Optimal and robust control for linear state-delay systems

This paper presents the optimal regulator for a linear system with state delay and a quadratic criterion. The optimal regulator equations are obtained using the maximum principle. Performance of the obtained optimal regulator is verified in the illustrative example against the best linear regulator available for linear systems without delays. Simulation graphs demonstrating better performance of the obtained optimal regulator are included. The paper then presents a robustification algorithm for the obtained optimal regulator based on integral sliding mode compensation of disturbances. The general principles of the integral sliding mode compensator design are modified to yield the basic control algorithm oriented to time-delay systems, which is then applied to robustify the optimal regulator. As a result, the sliding mode compensating control leading to suppression of the disturbances from the initial time moment is designed. The obtained robust control algorithm is verified by simulations in the illustrative example.     

Stochastic fault tolerant control of networked control systems

A problem of stabilization about uncertain networked control systems (NCSs) with random but bounded delays is discussed in this paper. By using augmented state-space method, this class of problems can be modeled as discrete-time jump linear systems governed by finite-state Markov chains. A new switched model based on probability is proposed to research problems of reliable control when actuators become ageing or partially disabled. Using improved V-K iteration algorithm, a class of reliable controllers are designed to make systems asymptotically mean square stable under several stochastic disturbances such as random time-delay and stochastic actuator failure and the maximal redundancy degree is given through this method.     

An intelligent self-repairing control for nonlinear MIMO systems via adaptive sliding mode control technology

In this paper, an intelligent self-repairing control scheme is proposed for a class of nonlinear MIMO system. A direct self-repairing controller of a nonlinear SISO system is firstly designed, and then the control scheme is promoted to a nonlinear MIMO system. The error signals are replaced by the state variables to deal with the high derivate problems of the desired signals and a nonlinear regulating function is brought in to improve the performances of the sliding mode. The self-repairing controller is made up of four parts: the nonlinear regulator, the equal controller, the compensator I and the compensator II. The control method is applied to a helicopter flight control system with loss-in-effectiveness faults. Some simulation results illustrate the effectiveness and feasibility of the proposed control scheme in the paper.     

Permissible control of general constrained mechanical systems

This paper develops a unified approach for modeling and controlling mechanical systems that are constrained with general holonomic and nonholonomic constraints. The approach conceptually distinguishes and separates constraints that are imposed on the mechanical system for developing its physical structure between constraints that may be used for control purposes. This gives way to a general class of nonlinear control systems for constrained mechanical systems in which the control inputs are viewed as the permissible control forces. In light of this view, a new and simple technique for designing nonlinear state feedback controllers for constrained mechanical systems is presented. The general applicability of the approach is demonstrated by considering the nonlinear control of an underactuated system.     

Adaptive control of printing devices using fractional-order hold circuits adjusted through neural networks

A connectionist method for autotuning the free parameter of a fractional-order hold (FROH) circuit in order to improve the performance of the digitally controlled systems is proposed. Such a technique employs multilayer perceptrons to approximate the mapping between the sampling period/continuous-time parameters of the estimated plant and the optimal value of the FROH adjustable gain. In this way, adaptive discretization systems to improve the stability properties of the resulting discrete-time zeros are implemented. Simulation results are presented in order to illustrate the properties of the complete system applied to two actual digitally controlled printing devices (HP 7090A and low-cost computer printer).     

Sampled-data control for time-delay systems

The sampled-data systems are hybrid ones involving continuous time and discrete time signals, which makes the traditional analysis and synthesis methodologies of time-delay systems unable to be directly used in the cases of hybrid systems with time-delay. The primary disadvantages of current design techniques of sampled-data control are their inabilities to deal effectively with time-delay and the model uncertainty. In this paper, we generalized the analysis methodology of time-delay systems to that of the hybrid systems with time-delay and uncertainty, which developed a design procedure of sampled-data control for time-delay systems. Asymptotic stability of the time-delay hybrid systems was developed. The time-delay dependent robust sampled-data control for the time-varying delay of an uncertain linear system was then discussed. The results were described as linear matrix inequalities, which can be solved using newly released LMITool.     

