Adaptive neural control for uncertain systems subject to actuator failure

In this paper, the design of controller based on neural network is investigated for a class of uncertain systems subject to actuator failures. An adaptive neural controller is designed by utilizing the approximation technique of neural network. The key feature in this work is to remove the requirement on the boundedness of unknown nonlinear functions that is usually encountered in the existing works. Moreover, sufficient conditions are derived such that the closed-loop system is robustly stable. Finally, numerical simulation results are given.     

Distributed model predictive control for polytopic uncertain systems subject to actuator saturation

In this paper, we present a distributed model predictive control (MPC) algorithm for polytopic uncertain systems subject to actuator saturation. The global system is decomposed into several subsystems. A set invariance condition for polytopic uncertain system with input saturation is identified and a min–max distributed MPC strategy is proposed. The distributed MPC controller is designed by solving a linear matrix inequalities (LMIs) optimization problem. An iterative algorithm is developed for making coordination among subsystems. Case studies are carried out to illustrate the effectiveness of the proposed algorithm.     

Switching control of linear systems subject to asymmetric actuator saturation

In this paper, we study the saturation control problem for linear time-invariant (LTI) systems subject to asymmetric actuator saturation under a switching control framework. The LTI plant with asymmetric saturation is first transformed to an equivalent switched linear model with each subsystem subject to symmetric actuator saturation, based on which a dwell-time switching controller augmented with a controller state reset is then developed by using multiple Lyapunov functions. The controller synthesis conditions are formulated as linear matrix inequalities (LMIs), which can be solved efficiently. Simulation results are also included to illustrate the effectiveness and advantages of the proposed approach.     

Output-feedback control for switched linear systems subject to actuator saturation

This article is devoted to the output-feedback ?_∞ control problem for switched linear systems subject to actuator saturation. We consider both continuous- and discrete-time switched systems. Using the minimal switching rule, nonlinear output feedbacks expressed in the form of quasi-linear parameter varying system are designed to satisfy a pre-specified disturbance attenuation level defined by the regional ?₂ (?₂)-gains over a class of energy-bounded disturbances. The conditions are expressed in bilinear matrix inequalities and can be solved by line search coupled with linear matrix inequalities optimisation. A spherical inverted pendulum example is used to illustrate the effectiveness of the proposed approach.     

Sliding mode control for uncertain switched systems subject to actuator nonlinearity

This paper considers the problem of sliding mode control for a class of uncertain switched systems subject to sector nonlinearities and dead-zone. In the control systems, each subsystem is not required to share the same input channel, which is usually assumed in the previous works. By employing a weighted sum of the input matrices, a common sliding surface is designed and the corresponding sliding mode dynamics is obtained. A switching signal based on the average dwell time strategy is further proposed to ensure the exponential stability of the sliding mode dynamics. Moreover, it is shown that the reachability of the specified sliding surface can be ensured despite the presence of actuator nonlinearity, parameter uncertainties and external disturbances. Finally, a numerical example is given to demonstrate the effectiveness of the proposed method.     

Output feedback control of linear fractional transformation systems subject to actuator saturation

In this paper, the control problem for a class of linear parameter varying (LPV) plant subject to actuator saturation is investigated. For the saturated LPV plant depending on the scheduling parameters in linear fractional transformation (LFT) fashion, a gain-scheduled output feedback controller in the LFT form is designed to guarantee the stability of the closed-loop LPV system and provide optimised disturbance/error attenuation performance. By using the congruent transformation, the synthesis condition is formulated as a convex optimisation problem in terms of a finite number of LMIs for which efficient optimisation techniques are available. The nonlinear inverted pendulum problem is employed to demonstrate the effectiveness of the proposed approach. Moreover, the comparison between our LPV saturated approach with an existing linear saturated method reveals the advantage of the LPV controller when handling nonlinear plants.     

Energy-based control for hybrid port-controlled Hamiltonian systems

In this paper we develop an energy-based hybrid control framework for hybrid port-controlled Hamiltonian systems. In particular, we obtain constructive sufficient conditions for hybrid feedback stabilization that provide a shaped energy function for the closed-loop system, while preserving a hybrid Hamiltonian structure at the closed-loop level. Furthermore, an inverse optimal hybrid feedback control framework is developed that characterizes a class of globally stabilizing energy-based controllers that guarantee hybrid sector and gain margins to multiplicative input uncertainty of hybrid Hamiltonian systems.     

