Search

This paper addresses the problem of global adaptive stabilization by output-feedback for a class of uncertain nonlinear systems with unknown growth rate and unknown output function. By constructing a suitable Lyapunov function, a new systematic design scheme is proposed to derive an adaptive output-feedback controller with appropriate design parameters based on a dynamic high-gain, under which the system states can be globally regulated to zero. Because the proposed method uses the adaptive technique rather than the time-varying one, it does not require a priori known information about either the growth rate or the upper and lower bounds of the derivative of the unknown output function. This makes the investigated system substantially different from the existing results and also highlights the main contributions of the paper. A practical example is presented to show the validity of the theoretical results.

This paper studies the H ∞ filtering problem for a class of discrete-time two-dimensional (2-D) switched systems. The 2-D systems under consideration are described by the well-known Fornasini–Marchesini local state-space model. Our attention is focused on designing a full-order filter such that the filtering error system is guaranteed to be asymptotically or exponentially stable with a prescribed H ∞ disturbance attenuation level. Based on the switched quadratic Lyapunov function and the piecewise Lyapunov function approach, sufficient conditions are established to ensure the existence of the desired filters for such systems under the arbitrary switching and restricted switching signals, respectively. Furthermore, the notion of time instant for 2-D switched systems is introduced and the extended average dwell time technique is utilized under the restricted switching signal. Then the corresponding filter is designed by dealing with a convex optimization problem that can be efficiently solved via standard numerical algorithms. Finally, two numerical examples are provided to demonstrate the effectiveness of the developed filter design algorithms.

This paper investigates the periodic event-triggered robust output feedback control problem for a class of nonlinear uncertain systems subject to time-varying disturbance. By means of the feedback domination approach and disturbance compensation technique, a new framework of periodic event-triggered robust control strategy is developed in the form of output feedback, which encompasses a discrete-time event-triggering transmission scheme that is only intermittently monitored at sampling instants and a discrete-time output feedback controller consisting of a set of linear difference equations. The proposed robust method may reduce the communication resource utilization as compared to the non-event triggering schemes while maintaining a desirable closed-loop system performance even in the presence of a general class of time-varying disturbance and nonlinear uncertainties. The closed-loop system under the proposed control scheme is actually modeled as a hybrid system, and it is shown that the global practical stability of the closed-loop hybrid system is guaranteed by selecting a sufficiently large scaling gain and a sufficiently small sampling period. Finally, the experimental results on a DC–DC buck power converter are presented to illustrate the effectiveness of the proposed control approaches.

This paper is concerned with the network-based practical set consensus problem of multi-agent systems subject to input saturation constraints. Considering network-induced delays, data quantization and aperiodic sampling intervals, a network-based framework which allows each agent to be remotely controlled over the communication network is established. Under this framework, a new network-based consensus protocol with input saturation constraints is proposed. With this protocol, the consensus problem can be transformed into the stabilization problem of time-delay systems with bounded perturbations. By using the Lyapunov–Krasovskii approach, a stability condition guaranteeing that the error system can exponentially converge to a bounded set is derived, where the region of initial conditions can be estimated by considering the effect of the first delay interval. Based on this stability condition, the network-based consensus controller gain matrix can be obtained. An optimization algorithm is introduced for simultaneously designing the consensus controller gain and estimating the region of attraction as small as possible and the region of initial conditions as large as possible. A numerical example is given to illustrate the efficiency of all the results derived in this paper.

In this paper, a class of optimization problems is investigated, where the objective function is the sum of N convex functions viewed as local functions and the constraints are N nonidentical closed convex sets . Additionally, it is aimed to solve the considered optimization problem in a distributed manner and thus a sequence of time-varying unbalanced directed graphs is introduced first to depict the information connection topologies. Then, the novel push-sum based constrained optimization algorithm (PSCOA) is developed, where the new gradient descent-like method is applied to settle the involved closed convex set constraints. Furthermore, the rigorous convergence analysis is shown under some standard and common assumptions and it is proved that the developed distributed discrete-time algorithm owns a convergence rate of O ( ln t t ) in general case. Specially, the convergence rate of O ( 1 t ) can be further obtained under the assumption that at least one objective function is strongly convex. Finally, simulation results are given to demonstrate the validity of the theoretical results.

In this paper, the finite-time control problem for a class of double integrator systems under state constraints is investigated. Different from the traditional finite-time control methods with constant gains, an improved finite-time controller with state-dependent gains is designed. The main challenges lie in the finite-time stability analysis of the resultant closed-loop system caused by state-dependent control gains and how to make the system states keep in the constrained region. Based on the homogeneous system theory, the finite-time stability of the closed-loop system under state constraints is proved. In addition to the finite-time convergence, it is also shown that the states of the system always are kept in the constrained region. Finally, the developed finite-time controller is applied to buck converter and the validity of the proposed method is verified through a DC–DC buck converter under voltage and current constraints.

Due to its potential applications in robotics and navigation, recent years have witnessed some progress in Kalman filter (KF) with norm constraints on the state. A noticeable discovery of the existing literature is that the KF gain has an analytical expression, and the brute-force normalization (i.e., estimation without considering the norm constraints, followed by a normalization operation) is optimal in the mean-square sense. Although there are some extensions of the former works to situations with uncertainties, existing results are only limited to cases where models/bounds or statistical properties of the disturbances are known. The paper considers the design of KF for systems subject to norm constraints on the state and unknown inputs, whose models or statistical properties are not assumed to be available. Both cases with and without direct feedthrough will be discussed. For systems without direct feedthrough, we show that the KF gain can be derived explicitly and the brute-force normalization is optimal in the mean-square sense, thereby generalizing the above-mentioned works on norm-constrained KF. However, for the case with direct feedthrough, the KF gain does not seem to admit an analytical solution, as to be shown via a counterexample .