Robust adaptive sliding mode control for uncertain discrete-time systems with time delay

This paper focuses on robust adaptive sliding mode control for discrete-time state-delay systems with mismatched uncertainties and external disturbances. The uncertainties and disturbances are assumed to be norm-bounded but the bound is not necessarily known. Sufficient conditions for the existence of linear sliding surfaces are derived within the linear matrix inequalities (LMIs) framework by employing the free weighting matrices proposed in He et al. (2008) [3], by which the corresponding adaptive controller is also designed to guarantee the state variables to converge into a residual set of the origin by estimating the unknown upper bound of the uncertainties and disturbances. Also, simulation results are presented to illustrate the effectiveness of the control strategy.     

A PI controller configuration for robust control of a class of nonlinear systems

The PI control configuration for stabilization and signal tracking of nonlinear systems is investigated. Semiglobal asymptotic stability and semiglobal practical signal tracking of the controlled system are proven using results from the theory of nonlinear singularly perturbed systems.     

Optimal control for linear systems with time delay in control input

This paper presents the optimal regulator for a linear system with time delay in control input and a quadratic cost function. The optimal regulator equations are obtained using the duality principle, which is applied to the optimal filter for linear systems with time delay in observations, and then proved using the maximum principle. Performance of the obtained optimal regulator is verified in the illustrative example against the best linear regulator available for linear systems without delays. Simulation graphs and comparison tables demonstrating better performance of the obtained optimal regulator are included.     

网络化控制系统随机时延的补偿研究

在网络化控制系统中引入离散隐马尔可夫模型,建立网络状态与控制器-执行器时延之间的概率模型.网络化控制系统被建模成一个马尔可夫跳变线性系统,并预测出当前采样周期内的控制器-执行器时延.使用该预测值设计一个状态反馈控制器,实现对控制器-执行器时延的补偿.对比仿真实验验证了所提方法的优越性.     

Adaptive output feedback neural network control of uncertain non-affine systems with unknown control direction

This paper deals with the problem of adaptive output feedback neural network controller design for a SISO non-affine nonlinear system. Since in practice all system states are not available in output measurement, an observer is designed to estimate these states. In comparison with the existing approaches, the current method does not require any information about the sign of control gain. In order to handle the unknown sign of the control direction, the Nussbaum-type function is utilized. In order to approximate the unknown nonlinear function, neural network is firstly exploited, and then to compensate the approximation error and external disturbance a robustifying term is employed. The proposed controller is designed based on strict-positive-real (SPR) Lyapunov stability theory to ensure the asymptotic stability of the closed-loop system. Finally, two simulation studies are presented to demonstrate the effectiveness of the developed scheme.     

Adaptive prescribed performance control for nonlinear robotic systems

This paper investigates the problem of asymptotic tracking control of nonlinear robotic systems with prescribed performance. The control strategy is developed based on a modified prescribed performance function (PPF) to guarantee the transient behavior, while the requirements on the accurate initial tracking error in the classical PPF can be remedied. The fuzzy logic system (FLS) is used to approximate the unknown dynamics. In the existing PPF based adaptive control schemes with FLSs, the tracking error does not achieve asymptotic convergence. To address this issue, a robust integral of the sign of the error (RISE) term is incorporated into the control design to reject the FLS approximation errors and external disturbances, such that the asymptotic convergence is achieved. Finally, numerical simulation and experimental results validate the effectiveness of the proposed control scheme.     

Robust stable pole-placement-based adaptive control of continuous linear systems with two parametrical estimation schemes

This paper deals with the pole-placement-type robust adaptive control of continuous linear systems in the presence of bounded noise and a common class of unmodeled dynamics provided that two estimation schemes are used in parallel. Both estimation schemes are introduced in order to minimize the plant identification error by selecting, as plant parameter estimates, a convex combination of both parameter estimates which leads to the selection of one of the estimation schemes, via a switching rule, on time intervals of at least a minimum prefixed residence duration. The weights of the individual parameter vector estimates are provided at each time by an optimization or suboptimization scheme for a quadratic loss function of the possibly filtered tracking error and/or control input. The robust stability of the overall adaptive scheme is ensured by an adaptation relative dead zone which takes into account the contribution of the unmodeled dynamics and bounded noise. The basic results are derived for two different estimation strategies which have either a shared regressor with the plant or individual regressors for the input contribution and its contributed derivatives. In this second case, the plant input is obtained from a similar convex combination rule as the one used for the estimators in the first approach. An extension of the basic strategies is also pointed out including a combined use of the (sub) optimization scheme with a supervisor of past measures for the on-line calculation of the estimator weights in the convex combination. Finally, the extension of the scheme for the use of any number of parametrical estimators is focused on.     

