Constrained predictive control synthesis for quantized systems with Markovian data loss

Zou, Yuanyuan; Lam, James; Niu, Yugang; Li, Dewei

doi:10.1016/j.automatica.2015.03.016

Cited by 96 publications

(46 citation statements)

References 25 publications

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“…Theorem 3. Consider the discrete-time polytopic uncertain system with state saturation nonlinearities described by Equations (1)- (4) under the RR protocol (7). If the optimization problem Op is feasible at the initial time instant k, then it is also feasible for all the future time instants t > k. Furthermore, the closed-loop system (9) is asymptotically stable with the feedback gains determined by (19).…”

Section: Rmpc-based Controller Design Under the Rr Protocolmentioning

confidence: 99%

“…In the past few decades, model predictive control (MPC), known as receding horizon control, has received particular research attention because of its great potential in handling the optimization problems including input, state/output constraints. So far, an MPC strategy has been extensively applied into various industries and a rich body of the results has been reported in the literature . Note that the aforementioned MPC strategies for nominal systems might be invalid for systems with parameter uncertainties.…”

Section: Introductionmentioning

confidence: 99%

“…So far, an MPC strategy has been extensively applied into various industries and a rich body of the results has been reported in the literature. [1][2][3][4][5] Note that the aforementioned MPC strategies [6][7][8][9] for nominal systems might be invalid for systems with parameter uncertainties. It is, therefore, vitally important to develop some new MPC strategies to guarantee the system robustness against the parameter uncertainties, which readily gives rise to the robust MPC (RMPC).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Robust model predictive control for polytopic uncertain systems with state saturation nonlinearities under Round‐Robin protocol

Wang

Song

Wei

et al. 2019

Intl J Robust & Nonlinear

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Summary This paper is concerned with the robust model predictive control (RMPC) problem for polytopic uncertain systems with state saturation nonlinearities under the Round‐Robin (RR) protocol. With respect to the practical application, one of the most commonly encountered obstacles that stem from the physical limitation of system components, ie, state saturation, is adequately taken into consideration. In order to reduce the network transmission burden and improve the utilization of the network from the controller nodes to the actuator node, a so‐called RR protocol is employed to orchestrate the data transmission order. At each transmission instant, only one controller node that obtains the priority is accessible to the shared communication network. Our aim of the underlying problem is to design a set of controllers in the framework of RMPC such that the closed‐loop system is asymptotically stable. By taking the influence of the RR protocol and the state saturation precisely into account, some sufficient criteria are established in terms of the token‐dependent Lyapunov‐like approach. Then, an online optimization problem subjected to some matrix inequality constraints is provided, and the desired controllers can be obtained by solving the certain upper bound of the objective addressed. Finally, a distillation process example is provided to illustrate the effectiveness of the proposed RMPC approach.

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Section: Rmpc-based Controller Design Under the Rr Protocolmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robust model predictive control for polytopic uncertain systems with state saturation nonlinearities under Round‐Robin protocol

Wang

Song

Wei

et al. 2019

Intl J Robust & Nonlinear

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“…Proof. In order to establish string stability of the vehicle platoon, substituting (8) into (3) we obtain that…”

Section: Predictive Control For Vehicle Platoonmentioning

confidence: 99%

Delayed Feedback MPC Algorithms of Vehicle Platoons Subject to Constraints on Measurement Range and Driving Behaviors

Zhao

et al. 2018

Asian Journal of Control

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The control problem of vehicle platoons considering sensors with limited measurement range and actuator time delay is investigated in the face of constraints. A new delayed feedback model predictive control scheme is proposed to solve the problem while satisfying the constraints on measurement range and driving behaviors. A family of controllers is presented with free parameters which are then computed by online solving of a receding horizon optimal control problem. Terminal equality constraints are adopted to guarantee stability of the closed-loop system. Some sufficient conditions with guaranteed string stability of the platoon and zero steady-state error are established. The effectiveness and advantages of the presented method are demonstrated by simulating two classical road scenarios.

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“…Over the past few decades, due to its practical insights, the model predictive control (MPC) problem has attracted considerable research interest in systems with bounded disturbances, which gives rise to robust MPC (RMPC), see [4], [10], [17], [35] for discrete-time systems and [21], [26]- [28] for continuous-time systems. Up to date, RMPC strategy has been extensively applied into various engineering areas such as chemical process, power systems, DC motors and mobile robots [8], [10]- [14], [31], [36]. From the technical viewpoint, there are generally two approaches dealing with the external bounded disturbances for discrete-time systems with respect to the RMPC problem, namely, the "min-max" optimization approach [24] and the open-loop optimization approach [3], [15].…”

Section: Introductionmentioning

confidence: 99%

N-Step MPC for Systems With Persistent Bounded Disturbances Under SCP

Song

Wang

Liu

et al. 2020

IEEE Trans. Syst. Man Cybern, Syst.

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This paper is concerned with the N -step model predictive control (MPC) problem for a class of constrained systems with persistent bounded disturbances under the stochastic communication protocol (SCP). The control signals are transmitted to the plant via a shared network subject to a prescribed SCP for the purpose of avoiding data collisions. The SCP scheduling, which is governed by a Markov chain, is applied to orchestrate the transmission order of the controller nodes. Under the SCP, only one control node is allowed to update the control signal sent to the plant at each communication instant. Our aim is to design a set of desired controllers in the framework of N -step MPC such that the mean-square input-to-state stability of the closedloop system is guaranteed. An optimization algorithm consisting of both off-line and online parts is developed to cope with the design problem of the N -step controller. Finally, a numerical example is utilized to illustrate the validity of the proposed Nstep MPC strategy.Index Terms-N -step model predictive control, stochastic communication protocol, mean-square input-to-state stability, persistent bounded disturbances.Yan Song received the B.Sc. degree in materials science and engineering

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Constrained predictive control synthesis for quantized systems with Markovian data loss

Cited by 96 publications

References 25 publications

Robust model predictive control for polytopic uncertain systems with state saturation nonlinearities under Round‐Robin protocol

Robust model predictive control for polytopic uncertain systems with state saturation nonlinearities under Round‐Robin protocol

Delayed Feedback MPC Algorithms of Vehicle Platoons Subject to Constraints on Measurement Range and Driving Behaviors

N-Step MPC for Systems With Persistent Bounded Disturbances Under SCP

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