Wyniki wyszukiwania - Biblioteka Nauki

1

Advanced predictive control of a distillation column with neural models

100%

Lawrynczuk M.

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2007

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tom Vol. 17, no. 2

163-189

EN

This paper describes application of linear and nonlinear Model Predictive Control (MPC) algorithms to a cyclohexane-heptane distillation column. Two nonlinear MPC techniques are compared in terms of control accuracy and computational complexity: MPC with Nonlinear Optimization (MPC-NO) and MPC with Nonlinear Prediction and Linearization (MPC-NPL). In nonlinear MPC a feedforward neural model is used rather than significantly complicated and causing numerical problems fundamental model of the process.

2

Extension of first order Predictive Functional Controllers to handle higher order internal models

100%

Khadir M. , Ringwood J.

International Journal of Applied Mathematics and Computer Science

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2008

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tom 18

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nr 2

229-239

EN

Predictive Functional Control (PFC), belonging to the family of predictive control techniques, has been demonstrated as a powerful algorithm for controlling process plants. The input/output PFC formulation has been a particularly attractive paradigm for industrial processes, with a combination of simplicity and effectiveness. Though its use of a lag plus delay ARX/ARMAX model is justified in many applications, there exists a range of process types which may present difficulties, leading to chattering and/or instability. In this paper, instability of first order PFC is addressed, and solutions to handle higher order and difficult systems are proposed. The input/output PFC formulation is extended to cover the cases of internal models with zero and/or higher order pole dynamics in an ARX/ARMAX form, via a parallel and cascaded model decomposition. Finally, a generic form of PFC, based on elementary outputs, is proposed to handle a wider range of higher order oscillatory and non-minimum phase systems. The range of solutions presented are supported by appropriate examples.

3

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

100%

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.

4

PH control of penicillin fermentation process using predictive approach

100%

Ashoori A. , Moshiri B. , Ramezani A. , Bakhtiari M. R. , Khaki-Sedigh A.

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2009

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tom Vol. 35, no 1

65-74

EN

Bioprocesses which are involved in producing different pharmaceutical products may conveniently be classified according to the mode chosen for the process: either batch, fed-batch or continuous. From the control engineer's viewpoint they are fed-batch processes, which present the greatest challenge to get a pure product with a high concentration. Complicated dynamics, nonlinearity and non-stationarity make controlling them a very delicate task. pH control of bioreactors has been an interesting problem from both implementation and controller design points of view. This is particularly true if the complex microbial interactions yield significant nonlinear behavior. When this occurs, conventional control strategies may not succeed and more advanced strategies need to be suggested. This paper discusses model predictive control (MPC) based on a detailed unstructured model for penicillin production in a fed-batch fermentor. The approach used here is to use quadratic cost function for pH regulation, while taking into account control signal fluctuations in the optimization block. The result of applying the obtained controller and also its sensitivity to disturbance have been displayed and compared with the results of an auto-tuned PID controller used in previous works. The merit of this method is its low computational cost of solving the optimization problem, while leading to a closed form controller as well.

5

Stable output feedback model predictive control design: LMI approach

100%

Vesely V. , Bars R.

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2008

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tom Vol. 18, no. 3

385-394

EN

The paper addresses two design problems dealing with a quadratic stable output/state feedback model predictive control for linear systems without constraints. For the first approach the model predictive control is designed for a N2 state ahead prediction using the Lyapunov function approach with cost function. In the second approach, the one step ahead prediction control is designed using classical LQR state feedback controller approach. By Diophantine matrix equation, the classical state feedback is recalculated to output one step ahead model predictive control. Three examples are given to demonstrate the effectiveness of proposed methods.

6

Towards robust predictive fault-tolerant control for a battery assembly system

88%

Seybold L. , Witczak M. , Majdzik P. , Stetter R.

International Journal of Applied Mathematics and Computer Science

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2015

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tom 25

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nr 4

849-862

EN

The paper deals with the modeling and fault-tolerant control of a real battery assembly system which is under implementation at the RAFI GmbH company (one of the leading electronic manufacturing service providers in Germany). To model and control the battery assembly system, a unified max-plus algebra and model predictive control framework is introduced. Subsequently, the control strategy is enhanced with fault-tolerance features that increase the overall performance of the production system being considered. In particular, it enables tolerating (up to some degree) mobile robot, processing and transportation faults. The paper discusses also robustness issues, which are inevitable in real production systems. As a result, a novel robust predictive fault-tolerant strategy is developed that is applied to the battery assembly system. The last part of the paper shows illustrative examples, which clearly exhibit the performance of the proposed approach.

