Output Constraint Softening for SISO Model Predictive Control

Zafiriou, Evanghelos; Chiou, Hung-Wen

doi:10.23919/acc.1993.4792877

Cited by 30 publications

(9 citation statements)

References 8 publications

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“…This issue may remain as an extended research topic for future work. Suggestions such as softening the constraints [36], [37] or applying linear matrix inequalities based on robust MPC [38], [39] are some simulation examples for possible directions for further successful constrained control implementation.…”

Section: Discussionmentioning

confidence: 99%

Precision Tracking Control and Constraint Handling of Mechatronic Servo Systems Using Model Predictive Control

Lin

Liu²

2012

IEEE/ASME Trans. Mechatron.

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This paper presents precision tracking control and constraint handling of mechatronic servo systems using model predictive control. The current study revisits integral model predictive control, a common technique used in industrial process applications, from a motion control perspective for step tracking and constraint handling. To improve the control performance for periodic signal tracking, this paper integrates an internal modelbased repetitive control law with the model predictive controller and transforms the original problem to a quadratic programming problem to deal with the given constraints. The current study applies the aforesaid controls to a piezoactuated system, implemented at a 10-kHz sampling rate. This research analyzes and discusses the experimental results of several controller design parameters affecting the control performance. Asymptotic error tracking and constraint handling results particularly demonstrate the effectiveness and potential of the model predictive controller for the servo design of fast mechatronic systems.

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Section: Discussionmentioning

confidence: 99%

Precision Tracking Control and Constraint Handling of Mechatronic Servo Systems Using Model Predictive Control

Lin

Liu²

2012

IEEE/ASME Trans. Mechatron.

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“…The optimization may include linear inequality constraints imposed on input and output variables. γ(t) is a slack variable that slackens the output constraints to avoid possible infeasibility when there is a large disturbance [14].…”

Section: Basic Structure and Dynamic Optimizationmentioning

confidence: 99%

Model predictive control of condensate recycle process in a cogeneration power station: Controller design and numerical application

Won

Lee

Kim

et al. 2008

Korean J. Chem. Eng.

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A model predictive control (MPC) system has been developed for application to the condensate recycle process of a 300 MW cogeneration power station of the East-West Power Plant, Gyeonggido, Korea. Unlike other industrial processes where MPC has been predominantly applied, the operation mode of the cogeneration power station changes continuously with weather and seasonal conditions. Such characteristic makes it difficult to find the process model for controller design through identification. To overcome the difficulty, process models for MPC design were derived for each operation mode from the material balance applied to the pipeline network around the concerned process. The MPC algorithm has been developed so that the controller tuning is easy with one tuning knob for each output and the constrained optimization is solved by an interior-point method. For verification of the MPC system before process implementation, a process simulator was also developed. Performance of the MPC was investigated first with a process simulator against various disturbance scenarios.

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“…Output and state constraints may lead to infeasibilities in the optimization problem, and as a result these are often omitted or incorporated in a ‘soft’ form by replacing the hard constraint with corresponding penalty terms in the objective function (Zafiriou & Chiou 1993). A schematic of MPC implementation is shown in figure 6.…”

Section: Decision Support Toolsmentioning

confidence: 99%

Systems engineering medicine: engineering the inflammation response to infectious and traumatic challenges

Clermont

2010

J. R. Soc. Interface.

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The complexity of the systemic inflammatory response and the lack of a treatment breakthrough in the treatment of pathogenic infection demand that advanced tools be brought to bear in the treatment of severe sepsis and trauma. Systems medicine, the translational science counterpart to basic science's systems biology, is the interface at which these tools may be constructed. Rapid initial strides in improving sepsis treatment are possible through the use of phenomenological modelling and optimization tools for process understanding and device design. Higher impact, and more generalizable, treatment designs are based on mechanistic understanding developed through the use of physiologically based models, characterization of population variability, and the use of control-theoretic systems engineering concepts. In this review we introduce acute inflammation and sepsis as an example of just one area that is currently underserved by the systems medicine community, and, therefore, an area in which contributions of all types can be made.

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Output Constraint Softening for SISO Model Predictive Control

Cited by 30 publications

References 8 publications

Precision Tracking Control and Constraint Handling of Mechatronic Servo Systems Using Model Predictive Control

Precision Tracking Control and Constraint Handling of Mechatronic Servo Systems Using Model Predictive Control

Model predictive control of condensate recycle process in a cogeneration power station: Controller design and numerical application

Systems engineering medicine: engineering the inflammation response to infectious and traumatic challenges

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