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The boiler-turbine coordinated control system in power station has the characteristics of multi-variables, nonlinear, time-varying, coupling and large time-delay etc, and the performance of the boiler-turbine control system has great impact on the safety and economy of a power unit. Biology immune system is characterized by its strong robustness and self-adaptability even when encountering amounts...
Intelligent Buildings (IB) has not been adopted as quickly and widely as expected. One of the reasons for this is the lack of information and technology support to IB subsystem involved. This paper redefines the concept and connotation of IB, analyzes the influence of ANN on the development of IB, analyses many subsystems of IB which are time-variant nonlinearity complex system, discusses the role...
The optimization and control problem of Wet Flue Gas Desulphurization (WFGD) in power plant was studied. Mixed logical dynamical (MLD) model was built to describe hybrid phenomenon of the WFGD process. Based on the mixed logic dynamical model of WFGD, the predictive control optimization problem was solved using the Mixed Integer Quadratic program (MIQP) solver. Therefore the optimal discrete decision...
In the domain of industry process control, the model identification and predictive control of nonlinear systems are always difficult problems. To solve the problems, an identification method based on least squares support vector machines for function approximation is utilized to identify a nonlinear autoregressive external input (NARX) model. The NARX model is then used to construct a novel nonlinear...
We consider the problem of designing encoders, decoders and controllers which stabilize feed forward nonlinear systems over a communication network with finite bandwidth and large delay. The control scheme guarantees minimal data-rate semi-global asymptotic and local exponential stabilization of the closed-loop system. The analysis rests on the stability properties of a class of cascade impulsive...
The design of a nonlinear predictive controller, based on a fuzzy model is presented. The Takagi-Sugeno fuzzy model with an adaptive neuro-fuzzy implementation is used and incorporated as a predictor in a predictive controller. An optimization approach with a simplified gradient technique is used to calculate predictions of the future control actions. In this approach, adaptation of the fuzzy model...
The goal of this paper is to control PMSM (permanent magnet synchronous motor) with PCH (port-controlled Hamiltonian) theory. PCH is a kind of nonlinear control method. Energy-shaping approach is the essence of passivity-based control. With the definition of generalized passive Hamiltonian system, the PCH structure of permanent magnet synchronous motor was given. By using the MTPA (maximum torque...
This paper gives an X-Q nonlinear controller and CAD method for the multi-objective control systems. Then discusses how to use the X-Q controller to perform it. Finally, in this paper, an example of servo system designed with CAD multi-objective optimal design method is given. The performance checked by computer simulation shows that the performance of multi-objective optimal system is better than...
This paper presents a new active control strategy with enhanced robustness for the structural systems with nonlinear uncertainties and exogenous disturbances. Particularly, due to existence of the perturbation in the mass matrix, the considered plant is described by a dynamical model with neural uncertainty, or an uncertain descriptor (or singular) system. A novel direct robust vibration control law...
This paper is concerned with the asymptotic stability of Takagi-Sugeno (T-S) fuzzy systems with state-delay. The state-of-the-art of delay-dependent stability analysis method is provided in terms of linear matrix inequalities (LMIs). The augmented Lyapunov functional method is used to establish delay-dependent criteria. Theoretical comparisons are done among the present and existing results. Illustrative...
In this paper, a direct adaptive fuzzy backstepping control approach for a class of unknown nonlinear systems is developed. By a special design scheme, the controller singularity problems is avoided perfectly in this approach. Furthermore, the closed-loop signals are guaranteed to be semiglobally uniformly ultimately bounded and the outputs of the system are proved to be converge to a small neighborhood...
In order to measure the uncertainty of the stochastic systems subjected to arbitrary noise disturbance instead of Gaussian white noise, the minimum entropy control of tracking errors for dynamic stochastic systems is presented in this paper. Different from conventional hypothesis, it is assumed that the system output and noise obey multi-to-one mapping, which is more general in the practical application...
In this paper, we describe our application of a neuro-controller based on hybrid system to optimize the combustion-process for an industrial waste incineration plant. This controller originally was designed for online controlling. It is difficult to achieve effective control of time variable and nonlinear plants such a waste combustion. Therefore a modified network topologies and training regimes...
For packet-based transmission of data over a network, or temporary sensor failure, etc., data samples may be missing in the measured signals. The missing measurements will happen at any sample time, and the probability of the occurrence of missing data was assumed to be known. The series which fulfils Bernoulli distribution was used to describe the missing measurements. Based on the Takagi-Sugeno...
A novel control algorithm - fuzzy generalized predictive control (FGPC) algorithm for nonlinear systems is proposed. Fuzzy Gustafon-Kessel algorithm and weighted recursive least-square method are applied to identify the T-S model offline. The identified model is then combined with generalized predictive control algorithm to control non-linear systems. And the detailed steps of the algorithm have been...
To circumvent the drawbacks in nonlinear controller designing of chemical processes, an adaptive feedback-feedforward control scheme is proposed in this paper. A class of nonlinear processes with modest nonlinearities is approximated by a composite model consisting a linear ARX model and a fuzzy neural network-based linearization error model. In addition, the stable analysis is also discussed. Simulation...
The flight simulator is a kind of servo system with uncertainties and disturbances. To obtain high performance and good robustness for the flight simulator, we present an adaptive backstepping controller, which is robust to the parameter uncertainties and load disturbances. First, the model of the flight simulator with parameter uncertainties and load torque disturbances is proposed based on experiment...
State estimation has been favored in many studies. In many control design methods such as state feedback and optimal regulators state variables are needed but very often they are not available at the output of the plant and should be estimated. Also many fault detection and isolation (FDI) techniques use state variables to determine fault kind and location. There exist varies observers for using in...
This study investigates the existence of stabilizing switching rules among a class of unstable nonlinear systems. A high-order checking condition and an associated stabilizing switching rule are presented to implement the stabilizability of the switched systems. An illustrative example is also given to demonstrate the usage and the benefits of the proposed scheme
Chaos control refers to any form of manipulation of chaotic dynamical behavior exhibited by complex nonlinear systems. A chaotic system in general cannot be made to converge to a freely evolving desired trajectory, whether periodic or chaotic, because of the inherent unpredictability of the system. The control of chaos in this context consists of forcing the system to evolve along the desired trajectory...
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