Design of sampled-data critical control systems with persistent/transient inputs using lifting technique

This paper gives a matching condition for sampled-data critical control systems with persistent and/or transient inputs by using the lifting technique. The obtained condition is given as a set of inequality constraints on the impulse and/or step responses. A computational method for evaluating it is also discussed.     

Consensus of multi-agent systems based on sampled-data control

This article studies the consensus problem in directed networks, assuming that each agent is with double-integrator dynamics and only obtains the measurements of its positions relative to its neighbours at sampling instants. We propose a protocol based on sampled-data control and derive an equivalent characterisation of the solvability of the consensus problem under this protocol. In virtue of this equivalent characterisation, we further consider two cases: fixed topology and switching topology. For the first case, we present a set of sampling periods and feedback coefficients which ensure that the protocol can solve a consensus problem. For the second case, we derive sufficient conditions for the protocol to solve a consensus problem under arbitrary switching signals and under a class of switching signals, respectively. Finally, simulations are provided to illustrate the effectiveness of the theoretical results.     

Stabilisation analysis for switched neutral systems based on sampled-data control

In this paper, the problem of stabilisation analysis for switched neutral systems based on sampled-data control and average dwell time approach is investigated. Delay-dependent stabilisation results are derived in terms of linear matrix inequalities by constructing piecewise Lyapunov–Krasovskii functional based on the Wirtinger's inequality. Also, the controller gain matrix is designed by applying an input-delay approach. Further convex combination technique and some integral inequalities are used to derive less conservative results. The effectiveness of the derived results is validated through numerical examples.     

Design and stabilization of sampled-data neural-network-based control systems.

This paper presents the design and stability analysis of a sampled-data neural-network-based control system. A continuous-time nonlinear plant and a sampled-data three-layer fully connected feedforward neural-network-based controller are connected in a closed loop to perform the control task. Stability conditions will be derived to guarantee the closed-loop system stability. Linear-matrix-inequality- and genetic-algorithm-based approaches will be employed to obtain the largest sampling period and the connection weights of the neural network subject to the considerations of the system stability and performance. An application example will be given to illustrate the design procedure and effectiveness of the proposed approach.     

Dissipative based adaptive reliable sampled-data control of time-varying delay systems

This paper is concerned with the problem of dissipative based adaptive reliable controller for a class of time delay systems subject to actuator failures and time-varying sampling with a known upper bound on the sampling intervals. By constructing a proper Lyapunov-Krasovskii functional which fully uses the available information about the actual sampling pattern and time delays, a new set of sufficient conditions is derived to obtain the required result. Then, a dissipative based adaptive sampled-data controller is designed such that the resulting closed-loop system is reliable in the sense that it is asymptotically stable and has the prescribed dissipative performance under given constraints. The existence condition of the desired dissipative based adaptive reliable sampled-data controller is obtained in terms of linear matrix inequalities. Further, the performance of the proposed controller is implemented on a liquid propellant rocket motor with a pressure feeding system model. The simulation results show the effectiveness and better performance of the proposed adaptive reliable sampled-data controller over conventional reliable controller.     

Adaptive sampled-data based linear quadratic optimal control of stochastic systems

The problem of sampled-data (SD) based adaptive linear quadratic (LQ) optimal control is considered for linear stochastic continuous-time systems with unknown parameters and disturbances. To overcome the difficulties caused by the unknown parameters and incompleteness of the state information, and to probe into the influence of sample size on system performance, a cost-biased parameter estimator and an adaptive control design method are presented. Under the assumption that the unknown parameter belongs to a known finite set, some sufficient conditions ensuring the convergence of the parameter estimate are obtained. It is shown that when the sample step size is small, the SD-based adaptive control is LQ optimal for the corresponding discretized system, and sub-optimal compared with that of the case where the parameter is known and the information is complete.     

Output-feedback control of LPV sampled-data systems

In this paper, we address the analysis and the output-feedback synthesis problems for linear parameter-varying (LPV) sampled-data control systems with potentially variable sampling rates. We assume that the state-space matrices of the plant and the sampling interval depend on parameters that are measurable in real-time and vary in a compact set with bounded variation rates. We explore criteria such as the stability, the energy-to-energy gain (induced L 2 norm) and the energy-to-peak gain (induced L 2 -to- L X norm) of such sampled-data LPV systems using parameter-dependent Lyapunov functions. Based on these analysis results, the corresponding sampled-data output-feedback control synthesis problems are examined. Both analysis and synthesis conditions are formulated in terms of linear matrix inequalities (LMIs) that can be solved via efficient interior-point algorithms.     

