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961.
This paper concerns the convex optimal control problem governed by multiscale elliptic equations with arbitrarily rough $L^\infty$ coefficients, which has not only complex coupling between nonseparable scales and nonlinearity, but also important applications in composite materials and geophysics. We use one of the recently developed numerical homogenization techniques, the so-called Rough Polyharmonic Splines (RPS) and its generalization (GRPS) for the efficient resolution of the elliptic operator on the coarse scale. Those methods have optimal convergence rate which do not rely on the regularity of the coefficients nor the concepts of scale-separation or periodicity. As the iterative solution of the nonlinearly coupled OCP-OPT formulation for the optimal control problem requires solving the corresponding (state and co-state) multiscale elliptic equations many times with different right hand sides, numerical homogenization approach only requires one-time pre-computation on the fine scale and the following iterations can be done with computational cost proportional to coarse degrees of freedom. Numerical experiments are presented to validate the theoretical analysis. 相似文献
962.
Inez Maria Zwetsloot Tahir Mahmood Funmilola Mary Taiwo Zezhong Wang 《商业与工业应用随机模型》2023,39(6):789-817
Early detection of changes in the frequency of events is an important task in many fields, such as disease surveillance, monitoring of high-quality processes, reliability monitoring, and public health. This article focuses on detecting changes in multivariate event data by monitoring the time-between-events (TBE). Existing multivariate TBE charts are limited because they only signal after an event occurred for each of the individual processes. This results in delays (i.e., long time-to-signal), especially when we are interested in detecting a change in one or a few processes with different rates. We propose a bivariate TBE chart, which can signal in real-time. We derive analytical expressions for the control limits and average time-to-signal performance, conduct a performance evaluation and compare our chart to an existing method. Our findings showed that our method is an effective approach for monitoring bivariate TBE data and has better detection ability than the existing method under transient shifts and is more generally applicable. A significant benefit of our method is that it signals in real-time and that the control limits are based on analytical expressions. The proposed method is implemented on two real-life datasets from reliability and health surveillance. 相似文献
963.
This work mainly addresses terminal constrained robust hybrid iterative learning model predictive control against time delay and uncertainties in a class of complex batch processes with input and output constraints. In this work, an equivalently novel extended two-dimensional switched system is first constructed to represent the process model by introducing state difference, output error and new relaxation variable information. Then, a hybrid predictive updating controller is proposed and an optimal performance index function including terminal constraints is designed. Under the condition that the switching signal meets certain conditions, the solvable problem of model predictive control is realized by Lyapunov stability theory. Meanwhile, the design scheme of controller parameters is also given. In addition, the robust constraint set is adopted to overcome the disadvantage that the traditional asymptotic stability cannot converge to the origin when it involves disturbances, such that the system state converges to the constraint set and meets its expected value. Finally, the effectiveness of the proposed algorithm is verified by controlling the speed and pressure parameters of the injection molding process. 相似文献
964.
This paper investigates a resources-limited situation in the event-triggered model predictive control (ETMPC) for continuous-time nonlinear system with first-order hold fashion. In consideration of limited bandwidth in data transmission through wireless network under actual operation, our strategy divides the prediction horizon, and applies linear interpolation instead of zero-order hold fashion to obtain a better system performance, so that the reduction of resources and the optimization of strategy can be guaranteed. Furthermore, in actual industry processes, quadratic cost function cannot be implemented in all operations, then general cost function is adopted in this paper. Based on the first-order hold method and general cost function, the feasibility of the ETMPC algorithm and the stability of dynamical systems are analyzed. At last, a practical example is given to show the advantages of our method. 相似文献
965.
Impulsive control systems are suitable to describe and control a venue of real-life problems, going from disease treatment to aerospace guidance. The main characteristic of such systems is that they evolve freely in-between impulsive actions, which makes it difficult to guarantee its permanence in a given state-space region. In this work, we develop a method for characterizing and computing approximations to the maximal control invariant sets for linear impulsive control systems, which can be explicitly used to formulate a set-based model predictive controller. We approach this task using a tractable and non-conservative characterization of the admissible state sets, namely the states whose free response remains within given constraints, emerging from a spectrahedron representation of such sets for systems with rational eigenvalues. The so-obtained impulsive control invariant set is then explicitly used as a terminal set of a predictive controller, which guarantees the feasibly asymptotic convergence to a target set containing the invariant set. Necessary conditions under which an arbitrary target set contains an impulsive control invariant set (and moreover, an impulsive control equilibrium set) are also provided, while the controller performance are tested by means of two simulation examples. 相似文献
966.
Moving Target Defense (MTD) prevents adversaries from being able to predict the effect of their attacks by adding uncertainty in the state of a system during runtime. In this paper, we present an MTD algorithm that randomly changes the availability of the sensor data, so that it is difficult for adversaries to tailor stealthy attacks while, at the same time, minimizing the impact of false-data injection attacks. Using tools from the design of state estimators, namely, observers, and switched systems, we formulate an optimization problem to find the probability of the switching signals that increase the visibility of stealthy attacks while decreasing the deviation caused by false data injection attacks. We show that the proposed MTD algorithm can be designed to guarantee the stability of the closed-loop system with desired performance. In addition, we formulate an optimization problem for the design of the parameters so as to minimize the impact of the attacks. The results are illustrated in two case studies, one about a generic linear time-invariant system and another about a vehicular platooning problem. 相似文献
967.
This paper studies the stability problem of two-time-scale system via event-triggered impulsive control and self-triggered impulsive control. The overall system is modeled with the hybrid formalism. Two Chang transformations are introduced to completely decouple the hybrid system states into flow set and jump set. A composite impulsive controller based on slow and fast system states is proposed, under which the slow and fast subsystems are simultaneously triggered by event-triggered and self-triggered mechanism, respectively. As a result, the stability conditions are derived for the system under event-triggered and self-triggered impulsive control, respectively. Furthermore, the theoretical result of self-triggered impulsive control is applied to the consensus of the interconnected two-time-scale systems. Finally, simulation examples and comparison study show the effectiveness of the proposed control strategies. 相似文献
968.
In this paper, the exponential stability problem is addressed for a class of cyclic switched nonlinear systems with unstable modes. A novel event-triggered cyclic switching scheme (ETCSS) is proposed to generate a cyclic switching signal that exponentially stabilizes the considered system for any given initial configuration. Some easily verifiable stability criteria for switched nonlinear and linear systems are established, and the effects of event parameters on the convergence rate are also analyzed. Different from the existing studies, here the developed switching scheme is not only cyclic but also event-triggered and thereby the switching frequency is relatively low. Finally, a numerical example is given to verify the efficiency of the method. 相似文献
969.
This paper is concerned with stabilization of hybrid neural networks by intermittent control based on continuous or discrete-time state observations. By means of exponential martingale inequality and the ergodic property of the Markov chain, we establish a sufficient stability criterion on hybrid neural networks by intermittent control based on continuous-time state observations. Meantime, by M-matrix theory and comparison method, we show that hybrid neural networks can be stabilized by intermittent control based on discrete-time state observations. Finally, two examples are presented to illustrate our theory. 相似文献
970.
In this work we consider a poroelastic, flexible material that may deform largely, which is situated in an incompressible fluid driven by the Navier–Stokes equations in two or three space dimensions. By a variational approach we show existence of weak solutions for a class of such coupled systems. We consider the unsteady case, this means that the PDE for the poroelastic solid involves the Fréchet-derivative of a non-convex functional as well as (second order in time) inertia terms. 相似文献