A novel hybrid optimization algorithm, PSOCCA, based on the fusion of the Particle Swarm Optimization (PSO) and Cultural Algorithm (CA), is proposed in this paper to enhance the convergence characteristics of the orig...
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Using the known periodicity of the given trajectory, we develop a new dynamical linearization method by introducing a concept of PPD, then present a new model-free periodic adaptive control approach (MFPAC) and its ex...
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A control method for small domestic power plants using renewable energy is described in this paper. This method is not only capable of optimizing the working point of the plant but also implements active power factor ...
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A second-order sliding mode control scheme is developed to stabilize a class of linear uncertain fractiona-order dynamics. After making a suitable transformation that simplifies the sliding manifold design, a chatteri...
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In this paper, a discrete adaptive ILC is presented for a time-varying nonlinear system with nonparametric uncertainties. A novel estimation of nonparametric uncertainties is constructed just using the past I/O data, ...
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ISBN:
(纸本)9787894631046
In this paper, a discrete adaptive ILC is presented for a time-varying nonlinear system with nonparametric uncertainties. A novel estimation of nonparametric uncertainties is constructed just using the past I/O data, and the uncertainties are completely compensated such that the output tracking error is only affected by external disturbance. As a main contribution of this paper, all the discussions are done with the random initial condition and the iteration-varying target trajectories. Both the rigorous mathematical analysis and the simulation results illustrate the correctness and effectiveness of the proposed approach.
A simple dynamic model of an industrial size synchronous generator operating in a nuclear power plant is developed in this paper based on first engineering principles. The constructed state-space model consists of a n...
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ISBN:
(纸本)9788070438657
A simple dynamic model of an industrial size synchronous generator operating in a nuclear power plant is developed in this paper based on first engineering principles. The constructed state-space model consists of a nonlinear state equation and a bi-linear output equation. It has been shown that the model is locally asymptotically stable with parameters obtained from the literature for a similar generator. The effect of load disturbances on the partially controlled generator has been analyzed by simulation using a traditional PI controller. It has been found that the controlled system is stable and can follow the set-point changes in the effective power well. The disturbance rejection of the controller is also satisfactory.
Complex chemical reaction networks often exhibit different dynamic behaviour on different time scales. A combined approach is proposed in this work for determining physically meaningful mass action realizations of com...
Complex chemical reaction networks often exhibit different dynamic behaviour on different time scales. A combined approach is proposed in this work for determining physically meaningful mass action realizations of complex chemical reaction networks that describe its dynamic behaviour on different time scales. This is achieved by appropriately reducing the detailed overall mass action kinetic scheme using quasi steady state assumptions fit to the particular time scale, and then searching for an optimal realization using mixed integer linear programing. Furthermore, the relationship between the properties (reversibility, deficiency, stability) of the obtained realizations of the same system on different time scales are also investigated and related to the same properties of the detailed overall model. It is shown that the reduced models obtained by quasi steady state assumptions may show exotic nonlinear behaviour, such as oscillations, when the original detailed is globally asymptotically stable. The proposed methods are illustrated by using a simple Michaelis-Menten type reaction kinetic example. The simplified versions of the well known Brusselator model have also been investigated and presented as a case study.
This work studies the identification and control of circulation fluidized bed (CFB) boilers. The CFB boiler under investigation shows strong nonlinearity due to big changes of steam load. A linear parameter varying (L...
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This work studies the identification and control of circulation fluidized bed (CFB) boilers. The CFB boiler under investigation shows strong nonlinearity due to big changes of steam load. A linear parameter varying (LPV) model is used to represent the process dynamics and used in control. The steam flow is used as the working-point variable (scheduling variable) of LPV model. Multivariable plant tests were carried out and a LPV model and a linear model were identified. The identification results of the industrial CFB boiler show that the LPV model has much higher accuracy than the linear model. The simulation studies for the MPC control of the CFB boiler have been performed using both the LPV model and the linear model. The simulation results show that the MPC using the LPV model performs better than the MPC using the linear model.
In atomic force microscopy (AFM) the imaging speed is strongly limited by the bandwidth of the feedback loop that controls the interaction between the measurement tip and the sample. A significant increase in closed-l...
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