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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Air pollution contributes to the premature deaths of millions of people each year around the world, and air quality problems are growing in many developing nations. While past policy efforts have succeeded in reducing...
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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.
This paper is concerned with stochastic model predictive control for Markovian jump linear systems with additive disturbance, where the systems are subject to soft constraints on the system state and the disturbance s...
This paper is concerned with stochastic model predictive control for Markovian jump linear systems with additive disturbance, where the systems are subject to soft constraints on the system state and the disturbance sequence is finitely supported with joint cumulative distribution function given. By resorting to the maximal disturbance invariant set of the system, a model predictive control law is given based on a dynamic controller which is with guaranteed recursive feasibility and ensures the probabilistic constraints on the states. By optimizing the volume of the disturbance invariant set, the dynamic controller is given. The closed loop system under this control law is proven to be stable in the mean square sense. Finally, a numerical example is given to illustrate the developed results.
In the brand new era of “big data”, finding creative strategies for information fusion in a complicated system is important. Previous researchers have introduced a model called “Local Strongly Coupled system”, in ...
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In the brand new era of “big data”, finding creative strategies for information fusion in a complicated system is important. Previous researchers have introduced a model called “Local Strongly Coupled system”, in which every agent stochastically communicates only with its neighbours (i.e. “coupled” nodes). However, the meaning of a system's intrinsic a priori constraints is always neglected. And we assume that in practical instances, the whole system is supposed to take on a consistent result, or “consensus” decision. This paper, taking constraints into account, presents a new strategy to help minimize the filtering error. Furthermore, based on consensus policy, the approach is applied to local strongly coupled systems, especially systems with packet loss. Effectiveness and practicability of all the proposed algorithms are shown through simulations.
This paper considers using reset control to improve transient performance and overcome some fundamental limitations of linear systems. First, an auxiliary system is presented, then, based on which, a new reset control...
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This paper considers using reset control to improve transient performance and overcome some fundamental limitations of linear systems. First, an auxiliary system is presented, then, based on which, a new reset control model is proposed, such that the non-overshoot performance specification can be met for any minimum phase relative degree one plants, the results imply some limitations of linear systems are overcome and clearly illustrate the advantages of reset control. A numerical example is given to show the effectiveness.
This paper addresses the problem of infinite time performance of model predictive controllers applied to constrained nonlinear systems. The total performance is compared with a finite horizon optimal cost to reveal pe...
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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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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 quantification. In the step of hazard identification, the catastrophic functional hazards, derived from the functional hazard analysis (FHA) results for the airplane, are used as the top events to construct the fault trees. The minimal cut sets (MSCs) of the fault trees are the hazards to be identified exactly. In the step of hazard quantification, the probability of one hazard triggered by some uncontained debris is evaluated. After the probabilities of all the identified hazards are quantified, the risk assessment of the airplane is completed. And the assessment result is compared with the design specifications to show compliance with the safety design. A small example is introduced to illustrate the rationality and accuracy of the aforementioned method.
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