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.
We investigate necessary and sufficient conditions under which a nonlinear affine control system with outputs can be written as a gradient control system corresponding to some pseudo-Riemannian metric defined on the s...
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We investigate necessary and sufficient conditions under which a nonlinear affine control system with outputs can be written as a gradient control system corresponding to some pseudo-Riemannian metric defined on the state space. The results rely on a suitable notion of compatibility of the system with respect to a given affine connection, and on the input-output behavior of the prolonged system and the gradient extension. The symmetric product associated with an affine connection plays a key role in the discussion
The growing demand in system reliability and survivability under failures has urged ever-increasing research effort on the development of fault diagnosis and accommodation. In this paper, the on-line fault tolerant co...
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The growing demand in system reliability and survivability under failures has urged ever-increasing research effort on the development of fault diagnosis and accommodation. In this paper, the on-line fault tolerant control problem for dynamic systems under unanticipated failures is investigated from a realistic point of view without any specific assumption on the type of system dynamical structure or failure scenarios. The sufficient conditions for system on-line stability under catastrophic failures have been derived using the discrete-time Lyapunov stability theory. Based upon the existing control theory and the modern computational intelligence techniques, an on-line fault accommodation control strategy is proposed to deal with the desired trajectory-tracking problems for systems suffering from various unknown and unanticipated catastrophic component failures. Theoretical analysis indicates that the control problem of interest can be solved on-line without a complete realization of the unknown failure dynamics provided an on-line estimator satisfies certain conditions. Through the on-line estimator, effective control signals to accommodate the dynamic failures can be computed using only the partially available information of the faults. Several on-line simulation studies have been presented to demonstrate the effectiveness of the proposed strategy. To investigate the feasibility of using the developed technique for unanticipated fault accommodation in hardware under the real-time environment, an on-line fault tolerant control test bed has been constructed to validate the proposed technology. Both on-line simulations and the real-time experiment show encouraging results and promising futures of on-line real-time fault tolerant control based solely upon insufficient information of the system dynamics and the failure dynamics.
In this paper, we propose a genetic algorithm based design procedure for a multi layer feed forward neural network. A hierarchical genetic algorithm is used to evolve both the neural networks topology and weighting pa...
In this paper, we propose a genetic algorithm based design procedure for a multi layer feed forward neural network. A hierarchical genetic algorithm is used to evolve both the neural networks topology and weighting parameters. Compared with traditional genetic algorithm based designs for neural networks, the hierarchical approach addresses several deficiencies, including a feasibility check highlighted in literature. A multi objective cost function is used herein to optimize the performance and topology of the evolved neural network simultaneously. In the prediction of Mackey Glass chaotic time series, the networks designed by the proposed approach prove to be competitive, or even superior, to traditional learning algorithms for the multi layer Perceptron networks and radial basis function networks. Based upon the chosen cost function, a linear weight combination decision making approach has been applied to derive an approximated Pareto optimal solution set. Therefore, designing a set of neural networks can be considered as solving a two objective optimization problem.
The motion of a multi-body autonomous robot as well as of a train-like multi-articulated transportation vehicle is characterized by the deviation of the path of each intermediate vehicle from that of the leading one (...
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In this paper, a virtual factory using the manufacturing message specification (MMS) companion standard is implemented. The MMS companion standard (9506-3 ∼ 9506-7) and virtual machines are designed and implemented f...
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In this paper, we propose a genetic algorithm based design procedure for a radial-basis function neural network. A hierarchical rank density genetic algorithm (HRDGA) is used to evolve both the neural network's to...
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