2015
DOI: 10.1016/j.automatica.2015.03.016
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Constrained predictive control synthesis for quantized systems with Markovian data loss

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Cited by 97 publications
(46 citation statements)
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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%
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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%
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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%
“…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%