Mean-square stability for discrete bilinear systems in Hilbert space

A discrete system is uniformly mean-square stable if uniform convergence is preserved between input and state correlation sequences, and if nuclearity is preserved between the input and state correlation limits. This paper supplies a contraction-free condition ensuring mean-square stability for infinite-dimensional bilinear systems evolving in a separable Hilbert space.     

Decoupling the linear and nonlinear parts in Hammerstein model identification

The main result of this paper is to show that the linear part can be made decoupled from the nonlinear part in Hammerstein model identification. Therefore, identification of the linear part for a Hammerstein model becomes a linear problem and accordingly enjoys the same convergence and consistency results as if the unknown nonlinearity is absent.     

Distributed consensus over digital networks with limited bandwidth and time-varying topologies

In this paper, we consider discrete-time distributed average-consensus with limited communication data-rate and time-varying communication topologies. We design a distributed encoding-decoding scheme based on quantization of scaled innovations and a control protocol based on a symmetric compensation method. We develop an adaptive scheme to select the numbers of quantization levels according to whether the associated channel is active or not. We prove that if the network is jointly connected, then under the protocol designed, average-consensus can be asymptotically achieved, and the convergence rate is quantified. Especially, if the duration of any link failure in the network is bounded, then the control gain and the scaling function can be selected properly such that 5-level quantizers suffice for asymptotic average-consensus with an exponential convergence rate.     

Parameter convergence and minimal internal model with an adaptive output regulation problem

The parameter convergence of nonlinear adaptive control systems is an important yet not well addressed issue. In this paper, using the global robust output regulation problem of output feedback systems with unknown exosystems as a platform, we will show that the well known persistency of excitation (PE) condition still guarantees the convergence of the estimated parameter vector to the true parameter vector. Moreover, the PE condition will be satisfied if the internal model is minimal in certain sense.     

Finite-time consensus for multi-agent networks with unknown inherent nonlinear dynamics

The objective of this paper is to analyze the finite-time convergence of a nonlinear but continuous consensus algorithm for multi-agent networks with unknown inherent nonlinear dynamics. Due to the existence of the unknown inherent nonlinear dynamics, the stability analysis and the finite-time convergence analysis are more challenging than those under the well-studied consensus algorithms for known linear systems. For this purpose, we propose a novel comparison based tool. By using this tool, it is shown that the proposed nonlinear consensus algorithm can guarantee finite-time convergence if the directed switching interaction graph has a directed spanning tree at each time interval. Specifically, the finite-time convergence is shown by comparing the closed-loop system under the proposed consensus algorithm with some well-designed closed-loop system whose stability properties are easier to obtain. Moreover, the stability and the finite-time convergence of the closed-loop system using the proposed consensus algorithm under a (general) directed switching interaction graph can even be guaranteed by the stability and the finite-time convergence of some well-designed nonlinear closed-loop system under some special directed switching interaction graph. This provides a stimulating example for the potential applications of the proposed comparison based tool in the stability analysis of linear/nonlinear closed-loop systems by making use of known results in linear/nonlinear systems.     

Consensus of single integrator multi-agent systems with directed topology and communication delays

In this paper, a distributed control scheme has been developed for consensus of single integrator multi-agent systems with directed fixed communication topology for arbitrarily large constant, time-varying or distributed communication delays. It is proved that the closed loop control system can reach consensus with an exponential convergence rate if and only if the topology is quasi-strongly connected. Simulation results are also provided to demonstrate the effectiveness of the proposed controller.     