Multirate sampled-data control of two-time-scale systems

Two-time-scale decoupling is adopted to obtain slow and fast subsystems. Based on these subsystems, a multirate composite control strategy is derived. It is shown how a recently presented multirate scheme can be modified to handle slow, nonzero mean disturbances, by introducing time incremental models. The improvements with the given multirate control law are illustrated by an example     

Non-uniform sampled-data control of MIMO systems

In this paper, the problem of controlling MIMO plants where the pattern of measurements sampling and control actions delivering is not regular is tackled. Multirate control, time delay systems, missing data environments as well as limited computing and communication resources are on the grounds of these problems. There is an extensive literature on these topics. Different models are reviewed and, from the control viewpoint, model-based control design techniques are reported and new algorithms are proposed. Among the main challenges are the achievement of good performance and the avoidance of intersampling ripple. Both issues are considered and some results are discussed.     

Continuous-time deadbeat control for sampled-data systems

In this paper, the continuous-time deadbeat control problem for the sampled-data systems is considered. We derived the class of all controllers that achieve the continuous-time deadbeat control     

Ripple-free deadbeat control for sampled-data systems

In this paper the ripple-free deadbeat control problem for sampled-data systems is considered. This control objective is to settle the error to zero for all time after some finite settling time, in other words to eliminate the ripples between the sampling instants in deadbeat control of sampled-data systems. The necessary and sufficient conditions to solve this problem are derived and related to the system type, and a method of constructing the ripple-free deadbeat control system is presented.     

非线性采样控制系统的Lagrange稳定性

针对由具有外部慢时变输入信号的非线性被控对象和线性数字控制器构成的非线性采样系统,采用非线性跳跃系统方法及其线性化策略,得到了这类系统在理想离散化方式和无限字长数字控制器作用下(即不考虑量化因素)的Lagrange稳定(最终有界)条件.并指出,当考虑数字控制器和接口器件的非线性量化因素影响时,只要非线性量化环节及其相应偏导数有界,则所得稳定性结论仍然成立.     

基于分段连续Lyapunov函数的采样系统鲁棒保性能控制

针对不确定采样控制系统的鲁棒保性能控制问题,首先将采样系统描述为跳变线性系统,基于矩阵凸组合思想构造了分段连续Lyapunov函数,进而在线性矩阵不等式框架内给出了不确定采样系统鲁棒稳定的条件.针对范数有界参数不确定采样系统,提出了鲁棒保性能控制器设计的在线算法,在每个采样周期内通过求解一组线性矩阵不等式的可行解来构造出状态反馈增益矩阵.最后的仿真算例验证了所提设计方法的有效性.     

Delay-dependent sampled-data control based on delay estimates

In this paper the sampled-data stabilization of linear time-invariant systems with feedback delay is considered. We assume that the delay is time-varying and that its value is approximatively known. We investigate how to use the available information about the evolution of delays for adapting the control law in real time. Numerical methods for the design of a delay-dependent controller are presented. This allows for providing a control for some cases in which the stabilization cannot be ensured using a controller with a fixed structure.     

Some qualitative properties of sampled-data control systems

The authors consider sampled-data control systems which consist of an interconnection of a continuous-time nonlinear plant and a digital controller which has quantizers (but is otherwise linear), along with the required interface elements (A/D and D/A converters). In the present paper the authors study the qualitative effects of the quantizers and the plant nonlinearities of these systems     

Optimal control of sampled-data piecewise affine systems

This paper discusses the optimal continuous-time control problem of a class of piecewise affine (PWA) systems, where the switching action of the discrete state is determined at each sampling time according to a condition on the continuous state. Such a system is called here the sampled-data PWA (SD-PWA) system. First, important remarks on the control design of this system via the continuous-(or discrete-)time PWA model are pointed out, which motivate us to use the SD-PWA model. Next, based on the good properties of the proposed model, an optimal continuous-time controller of the SD-PWA systems is proposed.     

Stabilization of nonlinear systems using sampled-data output-feedback fuzzy controller based on polynomial-fuzzy-model-based control approach

This paper investigates the stability of sampled-data output-feedback (SDOF) polynomial-fuzzy-model-based control systems. Representing the nonlinear plant using a polynomial fuzzy model, an SDOF fuzzy controller is proposed to perform the control process using the system output information. As only the system output is available for feedback compensation, it is more challenging for the controller design and system analysis compared to the full-state-feedback case. Furthermore, because of the sampling activity, the control signal is kept constant by the zero-order hold during the sampling period, which complicates the system dynamics and makes the stability analysis more difficult. In this paper, two cases of SDOF fuzzy controllers, which either share the same number of fuzzy rules or not, are considered. The system stability is investigated based on the Lyapunov stability theory using the sum-of-squares (SOS) approach. SOS-based stability conditions are obtained to guarantee the system stability and synthesize the SDOF fuzzy controller. Simulation examples are given to demonstrate the merits of the proposed SDOF fuzzy control approach.