Soft Constrained Model Predictive Control With Robust Stability Guarantees

Zeilinger, Melanie N.; Morari, Manfred; Jones, Colin N.

doi:10.1109/tac.2014.2304371

Cited by 108 publications

(68 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In [25], MPC with mixed hard and soft constraints is considered and stability is proven by properly extending the stability proof in normal MPC. In [26], a soft constrained MPC approach with guaranteed stability is proposed by enlarging the terminal constraint and relaxing the state constraints. The approach can ensure the feasibility in a large region, and the optimal performance is preserved whenever all state constraints can be enforced.…”

Section: Discussion On Optimality and Stabilitymentioning

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

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).

show abstract

Section: Discussion On Optimality and Stabilitymentioning

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

“…Define an ellipsoid .P i ; 2 i / D ®´i 2 R n´W´T i P i´i 6 2 i¯, where P i 2 R n´ n´i s a positive definite matrix and i 2 R is a positive scalar. The purpose of this paper is to design the distributed OFMPC controller described by (8) for subsystem (3)- (6). More specifically, we are interested in determining the ellipsoid parameters P i and 2 i such that the zero-solution of the system (3)-(6) under the control law (8) is locally stable in a mean-square sense, and the upper bound of the objective function could be obtained by solving the given optimization problem.…”

Section: Assumptionmentioning

confidence: 99%

“…Consider system (3) controlled by (6). If conditions (35), (36) and (38) are satisfied and there are symmetric and definitely positive matrices W i;3 and W i;4 such that…”

Section: Lemmamentioning

confidence: 99%

“…Due to its ability on handling several process models with the inevitable parameter uncertainties and external disturbances as well as many performance criteria of significance to the process industries, robust MPC (RMPC) has been widely applied in the petrochemical and related industries [3,4]. In most existing literature, the system states are assumed to be measurable online, thus the achieved RMPC algorithms are usually based on state feedback control (e.g., [5,6]). However, system states are often unmeasurable in practice, thus it is necessary to pay more attention to output feedback control.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Distributed output feedback MPC with randomly occurring actuator saturation and packet loss

Song

Wei

Shuai

2015

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

In this paper, a distributed output feedback model predictive control (OFMPC) algorithm is presented for the polytopic uncertain system subject to randomly occurring actuator saturation and packet loss. Compared with the intensively applied state feedback control in MPC, the OFMPC is more feasible to the real world because the system states are often unmeasurable. With taking both actuator saturation and packet loss into account, the presented OFMPC algorithm is more practical. Moreover, by splitting the controller inputs into two independent parts, the presented dynamic output feedback control (DOFC) strategy provides more freedom to the controller design. With the global system decomposed into some subsystems, the computation complexity is reduced, thus the online designing time can be saved. By defining the estimation error function and forming an augmented system to handle the DOFC and by transforming the nonlinear feedback law into a convex hull of linear feedback laws, the distributed controllers are obtained by solving a linear matrix inequality (LMI) optimization problem. Finally, some simulation examples are employed to show the effectiveness of the techniques proposed in this paper. 3037Note that the results by OFMPC strategy provided earlier are mostly on centralized MPC. However, with the rapid development of the science technology and the network application, the systems to be handled are becoming more and more complex. It is often infeasible to apply the classical centralized MPC control solution to the practical systems accounting for the computational complexity and communication bandwidth limitations. Thus, a distributed MPC strategy has attracted much attention to reduce the computation time and the network transmission burden recently [11,12]. In a distributed MPC framework, each subsystem is controlled by an independent controller, all subsystems communicate with each other via network, and the technical target is to achieve the global performance of entire system. In this way, the system with distributed MPC has better error-tolerance and robustness compared with the traditional centralized MPC. Mercangöz et al. proposed an iterative implementation of a distributed MPC scheme and applied it to a four-tank system [13]. In [14], a class of linear systems coupled with neighbors through inputs was discussed and the corresponding distributed MPC algorithms were presented for the cooperative subsystems. A robust distributed MPC problem was discussed for a system with polytopic uncertainty in [15] and [16], where the model uncertain problems were casted into an LMI optimization problem.In [17], Zhang et al. presented a distributed MPC algorithm for the polytopic system subject to inputs saturation.On the other hand, the obstacles in delivering are often owing to the physical limitations of system components, of which the most commonly encountered one stems from the saturation that occurs in actuators. Saturation brings in nonlinear characteristics that can severely restrict the amount of transmitted...

show abstract

“…About the robust stability guarantees, the article [5] presented a new soft constrained model predictive control approach for tracking that provides stability guarantees even for unstable systems. The research of article [6] exploited the cubature rule in the unknown input observer structure to overcome nonlinear calculations in the presence of external disturbances for sensor fault detection purposes.…”

Section: Introductionmentioning

confidence: 99%

Opportunistic Cooperative Sensing for Indoor Complex Environment Monitoring

Jin¹,

Qian²,

Xing³

et al. 2017

Int. J. Onl. Eng.

View full text Add to dashboard Cite

Abstract-Sensor failure have become a major bottleneck, which restricted the wide application of wireless sensor networks WSNs (Wireless Sensor Networks). In order to satisfy the needs of industrial production and daily living environment monitoring, it is important to improve the survivability of wireless sensor networks. This paper analyzed the damage types and causes of WSNs and the measurement methods of WSNs survivability. Then, based on the whole system model and sensor node circuit, we studied the fault detection method, which can improve the survivability of the scheme. Additionally, the robust guarantee scheme through hardware design and algorithm model was proposed. The performance results shown that the proposed scheme can realize the remote wireless communication services and prolong the network life cycle, so as to improve the survivability of WSNs. Through the combination of theoretical research and practical analysis system, the network survivability would be improved through soft and hard optimization combination strategy.Keywords-Wireless sensor networks; invulnerability; indoor Complex environment monitoring; hardware design; robust guarantee IntroductionThe environmental monitoring system [1, 2] based on WSNs (Wireless Sensor Networks) is used to reduce the cost of the entire system, mostly using mass low-cost sensor nodes [3] for networking. Invulnerability of WSNs is the ability measurement metric of WSNs, which could be sustainable and stable to provide reliable services for the entire network system. In the case of random or deterministic failures [4] in the network communication, the system is able to complete the key services.About the robust stability guarantees, the article [5] presented a new soft constrained model predictive control approach for tracking that provides stability guarantees even for unstable systems. The research of article [6] exploited the cubature rule in the unknown input observer structure to overcome nonlinear calculations in the presence of external disturbances for sensor fault detection purposes. In article [7], it is demonstrated that, under mild assumptions, the distributional robust optimization problems over Wasserstein balls can in fact be reformulated as finite convex programs---in many interesting cases even as tractable linear programs. A robust adaptive beamforming scheme based on two-component electromagnetic vector-sensor 4

show abstract

Soft Constrained Model Predictive Control With Robust Stability Guarantees

Cited by 108 publications

References 27 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

Distributed output feedback MPC with randomly occurring actuator saturation and packet loss

Opportunistic Cooperative Sensing for Indoor Complex Environment Monitoring

Contact Info

Product

Resources

About