability are improved. The effectiveness of the proposed method is verified by simulation and practical implementations.

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The synthesis of robust model predictive control with QP formulation

Cited by 14 publications

References 16 publications

Fault Tolerant Control for MIMO Nonlinear Systems via MPC Based on MIMO ARX‐Laguerre Multiple Models

Fault Tolerant Control for MIMO Nonlinear Systems via MPC Based on MIMO ARX‐Laguerre Multiple Models

Advanced Control of Atomic Force Microscope for Faster Image Scanning

Modelling and control design for an electro-pneumatic braking system in trains with multiple locomotives

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