Matrix Inequalities Based Robust Model Predictive Control for Vehicle Considering Model Uncertainties, External Disturbances, and Time-Varying Delay

Liu, Wenjun; Chen, Guang; Knoll, Alois

doi:10.3389/fnbot.2020.617293

Cited by 10 publications

(12 citation statements)

References 34 publications

(25 reference statements)

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“…then, the MPC problem can be converted to the standard multi-parameter quadratic programming form of equation (9), where…”

Section: Design Of Empc Controllermentioning

confidence: 99%

“…Thus the Theorem 1 is proved. Note3: Since the MPC control problem in (7)(8) is not a standard mpQP problem, and due to the particularity of turboshaft engine-rotor system, the influence of disturbance input (rotor system input) on the derivation process has to be considered in the conversion of problem (7) (8) to problem (9). Therefore, we need to first convert the MPC problem of turboshaft engine-rotor system into a standard mpQP problem, which is the main role of Theorem 1.…”

Section: Design Of Empc Controllermentioning

confidence: 99%

“…) is a set of initial feasible solutions of optimization problem (9). Theorem 2: Let F l be the first m rows of matrix −(M 0 ) −1 N 0 , G l be the first m rows of matrix (M 0 ) −1 N 0 Z 10 + Z 0 , and m is the number of rows of Z , then the EMPC controller of the turboshaft engine-rotor system is continuous, and its explicit expression on any partition is as follows:…”

Section: Design Of Empc Controllermentioning

confidence: 99%

“…Meanwhile, MPC has also been used in large machineries, such as wind power generator [7] and heavy duty diesel engine [8]. [9], [10] give a detailed derivation process on the improvement and robustness of the MPC algorithm from a theoretical point of view. In recent years, the application research of MPC in Aeroengine has also made a great breakthrough.…”

Section: Introductionmentioning

confidence: 99%

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Explicit Model Predictive Based Fault Tolerant Control for Turboshaft Engine System

Wang

Yang

2022

IEEE Access

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In order to tackle the faults of turboshaft engine-rotor systems and the real time implementation difficulty of traditional model predictive control methods, an explicit model predictive (EMPC) fault-tolerant control algorithm is designed based on an active fault-tolerant control scheme that implicitly detects the faults and adjusts the control law online. The proposed real time control algorithm can achieve good control command tracking performance and, at the same time, guarantee limit protection of turboshaft enginerotor system. Firstly, to start with the algorithm, a dynamic system model library and a fault monitoring mechanism are established, in which the dynamic system model library contains sets of piecewise affine models (PWA) of normal engine mode and modes with faults. Secondly, the EMPC fault-tolerant controller is derived and designed for each sub-model in the modes of dynamic model library. Through the transformation and derivation of the traditional model predictive controller of turboshaft engine-rotor system, the explicit solution of its fault-tolerant controller is obtained. With the explicit solution which is in the form of state feedback control for each partition, controller gains can be designed off-line. Finally, the online engine control process is completed by searching the corresponding controller gain based on engine state, which improves the real-time performance of the control system. The effectiveness of the method for engine systems with modeled or un-modeled compressor faults and the robustness of the algorithm are verified by simulations.

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“…then, the MPC problem can be converted to the standard multi-parameter quadratic programming form of equation (9), where…”

Section: Design Of Empc Controllermentioning

confidence: 99%

Section: Design Of Empc Controllermentioning

confidence: 99%

Section: Design Of Empc Controllermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Explicit Model Predictive Based Fault Tolerant Control for Turboshaft Engine System

Wang

Yang

2022

IEEE Access

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“…Liu et al designed a robust model predictive control (RMPC) controller for vehicles subjected to bounded model uncertainties, norm-bounded external disturbances and bounded timevarying delay. Simulation results showed that the proposed vehicle dynamic controller can steer vehicle states into a very small region near the reference tracking signal even in the presence of external disturbances, model uncertainties and time-varying delay [17]. Samuel et al used model predictive control and proportional integral derivative control for lane keeping maneuvers of an autonomous vehicle.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Lane Tracking Control of the Commercial Vehicle Based on RMPC Algorithm Considering the State of Preceding Vehicle

Tang

Jiang

et al. 2022

Machines

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In order to improve the adaptability of the lane keeping control system to complex environments, a dynamic lane tracking control strategy of the commercial vehicle based on the robust model predictive control (RMPC) algorithm is proposed considering the state of the preceding vehicle. An RMPC controller is designed with path deviation and control increment as the objective function. The model predictive control problem is transformed into a min–max optimization problem. The linear matrix inequality (LMI) is used for the optimal solution to obtain the optimal control quantity. The strategy to improve the safety and comfort dynamically in the process of lane keeping is designed by adjusting the weight coefficient matrix of RMPC based on fuzzy theory. The results of the simulation and HiL test show that the RMPC controller can meet the requirement of adjusting the lane tracking process dynamically according to the state of the preceding vehicle, which keeps the balance between safety and comfort.

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Robust fuzzy model predictive control for nonlinear discrete‐time systems

Mahmoudabadi,

Naderi Akhormeh

2023

Adaptive Control & Signal

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SummaryThis paper studies the issue of Robust Fuzzy Model Predictive Control (RFMPC) of nonlinear discrete‐time systems in the presence of disturbance. To this end, Takagi‐Sugeno (T‐S) fuzzy model is adopted in order to characterize uncertain nonlinear systems and facilitate providing controller for such systems. Non‐parallel distributed compensation (non‐PDC) technique is applied to design improved RFPMC with better performance. The optimization problem of RFMPC is represented in terms of linear matrix inequalities (LMIs) so as to decrease online computational burden. Besides, feasibility and stability which are decisive factors in MPC theory are investigated for the proposed method. Ultimately, two examples including cart‐damper‐spring system are simulated to evaluate the results of this research work.

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Matrix Inequalities Based Robust Model Predictive Control for Vehicle Considering Model Uncertainties, External Disturbances, and Time-Varying Delay

Cited by 10 publications

References 34 publications

Explicit Model Predictive Based Fault Tolerant Control for Turboshaft Engine System

Explicit Model Predictive Based Fault Tolerant Control for Turboshaft Engine System

Dynamic Lane Tracking Control of the Commercial Vehicle Based on RMPC Algorithm Considering the State of Preceding Vehicle

Robust fuzzy model predictive control for nonlinear discrete‐time systems

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