A data driven subspace approach to predictive controller design

Kadali, Ramesh; Huang, Biao; Rossiter, Anthony

doi:10.1016/s1474-6670(17)33851-x

Cited by 27 publications

(52 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This subspace predictor is subsequently integrated into a predictive control objective function. The basic SPC algorithm was introduced by [30] and has since been used by various researchers [91,49,88]. If the subspace predictor is updated on-line with new input-output data when it becomes available, then SPC has the ability to adapt to changing system conditions, which can also include unanticipated faults.…”

Section: Model Predictive Controlmentioning

confidence: 99%

Fault Tolerant Flight Control - A Survey

Verhaegen

Kanev

Hallouzi³

et al. 2010

Lecture Notes in Control and Information Sciences

View full text Add to dashboard Cite

Section: Model Predictive Controlmentioning

confidence: 99%

Fault Tolerant Flight Control - A Survey

Verhaegen

Kanev

Hallouzi³

et al. 2010

Lecture Notes in Control and Information Sciences

View full text Add to dashboard Cite

“…There are abundant online and offline measurement data in the HEV operation process, which provide the possibility for the realization of the data-driven modeling and control for the HEV mode transition dynamic process. As one of the efficient data-driven control methods, the data-driven predictive control (DDPC) algorithm was proposed in recent years, which combines the modeling superiority of the data-driven and the multiple-constraint handling capability of the predictive control [16]. It was firstly proposed in [17].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Predictive Torque Coordination Control during Mode Transition Process of Hybrid Electric Vehicles

2017

View full text Add to dashboard Cite

Torque coordination control significantly affects the mode transition quality during the mode transition dynamic process of hybrid electric vehicles (HEV). Most of the existing torque coordination control methods are based on the mechanism model, whose control effect heavily depends on the modeling accuracy of the HEV powertrain. However, the powertrain structure is so complex, that it is difficult to establish its precise mechanism model. In this paper, a torque coordination control strategy using the data-driven predictive control (DDPC) technique is proposed to overcome the shortcomings of mechanism model-based control methods for a clutch-enabled HEV. The proposed control strategy is only based on the measured input-output data in the HEV powertrain, and no mechanism model is needed. The conflicting control requirements of comfortability and economy are included in the cost function. The actual physical constraints of actuators are also explicitly taken into account in the solving process of the data-driven predictive controller. The co-simulation results in Cruise and Simulink validate the effectiveness of the proposed control strategy and demonstrate that the DDPC method can achieve less vehicle jerk, faster mode transition and smaller clutch frictional losses compared with the traditional model predictive control (MPC) method.

show abstract

“…In the last 20 years of twentieth century, there have been a variety of predictive control algorithm applied to the actual industrial process predictive control has many advantages, can easily handle the equipment and security constraints and deal with multi-variables and nonlinear system, it can easily handle large lag and non-minimum phase system; has become a main research direction in the field of control [3][4].…”

Section: Introductionmentioning

confidence: 99%

Design of Adaptive Subspace Predictive Controller for Stable Operation

Li¹,

Qiu²,

Ye³

2017

dteees

View full text Add to dashboard Cite

Abstract. For a class of process which had characteristics of nonlinear time-varying, dynamic characteristic varing significantly and can not be accurately modeled mechanism, this paper proposed a adaptive subspace predictive controller for stable operation. The method compared the prediction error to update prediction model online, and adjuste the length of the scroll window automatically according to the feedback error, which enhanced the adaptability for nonlinear time-varying characteristics, and strengthened its ability to inhibit unpredictable interference. Finally, simulated to verify the effectiveness of the method.

show abstract

A data driven subspace approach to predictive controller design

Cited by 27 publications

References 8 publications

Fault Tolerant Flight Control - A Survey

Fault Tolerant Flight Control - A Survey

Data-Driven Predictive Torque Coordination Control during Mode Transition Process of Hybrid Electric Vehicles

Design of Adaptive Subspace Predictive Controller for Stable Operation

Contact Info

Product

Resources

About