This paper mainly discusses the remote tracking problem with partly quantized information and packet-dropout. Since the network exists between the remote plant and the local plant, any information transmitted between ...
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ISBN:
(纸本)9781479947249
This paper mainly discusses the remote tracking problem with partly quantized information and packet-dropout. Since the network exists between the remote plant and the local plant, any information transmitted between each other will experience the quantization errors and may be lost. In this situation, the controller of the local system needs to consider both the exact local information and the inaccurate remote information. A state feedback controller is adopted and the theorems to design such controller are given in terms of bilinear matrix inequalities(BMIs). Moreover, an algorithm is proposed and these BMIs are converted into a convex optimization problem. Finally, the efficiency of the proposed method is demonstrated by a simulation example.
Furnace exit gas temperature(FEGT) is the key parameter in the furnace ash fouling monitoring system. Since the standard least squares support vector machine(LSSVM) is not suitable for online identification and contro...
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ISBN:
(纸本)9781479947249
Furnace exit gas temperature(FEGT) is the key parameter in the furnace ash fouling monitoring system. Since the standard least squares support vector machine(LSSVM) is not suitable for online identification and control of FEGT,a novel CM-LSSVM-PLS method is proposed to predict FEGT in this paper. In the process of CM-LSSVM-PLS method, c-means cluster(CM) algorithm is used to partition the training data into several different subsets by considering the characteristics of operational data. Submodels are subsequently developed in the individual subsets based on LSSVM method. Partial least squares algorithm(PLS) is employed as the combination strategy. The online updating algorithm is then applied to the CM-LSSVM-PLS model. The proposed online model is verified through operation data of a 300 MW generating unit. The simulation results show that the proposed online updating model is effective for online FEGT forecasting.
In this paper, we consider the robust fault tolerant control of the distributed networked controlsystems(DNCSs). In DNCSs, sub-systems are connected with each other through a communication network. Each sub-system ha...
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ISBN:
(纸本)9781479947249
In this paper, we consider the robust fault tolerant control of the distributed networked controlsystems(DNCSs). In DNCSs, sub-systems are connected with each other through a communication network. Each sub-system has its own sensor, controller, actuator and quantizer. The output of each sub-system will be transmitted to all other sub-systems through the network. As a result, quantization errors and packet-dropouts cannot be avoided. We also consider the actuator faults situations, including outage, loss of effectiveness and impulse which is modeled by a Markov chain in this paper. A mode-based static output feedback controller is proposed to stable the DNCSs and to meet the robust H-inf performance. Finally, a simulation example is given to illustrate the effectiveness of the proposed method.
This paper is concerned with the state estimation problem for nonlinear systems with unknown covariance of process noise. The advantages of recently developed High-degree Cubature Kalman Filter (HCKF) are significant ...
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This paper is concerned with the state estimation problem for nonlinear systems with unknown covariance of process noise. The advantages of recently developed High-degree Cubature Kalman Filter (HCKF) are significant with its easy to implement and better estimation accuracy. However, it has bad robustness on modeling uncertainty for practical applications. To overcome the limitations of the HCKF, an Adaptive HCKF (AHCKF) is proposed by combing strong tracking filtering and Sage-Husa estimator. In the proposed state estimator, a fading factor is used to correct one state prediction covariance while the Sage-Husa estimator is adopted to recursively estimate the unknown process noise statistics. Therefore, the AHCKF can obtain better robustness and accuracy comparing with the conventional HCKF. Simulation examples on target tracking are demonstrated the validity of the proposed algorithms.
Brushless direct current (BLDC) motor is widely used in small and medium side electric vehicles as it exhibit highest specific power and thermal efficiency compared with induction motor. The permanent magnets in the r...
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Brushless direct current (BLDC) motor is widely used in small and medium side electric vehicles as it exhibit highest specific power and thermal efficiency compared with induction motor. The permanent magnets in the rotor create a constant magnetic flux, which limit the motor top speed. As the back electromotive force (EMF) voltage increases proportionally with motor rotational speed and it approaches the amplitude of the input voltage, the phase current amplitude will reach zero. By advancing the phase current, it is possible to extend the maximum speed of the BLDC motor beyond the rated top speed. This will allow smaller BLDC motor to be used in small electric vehicles and in larger applications will allow the use of BLDC motor without the use of multispeed transmission unit for high speed operation. Adjusting the phase angle will affect the speed of the motor as the each coil is energized earlier than the corresponding rise in back emf of the coil. Preliminary test results indicated that the motor top speed can be increased at least by 40 percent over the baseline speed at no load condition.
One approach based on AC20-128A is presented in order to assess the risk caused by uncontained engine rotor failure (UERF). In this approach, the risk assessment procedure includes hazard identification and hazard qua...
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In this paper, new Lyapunov-based reset rules are constructed to improve C2 gain performance of linear-time-invariant (LTI) systems. By using the hybrid system framework, sufficient conditions for exponential and fini...
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As one of key technologies in photovoltaic converter control, Maximum Power Point Tracking (MPPT) methods can keep the power conversion efficiency as high as nearly 99% under the uniform solar irradiance condition. Ho...
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This paper designs multi-step probabilistic sets for linear, discrete-time, stochastic systems with unbounded multiplicative noise and probabilistic constraints. Multi-step probabilistic sets strengthen IWPp by bringi...
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This paper designs multi-step probabilistic sets for linear, discrete-time, stochastic systems with unbounded multiplicative noise and probabilistic constraints. Multi-step probabilistic sets strengthen IWPp by bringing more degrees of freedom to optimize the applicable region of finite-step probabilistic constraints, and extending the prediction horizon of IWPp to infinity for infinite-horizon probabilistic constraints. Conditions for multi-step probabilistic sets are then incorporated into a stochastic model predictive control algorithm to satisfy probabilistic constraints. Closed-loop mean-square stability is guaranteed by the algorithm. A numerical example shows the performance of the proposed algorithm.
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