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This paper is concerned with approximately solving the optimal predictor-feedback control problem of multiplicative-noise systems with input delay in infinite horizon. The optimal predictor-feedback control, provided by the analytical method, is determined by Riccati–ZXL equations and is hard to obtain in the case of unknown system dynamics. We aim to propose a policy iteration (PI) algorithm for solving the optimal solution by approximate dynamic programming. For convergence analysis of the algorithm, we first develop a necessary and sufficient stabilizing condition, in the form of several new Lyapunov-type equations, which parameterizes all predictor-feedback controllers and can be seen as an important addition to Lyapunov stability theory. We then propose an iterative scheme for the Riccati–ZXL equations computations, along with convergence analysis, based on the condition. Inspired by this scheme, a data-driven online PI algorithm, convergence implied in that of the iterative scheme, is proposed for the optimal predictor-feedback control problem without full system dynamics. Finally, a numerical example is used to evaluate the proposed PI algorithm.

An energy provider faced with energy generation risks and a large homogeneous pool of customers designs its energy price as a time-varying function of a risk-related quantile of the total energy demand, which generalizes pricing through the mean of the total energy demand. In the infinite population limit, we model the pricing problem with a class of linear quadratic Gaussian quantilized mean field games. For these quantilized mean field games, we show existence and uniqueness of an equilibrium which reveals the price trajectory, as well as an approximate Nash property when the quantilized mean field game’s feedback control functions are applied to the large but finite game and the rate of convergence of the Nash deviation to zero as a function of the population size and the quantile is provided. Finally, the use of this class of quantilized mean field games is illustrated in the context of equivalent thermal parameter models for households heater and an energy provider using solar generation.

This paper addresses a resilient bipartite output consensus issue for high-order heterogeneous multi-agent systems (MASs) with Byzantine attacks. Output regulator equations as well observers are introduced for bipartite leader-following issue with heterogeneous dynamics. For the security concerns, a multidimensional-bipartite-absolute-mean-subsequence-reduced (MBA-MSR) algorithm is developed for each component of received information from neighbors. This algorithm sorts and trims a set of structures, which judge the absolute value of the difference between the output of the current follower observer and that of the signed neighbor observer. It is able to exclude bounded paralyzed signals as the graph is strongly robust. Based on the filtered information via the algorithm, a resilient adaptive observer is designed to for individual follower. A resilient control strategy is then proposed for the MAS to achieve bipartite output consensus under $f$-local/total attack. For reduction of number of sort, a conditional MBA-MSR (CMBA-MSR) algorithm is also developed. Finally, simulation examples are given to illustrate the effectiveness of the theoretical results.

This paper develops a distributed primal–dual algorithm via an event-triggered mechanism to solve a class of convex optimization problems subject to local set constraints, coupled equality and inequality constraints. Different from some existing distributed algorithms with the diminishing step-sizes, our algorithm uses the constant step-sizes, and is shown to achieve an exact convergence to an optimal solution with an ergodic convergence rate of $O(1/k)$ for general convex objective functions, where $k>0$ is the iteration number. Based on the event-triggered communication mechanism, the proposed algorithm can effectively reduce the communication cost without sacrificing the convergence rate. Finally, a numerical example is presented to verify the effectiveness of the proposed algorithm.

Output reference tracking of unknown nonlinear systems is considered. The control objective is exact tracking in predefined finite time, while in the transient phase the tracking error evolves within a prescribed boundary. To achieve this, a novel high-gain feedback controller is developed that is similar to, but extends, existing high-gain feedback controllers. Feasibility and functioning of the proposed controller is proven rigorously. Examples for the particular control objective under consideration are, for instance, linking up two train sections, or docking of spaceships.

A class of homogeneous systems with sector nonlinearities is considered in the paper. Sufficient conditions of finite-time (input-to-state) stability are established with the use of new constructive modification of the Implicit Lyapunov Function approach. The proposed conditions are given in the form of linear matrix inequalities. The theoretical results are supported with numerical examples.

In all the references on stochastic fixed-time stability, the customary treatment of the stochastic noise in the worst-case sense is that it is treated as the unfavorable factor for system stability. Consequently, stochastic fixed-time Lyapunov-type conditions are rather restrictive. Realizing this limitation, we revisit stochastic fixed-time stability and present a generalized fixed-time stability theorem for stochastic differential equations. This theorem completely removes the assumption in these references that the differential operator of the Lyapunov function must be strictly negative, and reveals a positive role of the stochastic noise in stochastic fixed-time stability. As the application of this theorem and its corollary, we solve the problem of fixed-time stabilization for stochastic nonlinear systems with high-order and low-order nonlinearities.

Given a fixed simulation budget, the problem of selecting the best and top-$m$ alternatives among a finite set of alternatives have been studied separately in simulation optimization literature, because the existing sampling procedures are often dedicated to one problem. Under a Bayesian framework, we formulate the top-$m$ selection into a stochastic dynamic program, and characterize the optimal sampling policy via Bellman equations. To determine sequential sampling decisions, we measure the expected marginal improvement from obtaining one additional simulation observation based on predictive distributions, and then develop two cheaply computational approximations to the improvement, thereby yielding two generic sampling procedures that are efficient in selecting top-$m$ alternative(s). The two procedures are proved to be consistent, in a sense that the best and top-$m$ alternatives can be correctly identified as the simulation budget goes to infinity. Numerical experiments on synthetic problems and a coronavirus transmission control application are conducted to demonstrate the efficiency and generality of our procedures.

In this paper, the problem of online distributed optimization with stochastic and nonconvex objective functions is studied by employing a multi-agent system. When making decisions, each agent only has access to a noisy gradient of its own objective function in the previous time and can only communicate with its immediate neighbors via a time-varying digraph. To handle this problem, an online distributed stochastic projection-free algorithm is proposed. Of particular interest is that the dynamic regrets are employed to measure the performance of the online algorithm. Existing works on online distributed algorithms involving stochastic gradients only provide the sublinearity results of regrets in expectation. Different from them, we study the high probability bounds of dynamic regrets, i.e., the sublinear bounds of dynamic regrets are characterized by the natural logarithm of the failure probability’s inverse. Under mild assumptions on the graph and objective functions, we prove that if the variations in both the objective function sequence and its gradient sequence grow within a certain rate, then the high probability bounds of the dynamic regrets grow sublinearly. Finally, a simulation example is carried out to demonstrate the effectiveness of our theoretical results.

This paper studies the optimal cooperative output regulation problem for unknown linear multi-agent systems by the integral reinforcement learning technique. Existing results on this problem were obtained by a non-fully distributed learning process. In contrast, we propose a distributed learning algorithm over the jointly connected switching communication networks. Moreover, by modifying the existing algorithm, we reduce the computational cost and weaken the solvability conditions. Two numerical examples are used to illustrate the effectiveness of our approach.