Proceedings Article10.1109/CCA.2001.973935
Predictive PID controller design
D. Uduehi,Andrzej Ordys,Michael J. Grimble +2 more
- 05 Sep 2001
- pp 612-617
7
TL;DR: In this paper, a polynomial predictive optimal control problem is discussed for the discrete time system where the structure of the controller is assumed to be limited to a discrete PID controller and the theoretical problem to be considered is to obtain the causal, stabilising, controller of the pre-specified PID form that minimises a generalised predictive control criterion and is equivalent to a GPC control law.
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Abstract: A polynomial predictive optimal control problem is discussed for the discrete time system where the structure of the controller is assumed to be limited to a discrete PID controller. The theoretical problem to be considered is to obtain the causal, stabilising, controller of the pre-specified PID form that minimises a generalised predictive control criterion and is equivalent to a GPC control law. The underlying practical problem and aim of the work is to obtain the optimal PID controller coefficients that provide the required closed loop performance over a range of operating conditions and working points.
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Citations
Combination of multi-model predictive control and the wave theory for the control of simulated moving bed plants
Carlos Vilas,Alain Vande Wouwer +1 more
TL;DR: In this paper, a new approach to the control of simulated moving bed (SMB) chromatographic separation processes is presented based on the combination of the wave theory and Multi-Model Predictive Control (MMPC).
28
Adaptive Constrained Predictive PID Controller via Particle Swarm Optimization
Song Ying,Chen Zengqiang,Yuan Zhuzhi +2 more
- 01 Jul 2006
TL;DR: A novel time-varying adaptive constrained predictive PID controller via PSO is proposed, based on the optimization of the GPC criterion with considering the constraints on the parameters of PID structures and control signal.
7
Adaptive PID control design based on generalized predictive control (GPC)
H.W. Comma
- 02 Sep 2004
TL;DR: In this article, a PID controller which is equivalent to a generalized predictive control (GPC) controller and incorporates the advantages of both PID and GPC has been designed, which can be used both in the adaptive/non-adaptive context.
4
•Journal Article
Internal model predictive control for a kind of processes with large dead time
TL;DR: Simulation results show that internal model predictive control has simple structure and is easy to be designed and readjusted, so it has certain application value.
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A receding-horizon multi-model approach to the control of SMB separation processes
Carlos Vilas,Alain Vande Wouwer +1 more
- 28 Oct 2010
TL;DR: This work proposes a Multi-Model Predictive Control (MMPC) strategy for SMB processes in which the partial differential equation model is reduced using the proper orthogonal decomposition technique, and the reduced-order model is the core of a receding-horizon control strategy.
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Optimization by Vector Space Methods
David G. Luenberger
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6.9K
Generalized predictive control—Part I. The basic algorithm
TL;DR: A novel method—generalized predictive control or GPC—is developed which is shown by simulation studies to be superior to accepted techniques such as generalized minimum-variance and pole-placement and to be a contender for general self-tuning applications.
3.8K
Paper: Model predictive heuristic control
TL;DR: In this paper, a new method of digital process control is described, which relies on three principles: 1) the multivariable plant is represented by its impulse responses which will be used on line by the control computer for long range prediction; 2) the behavior of the closed-loop system is prescribed by means of reference trajectories initiated on the actual outputs; 3) the control variables are computed in a heuristic way with the same procedure used in identification, which appears as a dual of the control under this formulation.
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On self tuning regulators
TL;DR: In this paper, the problem of controlling a system with constant but unknown parameters is considered and an algorithm obtained by combining a least squares estimator with a minimum variance regulator computed from the estimated model is analyzed.
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