We now apply the model predictive control (MPC) of speed limits that we have presented in previous publications to a calibrated METANET model of a 19 km stretch of the real-world freeway A1 in The Netherlands. This fr...
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This paper is concerned with an application study of model-based fault detection method to a ship propulsion system. When modeling the object system, Quasi-ARMAX model with multi-model form is used. In this model, the...
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This paper is concerned with an application study of model-based fault detection method to a ship propulsion system. When modeling the object system, Quasi-ARMAX model with multi-model form is used. In this model, the system non-linearity is incorporated into model parameters by using non-linear non-parametric models (NNMs). Kullback discrimination Information (KDI) is introduced as fault detection index to evaluate the distortion in identified model, which is caused by a fault. The effectiveness of the method is verified through simulation studies on the ship propulsion system.
In this paper, we present an Adaptive Network-based Fuzzy Inference System (ANFIS), based on a neuro-fuzzy controller, as a possible control mechanism for a ship stabilizing fin system. Simulation results show that AN...
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In this paper, we present an Adaptive Network-based Fuzzy Inference System (ANFIS), based on a neuro-fuzzy controller, as a possible control mechanism for a ship stabilizing fin system. Simulation results show that ANFIS can effectively improve the ship stabilizing performance against roll motion in cases of rough sea conditions. it is a promising alternative to conventional PID controllers.
Differential linear repetitive processes are a class of continuous-discrete 2D systems of both systems theoretic and applications interest. The feature which makes them distinct from other classes of such systems is t...
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Differential linear repetitive processes are a class of continuous-discrete 2D systems of both systems theoretic and applications interest. The feature which makes them distinct from other classes of such systems is the fact that information propagation in one of the two independent directions only occurs over a finite interval. In this paper we develop an operator theory approach for the study of basic systems theoretic structural and control properties of these processes. In particular, we first develop a characterization of the range space of an operator generated by dynamics of the processes under consideration and use it to characterize a controllability property. Also we extend this operator setting to produce new results for a (again physically relevant) linear-quadratic optimization problem for these processes and the resulting optimal feedback control law.
Differential linear repetitive processes are a class of continuous-discrete 2D linear systems of both systems theoretic and applications interest. The feature which makes them distinct from other classes of such syste...
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Differential linear repetitive processes are a class of continuous-discrete 2D linear systems of both systems theoretic and applications interest. The feature which makes them distinct from other classes of such systems is that information propagation in one of the two independent directions only occurs over a finite interval. Applications areas include iterative learning control and iterative solution algorithms for classes of dynamic nonlinear optimal control problems based on the maximum principle. In this paper, we investigate further the structural links between differential linear repetitive processes and a special class of time delay systems. This leads to some significant new controllability and optimal control results for these processes.
The advanced control system for control of nonlinear, slowly time variant and delayed processes is described in the paper. The proposed control system performs automatic tuning of fuzzy gain-scheduling controller para...
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In this paper, we propose a deterministic approach to the robust filtering problem for a class of uncertain nonlinear systems. Based on polynomial Lyapunov functions and a relaxation technique, we derive linear matrix...
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ISBN:
(纸本)0972184449
In this paper, we propose a deterministic approach to the robust filtering problem for a class of uncertain nonlinear systems. Based on polynomial Lyapunov functions and a relaxation technique, we derive linear matrix inequalities conditions that minimize an upper-bound on the finite horizon 2-norm of the estimation error for all admissible uncertainty and input signals (including disturbances and measurement noise). Through a simple redefinition of the Lyapunov matrix, we extended the results for the reduced-order case without considering non-convex rank constraints.
A controlsystemsengineering approach, employing a two-level overall system architecture and different but compatible formalisms for system representation on the upper and lower levels, has been investigated in detai...
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A controlsystemsengineering approach, employing a two-level overall system architecture and different but compatible formalisms for system representation on the upper and lower levels, has been investigated in detail. One design alternative is based on employing fuzzy-system approximators and solving for the adaptive tracking of the given, arbitrary, desired system outputs. The other alternative is based on state equations of composite systems and the use of neural-network approximators to deal with uncertainties and control adaptation. In both alternatives similarity property of subsystems has been exploited. Both designs can be implemented within the standard computer process control technology, and are therefore believed to be promising in applied systemsengineering.
The advanced control system for control of nonlinear, slowly time variant and delayed processes is described in the paper. The proposed control system performs automatic tuning of fuzzy gain-scheduling controller para...
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The advanced control system for control of nonlinear, slowly time variant and delayed processes is described in the paper. The proposed control system performs automatic tuning of fuzzy gain-scheduling controller parameters. The parameters are tuned according to a non-linear process model, which is identified by performing simple experiments on the actual process. The proposed control system (ASPECT) is implemented as a software product and currently runs on several PLC platforms.
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