Consensus of linear multi-agent systems subject to actuator saturation

This paper is aimed at studying the consensus of linear multi-agent systems subject to actuator saturation. In order to solve the consensus problem, a new family of scheduled low-and-high-gain decentralized control laws are designed, provided that the dynamics of each agent is asymptotically null controllable with bounded controls, and such control laws rely on the asymptotic property of a class of parametric algebraic Ricatti equations. It is shown that the consensus of the systems with connected and fixed topology can be achieved semi-globally asymptotically via the local error low-and-high-gain feedback. An illustrative example with simulations shows that our method as well as control protocols is effective for the consensus of the linear multi-agent systems subject to actuator saturation.     

Robust stability analysis and fuzzy-scheduling control for nonlinear systems subject to actuator saturation

Takagi-Sugeno (TS) fuzzy models can provide an effective representation of complex nonlinear systems in terms of fuzzy sets and fuzzy reasoning applied to a set of linear input-output submodels. In this paper, the TS fuzzy modeling approach is utilized to carry out the stability analysis and control design for nonlinear systems with actuator saturation. The TS fuzzy representation of a nonlinear system subject to actuator saturation is presented. In our TS fuzzy representation, the modeling error is also captured by norm-bounded uncertainties. A set invariance condition for the system in the TS fuzzy representation is first established. Based on this set invariance condition, the problem of estimating the domain of attraction of a TS fuzzy system under a constant state feedback law is formulated and solved as a linear matrix inequality (LMI) optimization problem. By viewing the state feedback gain as an extra free parameter in the LMI optimization problem, we arrive at a method for designing state feedback gain that maximizes the domain of attraction. A fuzzy scheduling control design method is also introduced to further enlarge the domain of attraction. An inverted pendulum is used to show the effectiveness of the proposed fuzzy controller.     

Set invariance analysis and gain-scheduling control for LPV systems subject to actuator saturation

In this paper, a set invariance analysis and gain scheduling control design approach is proposed for the polytopic linear parameter-varying systems subject to actuator saturation. A set invariance condition is first established. By utilizing this set invariance condition, the design of a time-invariant state feedback law is formulated and solved as an optimization problem with LMI constraints. A gain-scheduling controller is then designed to further improve the closed-loop performance. Numerical examples are presented to demonstrate the effectiveness of the proposed analysis and design method.     

Stabilization bound of singularly perturbed systems subject to actuator saturation

This paper considers the stabilization bound problem for singularly perturbed systems (SPSs) subject to actuator saturation. A state feedback stabilization controller design method is proposed and a basin of attraction depending on the singular perturbation parameter is constructed, which facilitates the formulation of the convex optimization problem for maximizing the basin of attraction of SPSs. Finally, examples are given to show the advantages and effectiveness of the obtained results.     

Gain scheduling consensus of multi-agent systems subject to actuator saturation

ABSTRACT

This paper presents a gain scheduling approach for achieving the consensus tracking of multi-agent systems with actuator saturation. We first construct a series of nesting ellipsoid invariant sets associated with consensus errors. When the consensus errors stay between the two ellipsoid invariant sets, the feedback gains keep constant, but when the consensus errors enter into the smaller ellipsoid invariant set, the feedback gains abruptly become larger. By combining this gain scheduling technique and the parametric Lyapunov equations, we, respectively, design state and output feedback gain scheduling protocols. Their main advantage, in comparison with the fixed case, is that the convergence rate of consensus tracking can be enhanced by scheduling the gain parameters. Numerical simulations verify the effectiveness of theoretical analysis.     

Adaptive control of the ODE systems with uncertain diffusion-dominated actuator dynamics

This article is concerned with the adaptive stabilising control design for the ordinary differential equation systems with uncertain diffusion-dominated actuator dynamics. This problem is highly difficult to solve and worthy of investigation, mainly due to the unknown diffusion coefficient which does not belong to any known finite interval and essentially different from the existing literature. By introducing an infinite-dimensional backstepping transformation, the pivotal target system is thus obtained, which makes the control design and stability analysis become more convenient. Then, based on the idea of certainty equivalence principle and recently developed adaptive technique, an adaptive stabilising controller is successfully constructed, which guarantees that all the closed-loop system states are bounded while the original system states converge to zero. A simulation example is presented to illustrate the effectiveness of the proposed method.     

