Robust variable horizon MPC with move blocking

Shekhar, Rohan C.; Maciejowski, Jan M.

doi:10.1016/j.sysconle.2012.02.004

Cited by 44 publications

(19 citation statements)

References 10 publications

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“…For small problems, it may be possible to simply calculate the volume approximation over all possible blocking structures for a given N and r, choosing the structure giving the largest approximate ROA volume. There are however  N − 1 r − 1  possible ways of blocking an Nstep horizon into r blocks (Shekhar & Maciejowski, 2012b), which grows rapidly with N.…”

Section: Theorem 7 a Blocking Structure To Subdivide N Inputs Into Rmentioning

confidence: 99%

“…Let a feasible solution to P ′ (N,r) be given by the tuple (P, c, Q , d,δ N ). Then, a corresponding feasible solution to P ′ (N, r) is given by the tuple (P, c, Q , d, δ N ), where δ N =δ N ∨ γ , for any γ ∈ {0, 1} N such that ∥δ N ∥ 1 = r. This is evident from the fact that a more restrictive blocking structure withr blocks can always be relaxed (Shekhar & Maciejowski, 2012b) to a less restrictive structure with r >r blocks, whilst still admitting Q and d as part of the feasible solution. In other words, reducing the number of blocks always introduces more restrictive equality constraints on (41b)-(41c).…”

Section: Theorem 7 a Blocking Structure To Subdivide N Inputs Into Rmentioning

confidence: 99%

“…However, this prevents a terminal constraint from being enforced, so explicit guarantees of closed-loop convergence cannot be given. Cagienard et al (2007) and Shekhar and Maciejowski (2012b) present approaches that utilise timevarying blocking structures, where the changing structure allows a shifted version of the previously optimal input sequence to be feasible at the following time step. Guarantees of both recursive feasibility and closed-loop convergence can then be provided, with an appropriately designed terminal constraint and cost function.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimal move blocking strategies for model predictive control

Shekhar

Manzie

2015

Automatica

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Section: Theorem 7 a Blocking Structure To Subdivide N Inputs Into Rmentioning

confidence: 99%

Section: Theorem 7 a Blocking Structure To Subdivide N Inputs Into Rmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal move blocking strategies for model predictive control

Shekhar

Manzie

2015

Automatica

Self Cite

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“…The so-called Variable-Horizon model predictive control (VH-MPC) which varies the horizon length according to a constrained optimization problem is well-suited to handle time-varying disturbances. The VH-MPC algorithm drives the system state to a closed set in finite time irrespective of bounded disturbances (Michalska & Mayne, 1993;Richards & How, 2006;Scokaert & Mayne, 1998;Shekhar & Maciejowski, 2012). It is possible to reduce the complexity of the algorithm by setting the variable horizon to predefinite values according to the onset of a disturbance.…”

Section: Introductionmentioning

confidence: 99%

Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

Dieulot

Colas

Chalal

et al. 2015

Control Engineering Practice

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Hardware in the loop Variable receding horizon a b s t r a c tThe supervision of a hybrid power plant, including solar panels, a gas microturbine and a storage unit operating under varying solar power profiles is considered. The Economic Supervisory Predictive controller assigns the power references to the controlled subsystems of the hybrid cell using a financial criterion. A prediction of the renewable sources power is embedded into the supervisor. Results deteriorate when the solar power is unsteady, owing to the inaccuracy of the predictions for a long-range horizon of 10 s. The receding horizon is switched between an upper and a lower value according to the amplitude of the solar power trend. Theoretical results show the relevance of horizon switching, according to a tradeoff between performance and prediction accuracy. Experimental results, obtained in a Hardware In the Loop (HIL) framework, show the relevance of the variable horizon approach. Power amplifiers allow us to simulate virtual components, such as a gas microturbine, and to blend their powers with that of real devices (storage unit, real solar panels). In this case, fuel savings, reaching 15%, obtained under unsteady operating conditions lead to a better overall performance of the hybrid cell. The overall savings obtained in the experiments amount to 12%.

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“…Several approaches have been developed to deal with the computational complexity problem of MPC. Shekhar and Maciejowski [15] introduced a new formulation of variable horizon MPC that utilised move blocking for reducing computational complexity. Ling et al [16] proposed a form of MPC in which the control variables were moved asynchronously.…”

Section: Introductionmentioning

confidence: 99%

Robust distributed model predictive control for uncertain networked control systems

Zhang

Wang

et al. 2014

IET Control Theory & Applications

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In this study, an approach to design robust distributed model predictive control (MPC) is proposed for polytopic uncertain networked control systems with time delays. To reduce the computational complexity and improve the flexibility, the entire system is decomposed into multiple smaller dimensional subsystems. For each subsystem, the proposed robust distributed MPC algorithm requires solving multiple linear matrix inequality optimisation problems to minimise an upper bound on a robust performance objective. An augmented polytopic uncertainty description is invoked to handle the input delays. The conservativeness of distributed MPC algorithm is reduced by utilising a sequence of feedback control laws. An iterative on-line algorithm for robust distributed MPC is developed to coordinate the distributed MPC controllers. Convergence and robust stability of the proposed distributed MPC are investigated. A numerical example is carried out to demonstrate the effectiveness of the proposed algorithm.

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Robust variable horizon MPC with move blocking

Cited by 44 publications

References 10 publications

Optimal move blocking strategies for model predictive control

Optimal move blocking strategies for model predictive control

Event-triggered variable horizon Supervisory Predictive control of hybrid power plants

Robust distributed model predictive control for uncertain networked control systems

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