A stable block model predictive control with variable implementation horizon

Sun, Jing; Kolmanovsky, Ilya; Ghaemi, Reza; Chen, Shuhao

doi:10.1016/j.automatica.2007.03.026

Cited by 26 publications

(14 citation statements)

References 11 publications

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“…Two examples are the parameterization method [13] and the "move blocking (MB)" method [14]- [16]. The former often assumes a priori knowledge of the control law, which can be approximated by a parameterized function (with less unknowns to reduce the order of the original problem), e.g., polynomial [13].…”

Section: Introductionmentioning

confidence: 99%

Fast Online Computation of a Model Predictive Controller and Its Application to Fuel Economy–Oriented Adaptive Cruise Control

Jia

et al. 2015

IEEE Trans. Intell. Transport. Syst.

114

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The recent progress of advanced vehicle control systems presents a great opportunity for the application of model predictive control (MPC) in the automotive industry. However, high computational complexity inherently associated with the receding horizon optimization must be addressed to achieve real-time implementation. This paper presents a generic scale reduction framework to reduce the online computational burden of MPC controllers. A lower dimensional MPC algorithm is formulated by combining an existing "move blocking " strategy with a "constraint-set compression" strategy, which is proposed to further reduce the problem scale by partially relaxing inequality constraints in the prediction horizon. The closed-loop stability is guaranteed by adding terminal zero-state constraint. The tradeoff between control optimality and computational intensity is achieved by proper design of the blocking and compression matrices. The fast algorithm has been applied on intelligent vehicular longitudinal automation, implemented as a fuel economy-oriented adaptive cruise controller and experimentally evaluated by a series of real-time simulations and field tests. These results indicate that the proposed method significantly improves the computational speed while maintaining satisfactory control optimality without sacrificing the desired performance.Index Terms-Adaptive cruise control (ACC), computation efficiency, fuel economy, model predictive control (MPC).

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Section: Introductionmentioning

confidence: 99%

Fast Online Computation of a Model Predictive Controller and Its Application to Fuel Economy–Oriented Adaptive Cruise Control

Jia

et al. 2015

IEEE Trans. Intell. Transport. Syst.

114

View full text Add to dashboard Cite

show abstract

“…In controls, one may use a multiplexer for the same reasons as in electronics [for instance, to save on the number of signal-conditioning channels and data converters (hardware multiplexing)] or the multiplexer may be implemented in software, becoming an integral part of the control law (software multiplexing). Multiplexed control has received recent attention in the context of MPC [16][17][18], typically restricting the number of actuators being updated at every sampling instant to one. In the following sections, we use a nominal linear model to discuss the theoretical implications of introducing multiplexing in conjunction with conventional linear quadratic control.…”

Section: Overview Of Multiplexed Controlmentioning

confidence: 99%

“…Currently, due to rapid progress in developing fast and cheap computing power, MPC is being considered as a serious candidate for faster processes such as mechanical systems and aircraft engine controls. Theoretical MPC research in the last five years seems directed to complexity reduction by piecewise implementation [15], multiplexed schemes [16][17][18], and to hybrid approaches that can optimize over logical or integer variables. For a survey containing industrial applications, see [19].…”

Section: B Overview Of Model-predictive Controlmentioning

confidence: 99%

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Multiplexed Predictive Control of a Large Commercial Turbofan Engine

Richter

Singaraju

Litt

2008

Journal of Guidance, Control, and Dynamics

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Model predictive control is a strategy well-suited to handle the highly complex, nonlinear, uncertain, and constrained dynamics involved in aircraft engine control problems. However, it has thus far been infeasible to implement model predictive control in engine control applications, because of the combination of model complexity and the time allotted for the control update calculation. In this paper, a multiplexed implementation is proposed that dramatically reduces the computational burden of the quadratic programming optimization that must be solved online as part of the model-predictive-control algorithm. Actuator updates are calculated sequentially and cyclically in a multiplexed implementation, as opposed to the simultaneous optimization taking place in conventional model predictive control. Theoretical aspects are discussed based on a nominal model, and actual computational savings are demonstrated using a realistic commercial engine model.

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“…Xie [9] presented the first state contractive NMPC algorithm, in which the contractive constraints are enforced on the one-step ahead predicted state. Sun et al [10] presented another contractive NMPC algorithm, which adopts a time-varying implementation horizon confirmed by solving an appropriate optimization problem.…”

Section: Introductionmentioning

confidence: 99%

A Contractive Sliding- mode MPC Algorithm for Nonlinear Discrete- time Systems

Zhao

Ding

2013

Int. J. Autom. Comput.

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This paper investigates a sliding-mode model predictive control (MPC) algorithm with auxiliary contractive sliding vector constraint for constrained nonlinear discrete-time systems. By adding contractive constraint into the optimization problem in regular sliding-mode MPC algorithm, the value of the sliding vector is decreased to zero asymptotically, which means that the system state is driven into a vicinity of sliding surface with a certain width. Then, the system state moves along the sliding surface to the equilibrium point within the vicinity. By applying the proposed algorithm, the stability of the closed-loop system is guaranteed. A numerical example of a continuous stirred tank reactor (CSTR) system is given to verify the feasibility and effectiveness of the proposed method.

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A stable block model predictive control with variable implementation horizon

Cited by 26 publications

References 11 publications

Fast Online Computation of a Model Predictive Controller and Its Application to Fuel Economy–Oriented Adaptive Cruise Control

Fast Online Computation of a Model Predictive Controller and Its Application to Fuel Economy–Oriented Adaptive Cruise Control

Multiplexed Predictive Control of a Large Commercial Turbofan Engine

A Contractive Sliding- mode MPC Algorithm for Nonlinear Discrete- time Systems

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