Most of the greenhouse climate control equipments in China are on-off control and without displacement ***,modeling and control theories based on continuous control devices are not *** the other hand,conventional on-o...
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Most of the greenhouse climate control equipments in China are on-off control and without displacement ***,modeling and control theories based on continuous control devices are not *** the other hand,conventional on-off control algorithms require frequent switch actions of equipments,which leads to equipment spoilage and energy *** address these issues,this paper proposes to control greenhouse climate using a hybrid system *** the interactive dynamics between the actions of ventilation window and the output of the control system（temperature）according to the value of inside temperature and operational constrains,the ventilation window temperature system in a greenhouse can be regarded as a hybrid dynamical *** addition to building models of greenhouse temperature,a model predictive control（MFC）algorithm of greenhouse air temperature is proposed for the mixed logical dynamical *** results in a greenhouse prove the effectiveness of our modeling and control algorithms.
In this paper, the problems of robust stability and stabilization for singular Markovian jump systems with uncertainties are considered. In the system, the uncertainties are unknown time-varying but norm bounded. The ...
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In this paper, the problems of robust stability and stabilization for singular Markovian jump systems with uncertainties are considered. In the system, the uncertainties are unknown time-varying but norm bounded. The system is transformed into an equivalent one by premultiplying and postmultiplying matrices. Sufficient conditions are given and the controllers are designed such that the system is regular, impulse-free and stochastically stable in mean square sense for the uncertainty. Finally, numerical examples are given to demonstrate the effectiveness of the proposed methods.
In this paper, a modular approach is proposed for a class of strict-feedback stochastic nonlinear systems with uncertain Wiener noises and constant unknown parameters. Both the adaptive Backstepping procedure and inpu...
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In this paper, a modular approach is proposed for a class of strict-feedback stochastic nonlinear systems with uncertain Wiener noises and constant unknown parameters. Both the adaptive Backstepping procedure and input-to-state stable(ISS) controller of global stabilization in probability are designed to guarantee that the system states are bounded and has adaptive stabilization while the covariance of Wiener noises is uncertain. According to Swapping technique, we develop two filters and convert dynamic parametric models into static ones to which the gradient update law is designed.
In this paper, a modular design approach of adaptive tracking is proposed for parameter-strict-feedback stochastic nonlinear systems with standard Wiener noises and constant unknown parameters. Both the adaptive backs...
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In this paper, a modular design approach of adaptive tracking is proposed for parameter-strict-feedback stochastic nonlinear systems with standard Wiener noises and constant unknown parameters. Both the adaptive backstepping procedure and input-to-state stable (ISS) controller of global stabilization in probability are designed separately to ensure the output-feedback tracking can be achieved. According to swapping technique, we develop two filters and convert dynamic parametric models into a static one to which the gradient update law is chosen. The transient performance shows the tracking error is bounded.
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