Controller design for Markov jumping systems subject to actuator saturation

In this paper, the stochastic stabilization problem for a class of Markov jumping linear systems (MJLS) subject to actuator saturation is considered. The concept of domain of attraction in mean square sense is used to analyze the closed-loop stability. When the jumping mode is available, a mode-dependent state feedback controller is developed. Otherwise, we give a less conservative approach to design the mode-independent state feedback controller. Both design procedures can be converted into a set of linear matrix inequalities (LMIs). Finally, a numerical example is provided to show the effectiveness of the techniques.     

Analysis and design for discrete-time linear systems subject to actuator saturation

We present a method to estimate the domain of attraction for a discrete-time linear system under a saturated linear feedback. A simple condition is derived in terms of an auxiliary feedback matrix for determining if a given ellipsoid is contractively invariant. Moreover, the condition can be expressed as linear matrix inequalities (LMIs) in terms of all the varying parameters and hence can easily be used for controller synthesis. The following surprising result is revealed for systems with single input: suppose that an ellipsoid is made invariant with a linear feedback, then it is invariant under the saturated linear feedback if and only if there exists a saturated (nonlinear) feedback which makes the ellipsoid invariant. Finally, the set invariance condition is extended to determine invariant sets for systems with persistent disturbances. LMI based methods are developed for constructing feedback laws that achieve disturbance rejection with guaranteed stability requirements.     

饱和非线性时滞系统约束预测控制

针对一类具有执行器饱和与输出约束的离散非线性时滞系统,提出新的模糊预测控制方法。首先,采用T-S模糊模型来逼近实际非线性系统,运用平行分步补偿（PDC）原理将该系统转化为一系列线性系统的凸组合。其次,通过每个采样时刻优化无穷时域的“min-max”性能指标来求解状态反馈预测控制器,得到系统满足Lyapunov渐近稳定的充分条件,并进一步将该条件转化为基于线性矩阵不等式（LMI）技术的半正定规划（SDP）问题。最后,通过数值仿真验证该方法的有效性。     

Adaptive fault-tolerant control of linear time-invariant systems in the presence of actuator saturation

This paper studies the problem of designing adaptive fault-tolerant controllers for linear tirne-invariant systems with actuator saturation. New methods for designing indirect adaptive fault-tolerant controllers via state feedback are presented for actuator fault compensations. Based on the on-line estimation of eventual faults, the adaptive fault-tolerant controller parameters are updating automatically to compensate the fault effects on systems. The designs are developed in the framework of linear matrix inequality （LMI） approach, which can enlarge the domain of attraction of closed-loop systems in the cases of actuator saturation and actuator failures. Two examples are given to illustrate the effectiveness of the design method.     

Adaptive neural network control of uncertain MIMO nonlinear systems with input saturation

In this paper, an adaptive neural network (NN) tracking controller is developed for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with input saturation. Radial basis function neural networks are utilized to approximate the unknown nonlinear functions in the MIMO system. A novel auxiliary system is developed to compensate the effects induced by input saturation (in both magnitude and rate) during tracking control. Endowed with a switching structure that integrates two existing representative auxiliary system designs, this novel auxiliary system improves control performance by preserving their advantages. It provides a comprehensive design structure in which parameters can be adjusted to meet the required control performance. The auxiliary system signal is utilized in both the control law and the neural network weight-update laws. The performance of the resultant closed-loop system is analyzed, and the bound of the transient error is established. Numerical simulations are presented to demonstrate the effectiveness of the proposed adaptive neural network control.     

Adaptive fault-tolerant control of linear systems with actuator saturation and L_2-disturbances

This paper studies the problem of designing adaptive fault-tolerant H-infinity controllers for linear timeinvariant systems with actuator saturation.The disturbance tolerance ability of the closed-loop system is measured by an optimal index.The notion of an adaptive H-infinity performance index is proposed to describe the disturbance attenuation performances of closed-loop systems.New methods for designing indirect adaptive fault-tolerant controllers via state feedback are presented for actuator fault compe...