Discrete-Time Model Predictive Control

Li, Dai; Xia, Yuanqing; Fu, Mengyin; Mahmoud, Magdi S.

doi:10.5772/51122

Cited by 14 publications

(9 citation statements)

References 153 publications

(144 reference statements)

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“…, ỹ (k + N |k)], where ỹ (k+m | k) is the predicted output variable at (k+m) th sampling instant when actual output y(k) at k th instant is given. The control horizon M is chosen to be less than the prediction horizon N [8].…”

Section: Controller Designmentioning

confidence: 99%

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Multiple model based predictive control of magnetic levitation system

Seban

Roy

2014

2014 Annual IEEE India Conference (INDICON)

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This paper deals with the real time control of an electromagnetic ball suspension system using multiple model based predictive control (MMPC). The objective is to control the position of a metal ball in air space. The multiple model predictive control is designed based on the linearized models of the system about multiple operating conditions. For real time control a laboratory version of magnetic levitation device (33-210) by Feedback instruments is used. The performance of MMPC is compared with normal model predictive control (MPC) and found that MMPC is better than MPC.

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Section: Controller Designmentioning

confidence: 99%

“…Even though nonlinear model predictive control finds space in the literature, the complexity and computational effort makes it less appealing. Linear model predictive control is direct, simple with less computational effort [8]. It can also be extended to different types of systems.…”

Section: Introductionmentioning

confidence: 99%

Multiple model based predictive control of magnetic levitation system

Seban

Roy

2014

2014 Annual IEEE India Conference (INDICON)

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“…Only the first control move is applied to the system. This is the opposite to the pre-computed control law such as PI control where the closed-loop performance is considered [9]. The advantages of MPC are ease of multivariable control tasks and avoiding of the cascade control structure.…”

Section: Modulation Techniquementioning

confidence: 99%

Comparison of MPC and PI Controller for Grid-Connected Cascade Inverter

Pastor

Dudrik

2014

ElAEE

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The paper describes and compares the dynamic performance of the PI and the MPC controllers used with the one-phase multilevel cascade inverter connected to the grid through an LCL filter. The cascade inverter uses multilevel sinusoidal PWM modulation technique and is controlled in rotating reference frame. The PI controller uses active damping using virtual resistance to damp oscillations of LCL filter. The MPC controller results present its ability to stabilise unstable plants and no additional active damping technique is necessary to stabilise the LCL filter. Both controllers have the same sampling time.

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“…As an alternative to such an indirect approach, to attack both the energy maximization problem and the force level quantization problem, the variant of closed-loop control model known as either receding horizon control or model predictive control has two appealing characteristics for use in a WEC with a discrete PTO system: as detailed in [15], MPC allows direct handling of constraints…”

Section: Model Predictive Control Of Discrete Pto Systemmentioning

confidence: 99%

Model Predictive Control of a Wave Energy Converter with Discrete Fluid Power Power Take-Off System

Hansen¹,

Asmussen

Bech³

2018

Energies

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Wave power extraction algorithms for wave energy converters are normally designed without taking system losses into account leading to suboptimal power extraction. In the current work, a model predictive power extraction algorithm is designed for a discretized power take of system. It is shown how the quantized nature of a discrete fluid power system may be included in a new model predictive control algorithm leading to a significant increase in the harvested power. A detailed investigation of the influence of the prediction horizon and the time step is reported. Furthermore, it is shown how the inclusion of a loss model may increase the energy output. Based on the presented results it is concluded that power extraction algorithms based on model predictive control principles are both feasible and favorable for use in a discrete fluid power power take-off system for point absorber wave energy converters.

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Discrete-Time Model Predictive Control

Cited by 14 publications

References 153 publications

Multiple model based predictive control of magnetic levitation system

Multiple model based predictive control of magnetic levitation system

Comparison of MPC and PI Controller for Grid-Connected Cascade Inverter

Model Predictive Control of a Wave Energy Converter with Discrete Fluid Power Power Take-Off System

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