7

Disturbance modeling and state estimation for offset-free predictive control with state-space process models

88%

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.

8

An infinite horizon predictive control algorithm based on multivariable input-output models

88%

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.

9

Predictive control for trajectory tracking and decentralized navigation of multi-agent formations

88%

Prodan I. , Olaru S. , Stoica C. , Niculescu S.-I.

International Journal of Applied Mathematics and Computer Science

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2013

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tom 23

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nr 1

91-102

EN

This paper addresses a predictive control strategy for a particular class of multi-agent formations with a time-varying topology. The goal is to guarantee tracking capabilities with respect to a reference trajectory which is pre-specified for an agent designed as the leader. Then, the remaining agents, designed as followers, track the position and orientation of the leader. In real-time, a predictive control strategy enhanced with the potential field methodology is used in order to derive a feedback control action based only on local information within the group of agents. The main concern is that the interconnections between the agents are time-varying, affecting the neighborhood around each agent. The proposed method exhibits effective performance validated through some illustrative examples.

10

Fault tolerant control using Gaussian processes and model predictive control

88%

Yang X. , Maciejowski J.

International Journal of Applied Mathematics and Computer Science

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2015

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tom 25

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nr 1

133-148

EN

Essential ingredients for fault-tolerant control are the ability to represent system behaviour following the occurrence of a fault, and the ability to exploit this representation for deciding control actions. Gaussian processes seem to be very promising candidates for the first of these, and model predictive control has a proven capability for the second. We therefore propose to use the two together to obtain fault-tolerant control functionality. Our proposal is illustrated by several reasonably realistic examples drawn from flight control.

11

Soft computing in modelbased predictive control footnotemark

88%

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.

12

Nonlinear predictive control based on neural multi-models

88%

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.

13

Robust MPC for actuator-fault tolerance using set-based passive fault detection and active fault isolation

88%

Xu F. , Puig V. , Ocampo-Martinez C. , Olaru S. , Niculescu S.-I.

International Journal of Applied Mathematics and Computer Science

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2017

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tom 27

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nr 1

43-61

EN

In this paper, a fault-tolerant control (FTC) scheme is proposed for actuator faults, which is built upon tube-based model predictive control (MPC) as well as set-based fault detection and isolation (FDI). In the class of MPC techniques, tubebased MPC can effectively deal with system constraints and uncertainties with relatively low computational complexity compared with other robust MPC techniques such as min-max MPC. Set-based FDI, generally considering the worst case of uncertainties, can robustly detect and isolate actuator faults. In the proposed FTC scheme, fault detection (FD) is passive by using invariant sets, while fault isolation (FI) is active by means of MPC and tubes. The active FI method proposed in this paper is implemented by making use of the constraint-handling ability of MPC to manipulate the bounds of inputs. After the system has been detected to become faulty, the input-constraint set of the MPC controller is adjusted to actively excite the system for achieving FI guarantees on-line, where an active FI-oriented input set is designed off-line. In this way, the system can be excited in order to obtain more extra system-operating information for FI than passive FI approaches. Overall, the objective of this paper is to propose an actuator MPC scheme with as little as possible of FI conservatism and computational complexity by combining tube-based MPC and set theory within the framework of MPC, respectively. Finally, a case study is used to show the effectiveness of the proposed FTC scheme.

14

Nonlinear state-space predictive control with on-line linearisation and state estimation

88%

Ławryńczuk M.

International Journal of Applied Mathematics and Computer Science

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2015

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tom 25

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nr 4

833-847

EN

This paper describes computationally efficient model predictive control (MPC) algorithms for nonlinear dynamic systems represented by discrete-time state-space models. Two approaches are detailed: in the first one the model is successively linearised on-line and used for prediction, while in the second one a linear approximation of the future process trajectory is directly found on-line. In both the cases, as a result of linearisation, the future control policy is calculated by means of quadratic optimisation. For state estimation, the extended Kalman filter is used. The discussed MPC algorithms, although disturbance state observers are not used, are able to compensate for deterministic constant-type external and internal disturbances. In order to illustrate implementation steps and compare the efficiency of the algorithms, a polymerisation reactor benchmark system is considered. In particular, the described MPC algorithms with on-line linearisation are compared with a truly nonlinear MPC approach with nonlinear optimisation repeated at each sampling instant.

15

Efficient nonlinear predictive control based on structured neural models

88%

Ławryńczuk M.