Robust convergence of discrete‐time delayed switched nonlinear systems and its applications to cascade systems

Systems in real world are always subject to perturbations. This paper addresses the convergence properties of a class of nominal systems with perturbations. It is assumed that the nominal system is a switched nonlinear exponentially stable one with time‐varying delays and that the perturbation exponentially or asymptotically converges to zero. It is revealed that the trajectories of the perturbed system behave as the perturbation, ie, trajectories exponentially or asymptotically converge to zero, depending on the property of perturbation. Applying these results to cascade switched nonlinear systems, it is shown that such systems are exponentially stable if and only if all the subsystems, obtained by removing the coupling terms, are exponentially stable. A similar conclusion is presented for systems being asymptotically stable. Finally, a numerical example illustrates the proposed theoretical results.     

State estimation for linear hybrid systems with periodic jumps and unknown inputs

In order to solve the state estimation problem for linear hybrid systems with periodic jumps and unknown inputs, some hybrid observers are proposed. The proposed observers admit a Luenberger‐like structure and the synthesis is given in terms of linear matrix inequalities (LMIs). Therefore, the proposed observer designs are completely constructive and provide some input‐to‐state stability properties with respect to unknown inputs. It is worth mentioning that the structure of the hybrid observers, as well as the structure of the LMIs, depends on some observability properties of the flow and jump dynamics, respectively. Then, in order to compensate the effect of the unknown inputs, a hybrid sliding‐mode observer is added to the Luenberger‐like observer structure, providing exponential convergence to zero of the state estimation error despite certain class of unknown inputs. The existence of the hybrid observers and the unknown input hybrid observer is guaranteed if and only if the hybrid system is observable and strongly observable, respectively. Some numerical examples illustrate the feasibility of the proposed estimation approach.     

Control design for chained-form systems with bounded inputs

Discontinuous, time-invariant controllers have been recently proposed in the literature as an alternative method to stabilize nonholonomic systems. These control laws are not continuous at the origin and although they provide exponential rates of convergence, they may use significant amount of control effort, especially if the initial conditions are close to an equilibrium manifold. We seek to remedy this situation by constructing bounded controllers (with exponential convergence rates) for nonholonomic systems in chained form.     

针对随机控制系统数值解的均方指数输入状态稳定性

分析了随机控制系统数值解的均方指数输入状态稳定性. 首先, 针对随机控制系统, 随机θ-方法满足有限时间强收敛条件. 然后, 我们证实, 在有限时间强收敛条件下, 随机控制系统是均方指数输入状态稳定的当且仅当随机θ-方法(充分小步长)是均方指数输入状态稳定的. 另外, 对一类满足单边Lipschitz条件的随机控制系统, 有两类隐式欧拉方法(对任意步长)能够继承原系统的均方指数输入状态稳定性. 最后, 一些数值实例证实了本文所获结论的正确性.     

Discontinuous control of nonholonomic systems

The problem of asymptotic convergence for a class of nonholonomic control systems via discontinuous control is addressed and solved from a new point of view. It is shown that control laws, which ensures asymptotic (exponential) convergence of the closed-loop system, can be easily designed if the system is described in proper coordinates. In such coordinates, the system is discontinuous. Hence, the problem of local asymptotic stabilization for a class of discontinuous nonholonomic control systems is dealt with and a general procedure to transform a continuous system into a discontinuous one is presented. Moreover, a general strategy to design discontinuous control laws, yielding asymptotic convergence, for a class of nonholonomic control systems is proposed. The enclosed simulation results show the effectiveness of the method.     

Acceleration of finite‐time stable homogeneous systems

Stabilization rates of power‐integrator chains are easily regulated. It provides a framework for acceleration of uncertain multiple‐input–multiple‐output dynamic systems of known relative degrees (RDs). The desired rate of the output stabilization (sliding‐mode control) is ensured for an uncertain system if its RD is known, and a rough approximation of the high‐frequency gain matrix is available. The uniformly bounded convergence time (fixed‐time stability) is obtained as a particular case. The control can be kept continuous everywhere except the sliding‐mode set if the partial RDs are equal. Similarly, uncertain smooth systems of complete multiple‐input–multiple‐output RDs (ie, lacking zero dynamics) are stabilized by continuous control at their equilibria in finite time and are accelerated. Output‐feedback controllers are constructed. Computer simulation demonstrates the efficiency of the proposed approach.     

