Efficient nonlinear model predictive control

Cannon, Mark; Kouvaritakis, B.; Brooms, Anthony C.; Lee, Y.I.

doi:10.1109/acc.2000.878857

Cited by 4 publications

(8 citation statements)

References 6 publications

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“…with @g @ % When we apply the robust constrained MPC in Theorem 1 to the LTV system (11), an ellipsoidal region of feasibility can be characterized by the following lemma.…”

Section: Aðkþ Bðkþmentioning

confidence: 99%

“…For the LTV system (11), an ellipsoidal region of feasibility of the robust constrained MPC in Theorem 1 is given by…”

Section: Lemmamentioning

confidence: 99%

“…For system (11), at sampling time k; apply uðkÞ ¼ F ðkÞxðkÞ where F ðkÞ ¼ YQ À1 with Q > 0 and Y obtained from the minimization problem min g;Q;X ;Y g subject to (5)- (7) and (13), where R is obtained from Lemma 1.…”

Section: Algorithmmentioning

confidence: 99%

“…It also provides a general framework for the scheduled MPC design, which can incorporate any local MPC design scheme once its explicit region of stability is characterized. Among various formulations of NMPC, Cannon et al [11] proposed an efficient nonlinear predictive controller with its region of stability approximated by an invariant ellipsoidal set, which can be a candidate for the local predictive controllers in the scheduled MPC design proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Efficient scheduled stabilizing model predictive control for constrained nonlinear systems

Wan

Kothare

2003

Intl J Robust & Nonlinear

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SUMMARYWe present a computationally efficient scheduled model predictive control (MPC) algorithm for constrained nonlinear systems with large operating regions. We design a set of local predictive controllers with estimates of their regions of stability covering the desired operating region, and implement them as a single scheduled MPC which on-line switches between the set of local controllers and achieves nonlinear transitions with guaranteed stability. This algorithm is computationally efficient and provides a general framework for the scheduled MPC design. The algorithm is illustrated with two examples.

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“…with @g @ % When we apply the robust constrained MPC in Theorem 1 to the LTV system (11), an ellipsoidal region of feasibility can be characterized by the following lemma.…”

Section: Aðkþ Bðkþmentioning

confidence: 99%

“…For the LTV system (11), an ellipsoidal region of feasibility of the robust constrained MPC in Theorem 1 is given by…”

Section: Lemmamentioning

confidence: 99%

Section: Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Efficient scheduled stabilizing model predictive control for constrained nonlinear systems

Wan

Kothare

2003

Intl J Robust & Nonlinear

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“…Moreover an optimized quality indicator includes a penalty for a control signals values change, which ensures lower energy consumption and lower device wear. For more information about MPC controllers please refer to [6]- [9].…”

Section: Introductionmentioning

confidence: 99%

Immune Inspired System for Chemical Process Optimization using the example of a Combustion Process in a Power Boiler

Wojdan

Świrski

Chomiak

2007

2007 International Conference on Intelligent Systems Applications to Power Systems

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The article presents an optimization method of combustion process in a power boiler. Immune Inspired Optimizer SILO is used to minimize CO and NOx emission. This solution is implemented in each of three units of Ostroleka Power Plant (Poland) and in the Newton Power Plant (USA). The result from the second SILO implementation in Newton Power Plant is presented. The results confirm that this solution is effective and usable in practice and it can be a good alternative to MPC controllers.

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