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1
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Supervisory predictive control and on-line set-point optimization

100%
Tatjewski P.
International Journal of Applied Mathematics and Computer Science
|
2010
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tom 20
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nr 3
483-495
EN
The subject of this paper is to discuss selected effective known and novel structures for advanced process control and optimization. The role and techniques of model-based predictive control (MPC) in a supervisory (advanced) control layer are first shortly discussed. The emphasis is put on algorithm efficiency for nonlinear processes and on treating uncertainty in process models, with two solutions presented: the structure of nonlinear prediction and successive linearizations for nonlinear control, and a novel algorithm based on fast model selection to cope with process uncertainty. Issues of cooperation between MPC algorithms and on-line steady-state set-point optimization are next discussed, including integrated approaches. Finally, a recently developed two-purpose supervisory predictive set-point optimizer is discussed, designed to perform simultaneously two goals: economic optimization and constraints handling for the underlying unconstrained direct controllers.
2
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Disturbance modeling and state estimation for offset-free predictive control with state-space process models

100%
Tatjewski P.
International Journal of Applied Mathematics and Computer Science
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2014
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tom 24
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nr 2
313-323
EN
Disturbance modeling and design of state estimators for offset-free Model Predictive Control (MPC) with linear state-space process models is considered in the paper for deterministic constant-type external and internal disturbances (modeling errors). The application and importance of constant state disturbance prediction in the state-space MPC controller design is presented. In the case with a measured state, this leads to the control structure without disturbance state observers. In the case with an unmeasured state, a new, simpler MPC controller-observer structure is proposed, with observation of a pure process state only. The structure is not only simpler, but also with less restrictive applicability conditions than the conventional approach with extended process-and-disturbances state estimation. Theoretical analysis of the proposed structure is provided. The design approach is also applied to the case with an augmented state-space model in complete velocity form. The results are illustrated on a 2×2 example process problem.
3
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Nonlinear predictive control based on neural multi-models

64%
Ławryńczuk M., Tatjewski P.
International Journal of Applied Mathematics and Computer Science
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2010
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tom 20
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nr 1
7-21
EN
This paper discusses neural multi-models based on Multi Layer Perceptron (MLP) networks and a computationally efficient nonlinear Model Predictive Control (MPC) algorithm which uses such models. Thanks to the nature of the model it calculates future predictions without using previous predictions. This means that, unlike the classical Nonlinear Auto Regressive with eXternal input (NARX) model, the multi-model is not used recurrently in MPC, and the prediction error is not propagated. In order to avoid nonlinear optimisation, in the discussed suboptimal MPC algorithm the neural multi-model is linearised on-line and, as a result, the future control policy is found by solving of a quadratic programming problem.
4
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Analysis of an isopetype dual algorithm for optimizing control and nonlinear optimization

64%
Tadej W., Tatjewski P.
International Journal of Applied Mathematics and Computer Science
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2001
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tom 11
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nr 2
429-457
EN
First results concerning important theoretical properties of the dual ISOPE (Integrated System Optimization and Parameter Estimation) algorithm are presented. The algorithm applies to on-line set-point optimization in control structures with uncertainty in process models and disturbance estimates, as well as to difficult nonlinear constrained optimization problems. Properties of the conditioned (dualized) set of problem constraints are investigated, showing its structure and feasibility properties important for applications. Convergence conditions for a simplified version of the algorithm are derived, indicating a practically important threshold value of the right-hand side of the conditioning constraint. Results of simulations are given confirming the theoretical results and illustrating properties of the algorithms.
5
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An infinite horizon predictive control algorithm based on multivariable input-output models

64%
Ławryńczuk M., Tatjewski P.
International Journal of Applied Mathematics and Computer Science
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2004
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tom 14
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nr 2
167-180
EN
In this paper an infinite horizon predictive control algorithm, for which closed loop stability is guaranteed, is developed in the framework of multivariable linear input-output models. The original infinite dimensional optimisation problem is transformed into a finite dimensional one with a penalty term. In the unconstrained case the stabilising control law, using a numerically reliable SVD decomposition, is derived as an analytical formula, calculated off-line. Considering constraints needs solving on-line a quadratic programming problem. Additionally, it is shown how free and forced responses can be calculated without the necessity of solving a matrix Diophantine equation.
6
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Effective dual-mode fuzzy DMC algorithms with on-line quadratic optimization and guaranteed stability

64%
Marusak P., Tatjewski P.
International Journal of Applied Mathematics and Computer Science
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2009
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tom 19
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nr 1
127-141
EN
Dual-mode fuzzy dynamic matrix control (fuzzy DMC-FDMC) algorithms with guaranteed nominal stability for constrained nonlinear plants are presented. The algorithms join the advantages of fuzzy Takagi-Sugeno modeling and the predictive dual-mode approach in a computationally efficient version. Thus, they can bring an improvement in control quality compared with predictive controllers based on linear models and, at the same time, control performance similar to that obtained using more demanding algorithms with nonlinear optimization. Numerical effectiveness is obtained by using a successive linearization approach resulting in a quadratic programming problem solved on-line at each sampling instant. It is a computationally robust and fast optimization problem, which is important for on-line applications. Stability is achieved by appropriate introduction of dual-mode type stabilization mechanisms, which are simple and easy to implement. The effectiveness of the proposed approach is tested on a control system of a nonlinear plant-a distillation column with basic feedback controllers.
7
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Soft computing in modelbased predictive control footnotemark

64%
Tatjewski P., Ławrynczuk M.
International Journal of Applied Mathematics and Computer Science
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2006
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tom 16
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nr 1
7-26
EN
The application of fuzzy reasoning techniques and neural network structures to model-based predictive control (MPC) is studied. First, basic structures of MPC algorithms are reviewed. Then, applications of fuzzy systems of the Takagi-Sugeno type in explicit and numerical nonlinear MPC algorithms are presented. Next, many techniques using neural network modeling to improve structural or computational properties of MPC algorithms are presented and discussed, from a neural network model of a process in standard MPC structures to modeling parts or entire MPC controllers with neural networks. Finally, a simulation example and conclusions are given.
8
Content available remote

Actuator fault tolerance in control systems with predictive constrained set-point optimizers

64%
Marusak P., Tatjewski P.
International Journal of Applied Mathematics and Computer Science
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2008
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tom 18
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nr 4
539-551
EN
Mechanisms of fault tolerance to actuator faults in a control structure with a predictive constrained set-point optimizer are proposed. The structure considered consists of a basic feedback control layer and a local supervisory set-point optimizer which executes as frequently as the feedback controllers do with the aim to recalculate the set-points both for constraint feasibility and economic performance. The main goal of the presented reconfiguration mechanisms activated in response to an actuator blockade is to continue the operation of the control system with the fault, until it is fixed. This may be even long-term, if additional manipulated variables are available. The mechanisms are relatively simple and consist in the reconfiguration of the model structure and the introduction of appropriate constraints into the optimization problem of the optimizer, thus not affecting the numerical effectiveness. Simulation results of the presented control system for a multivariable plant are provided, illustrating the efficiency of the proposed approach.
9
Content available remote

Fail-bounded implementations of the numerical model predictive control algorithms

45%
Gawkowski P., Ławryńczuk M., Marusak P., Sosnowski J., Tatjewski P.
Control and Cybernetics
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2010
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tom 39
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nr 4
1117-1134
10
Content available remote

Cooperation of model predictive control with steady-state economic optimisation

45%
Ławryńczuk M., Marusak P. M., Tatjewski P.
Control and Cybernetics
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2008
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tom 37
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nr 1
133-158
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