International Journal of Applied Mathematics and Computer Science

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2009

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tom 19

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nr 2

233-246

EN

This paper describes structured neural models and a computationally efficient (suboptimal) nonlinear Model Predictive Control (MPC) algorithm based on such models. The structured neural model has the ability to make future predictions of the process without being used recursively. Thanks to the nature of the model, the prediction error is not propagated. This is particularly important in the case of noise and underparameterisation. Structured models have much better long-range prediction accuracy than the corresponding classical Nonlinear Auto Regressive with eXternal input (NARX) models. The described suboptimal MPC algorithm needs solving on-line only a quadratic programming problem. Nevertheless, it gives closed-loop control performance similar to that obtained in fully-fledged nonlinear MPC, which hinges on online nonconvex optimisation. In order to demonstrate the advantages of structured models as well as the accuracy of the suboptimal MPC algorithm, a polymerisation reactor is studied.

16

Performance evaluation based fault tolerant control with actuator saturation avoidance

88%

Boussaid B. , Aubrun C. , Abdelkrim M. , Gayed M.

International Journal of Applied Mathematics and Computer Science

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2011

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tom 21

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nr 3

457-466

EN

In this paper, a new approach regarding a reconfigured system is proposed to improve the performance of an active fault tolerant control system. The system performance is evaluated with an intelligent index of performance. The reconfiguration mechanism is based on a model predictive controller and reference trajectory management techniques. When an actuator fault occurs in the system, a new degraded reference trajectory is generated and the controller calculates new admissible controls. A constraint set and cost function are established to avoid actuator saturation and reduce the control energy spent in closed loop dynamics. The effectiveness of the proposed method is illustrated using a hydrothermal system subject to actuator faults and constraints on actuator dynamic ranges.

17

Reliability-based economic model predictive control for generalised flow-based networks including actuators' health-aware capabilities

75%

Grosso J. , Ocampo-Martinez C. , Puig V.

International Journal of Applied Mathematics and Computer Science

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2016

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tom 26

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nr 3

641-654

EN

This paper proposes a reliability-based economic model predictive control (MPC) strategy for the management of generalised flow-based networks, integrating some ideas on network service reliability, dynamic safety stock planning, and degradation of equipment health. The proposed strategy is based on a single-layer economic optimisation problem with dynamic constraints, which includes two enhancements with respect to existing approaches. The first enhancement considers chance-constraint programming to compute an optimal inventory replenishment policy based on a desired risk acceptability level, leading to dynamical allocation of safety stocks in flow-based networks to satisfy non-stationary flow demands. The second enhancement computes a smart distribution of the control effort and maximises actuators' availability by estimating their degradation and reliability. The proposed approach is illustrated with an application of water transport networks using the Barcelona network as the case study considered.

18

Effective dual-mode fuzzy DMC algorithms with on-line quadratic optimization and guaranteed stability

75%

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.

19

Efficient MPC algorithms with variable trajectories of parameters weighting predicted control errors

75%

Nebeluk R. , Marusak P. , Nebeluk R. , Marusak P.

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nr 2

20

EDM process optimization via predicting a controller model

63%

Mahdavinejad R. A.

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2009

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tom Vol. 1, nr 3

161-167

EN

Purpose: Electro-discharge machining is an important manufacture technology in machining difficult-to-cut materials and to shape complicated contours and profiles with high material removal rate, low tool wear and good tolerances. Design/methodology/approach: In machining of carbon-based materials such as WC-Co and non-oxide ceramics which are growingly used, the complexity and non-linear nature of EDM is a serious problem. EDM is the best and nearly the only non-conventional method for machining of these kind of materials, but it shows high instability and tendency to arcing, compared with machining of steel. Occurrence of instability phenomenon due to the different input setting up parameters make the modeling of EDM process impossible with conventional methods. To achieve instantaneous data from machining condition, the new method of fuzzy analysis of single machining pulses and computing the magnitude of system condition in the form of a real number between 0 and 1, has been used. Findings: Some tests with WC-Co material are carried out and finally, the results of implementation of control system on a sinking ED machine and an EDM system that has been set with an expert user, has been compared. Practical implications: The optimization and control of EDM process using the neural model predictive control method. A genetic algorithm has also been employed to optimize the input parameters and to create the optimized setting collection of process. Originality/value: The testing results from ED machining of WC-Co confirms the capability of the system of predictive controller model based on neural network with 32.8% efficiency increasing in stock removal rate.