Convergence properties of constrained linear system under MPC control law using affine disturbance feedback

This paper shows new convergence properties of constrained linear discrete time system with bounded disturbances under Model Predictive Control (MPC) law. The MPC control law is obtained using an affine disturbance feedback parametrization with an additional linear state feedback term. This parametrization has the same representative ability as some recent disturbance feedback parametrization, but its choice together with an appropriate cost function results in a different closed-loop convergence property. More exactly, the state of the closed-loop system converges to a minimal invariant set with probability one. Deterministic convergence to the same minimal invariant set is also possible if a less intuitive cost function is used. Numerical experiments are provided that validate the results.     

Rational approximation of a class of infinite-dimensional systems II: Optimal convergence rates ofL ∞ approximants

The achievable errors in infinity-norm approximation of an irrational transfer function by a rational one of given degree are considered. Error bounds are given which have particular application to delay systems, and it is shown that optimal convergence rates are achievable if the corresponding impulse response has certain smoothness properties.     

Reach a nonlinear consensus for MAS via doubly stochastic quadratic operators

This technical note addresses the new nonlinear protocol class of doubly stochastic quadratic operators (DSQOs) for coordination of consensus problem in multi-agent systems (MAS). We derive the conditions for ensuring that every agent reaches consensus on a desired rate of the group's decision where the group decision value in its agent's initial statuses varies. Besides that, we investigate a nonlinear protocol sub-class of extreme DSQO (EDSQO) to reach a consensus for MAS to a common value with nonlinear low-complexity rules and fast time convergence if the interactions for each agent are not selfish. In addition, to extend the results to reach a consensus and to avoid the selfish case we specify a general class of DSQO for reaching a consensus under any given case of initial states. The case that MAS reach a consensus by DSQO is if each member of the agent group has positive interactions of DSQO (PDSQO) with the others. The convergence of both EDSQO and PDSQO classes is found to be directed towards the centre point. Finally, experimental simulations are given to support the analysis from theoretical aspect.     

Stochastic adaptive control using a modified least squares algorithm

Recent papers on stochastic adaptive control have established global convergence for algorithms using a stochastic approximation iteration. However, to date, global convergence has not been established for algorithms incorporating a least squares iteration. This paper establishes global convergence for a slightly modified least squares stochastic adaptive control algorithm. It is shown that, with probability one, the algorithm will ensure that the system inputs and outputs are sample mean square bounded and the mean square output tracking error achieves its global minimum possible value for linear feedback control.     

Moving formation convergence of a group of mobile robots via decentralised information feedback

In this article, a decentralised information feedback mechanism is introduced to a group of mobile robots such that the robots asymptotically converge to a given moving formation. It is assumed that the robots can exchange only position information according to a pre-specified communication graph. Each node represents a robot. Two robots are neighbours of each other if there is an edge between the two nodes. A feedback controller is performed for each robot by only using its own velocity information and the position information from its neighbours. It is proven that if the graph is connected, then the convergence to the moving formation of the closed-loop system is guaranteed. Several numerical simulations are presented to illustrate the results.     

Identification of an additive nonlinear system and its applications in generalized Hammerstein models

An identification algorithm is developed for a class of nonlinear systems that are multi-input and multi-output in an additive form. The convergence results are achieved and its applications to identification of a generalized Hammerstein system is also discussed.     

An approach to multivariable system identification

Two prototype identifiable structures are presented which make possible the identification via an equation-error model reference adaptive system of linear plants with rational transfer function matrices. The structures include as specialisations many of the particular structures presented hitherto in the literature. Convergence properties are also discussed, and several modes of convergence are distinguished: model output to plant output, model transfer function matrix to plant transfer function matrix, and model parameters to plant parameters. Conditions are presented for exponentially fast convergence in the absence of noise.