基于弱测量的量子系统消相干控制

研究了弱测量的性质以及潜在应用.首先,针对所定义的一类特定的正定算子值测量,从线性赋范空间的角度推导出其为弱测量时的参数条件.其次,讨论了弱测量的适用性:弱测量不仅适用于大量全同量子系统,若任意的测量算符都能够被选择性地施加,则弱测量也能够对单个的量子系统进行测量.最后,考察了弱测量对不同的量子系统的影响:对于大量全同量子系统,连续的弱测量对状态产生的影响相当于一个消相位过程,而对于单个的二能级量子系统,弱测量能够被用来抑制消相位以及去极化过程.     

Unknown input observer for linear non-minimum phase systems

An unknown input observer is to estimate the system state of a dynamic system subject to unknown input excitations. In this note, by assuming that at each time instant, the unknown input can be approximated by a polynomial over a local time interval, a finite-time observer is proposed to achieve approximate joint state and input estimation. Both the obtained state and input estimates are moving averages of the present and past output signals. The advantage of the proposed design is that it can be applied to non-minimum phase systems or systems with non-unity relative degree. Notice that most previous unknown input observer designs require the system to be minimum-phase and relative degree one.     

Adaptive robust fault-tolerant control for a two-slider synchronization system with a flexible beam

In this paper a novel adaptive robust fault-tolerant sync control method is proposed for a two-slider system where two sliders are constrained by a flexible beam. At first the dynamic models of sync motion system subject to external disturbances and actuator faults are derived. In order to avoid the shortcomings of truncated model, the model of flexible beam is described by using infinite dimensional equation. Then based on the models a novel disturbance observer and an adaptive fault-tolerant control law are designed. The disturbance observer is used to estimate and cancel external disturbances. The adaptive fault-tolerant control is used to deal with the partial loss of effectiveness faults. Lyapunov functional approach is used to prove that the closed-loop system with the proposed control laws is uniformly bounded stable. Finally, some simulation results display that the proposed control laws can obtain excellent sync performance in the present of external disturbances and actuator partial loss of effectiveness faults.     

Observer-based sliding mode control for a class of discrete systems via delta operator approach

In this paper, an observer-based sliding mode control (SMC) problem is investigated for a class of uncertain delta operator systems with nonlinear exogenous disturbance. A novel robust stability condition is obtained for a sliding mode dynamics by using Lyapunov theory in delta domain. Based on a designed sliding mode observer, a sliding mode controller is synthesized by employing SMC theory combined with reaching law technique. The robust asymptotical stability problem is also discussed for the closed-loop system composed of the observer dynamics and the state estimation error dynamics. Furthermore, the reachability of sliding surfaces is also investigated in state-estimate space and estimation error space, respectively. Finally, a numerical example is given to illustrate the feasibility and effectiveness of the developed method.     

Mixed order robust adaptive control for general linear time invariant systems

We provide a solution to the adaptive control problem of an unknown linear system of a given derivation order, using a reference model or desired poles defined in a possibly different derivation order and employing continuous adjustment of parameters ruled by possibly another different derivation order. To this purpose, we present an extension for the fractional settings of the Bezout’s lemma and gradient steepest descent adjustment. We analyze both the direct and indirect approaches to adaptive control. We discuss some robustness advantages/disadvantages of the fractional adjustment of parameters in comparison with the integer one and, through simulations, the possibility to define optimal derivation order controllers.     

Analyses and designs of a nonlinear mechanical scanning control system

In this literature, a scanning system with special mechanical structure and electronic control circuit is analyzed and designed. The scanning control system enlarges the field of view (FOV) and saves more 48% power consumption than conversional scanning systems with scanning motor. The considered system is characterized and analyzed by an equivalent nonlinear feedback control system. All analyzed results are verified by digital simulating and real system testing datum.     

Robust control for discrete-time singular large-scale systems with parameter uncertainty

This paper addresses the problem of robust stabilization for uncertain discrete-time singular large-scale systems with parameter uncertainties. The system under consideration is not necessarily regular. The parameter uncertainties are assumed to be time invariant, but norm-bounded. The purpose of the robust stabilization problem is to design state feedback controllers such that, for all admissible uncertainties, the closed-loop system is regular, causal and stable. In terms of strict LMIs, sufficient conditions for the solvability of the problem is presented, and the parameterization of desired state feedback controllers is also given. A numerical example is given to demonstrate the applicability of the proposed design approach.