Control of systems with sector-bounded nonlinearities: robust stability and command effort minimization by disturbance rejection

The paper shows that a control strategy with disturbance rejection is able to reduce the control effort to a minimum, ensuring at the same time a desired performance level. The disturbance to be rejected is completely unknown, except for a sectorial bound. The control unit is endowed with an extended state observer which includes a disturbance dynamics, whose state tracks the unknown disturbance to be rejected. In summary, the novel contributions of the paper are the following. First, we derive a robust stability condition for the proposed control scheme, holding for all the nonlinearities that are bounded by a known (or estimated) maximum slope. Second, we propose a novel approach for designing the observer and state feedback gains, which guarantee robust closed-loop stability. Third, we show that the designed control system yields, with a minimum control effort, the same control performance as a robust state feedback control, which on the contrary may require a larger command activity. Two simulated case studies are presented to show the effectiveness of the proposed approach.     

Output feedback control of networked systems

This paper considers the problem of control of networked systems via output feedback. The controller consists of two parts: a state observer that estimates plant state from the output when it is available via the communication network, and a model of the plant that is used to generate a control signal when the plant output is not available from the network. Necessary and sufficient conditions for the exponential stability of the closed loop system are derived in terms of the networked dwell time and the system parameters. The results suggest simple procedures for designing the output feedback controller proposed. Numerical simulations show the feasibility and efficiency of the proposed methods.     

Linearizing control of induction motor based on networked control systems

A new approach to speed control of induction motors is developed by introducing networked control systems （NCSs） into the induction motor driving system. The control strategy is to stabilize and track the rotor speed of the induction motor when the network time delay occurs in the transport medium of network data. First, a feedback linearization method is used to achieve input-output linearization and decoupling control of the induction motor driving system based on rotor flux model, and then the characteristic of network data is analyzed in terms of the inherent network time delay. A networked control model of an induction motor is established. The sufficient condition of asymptotic stability for the networked induction motor driving system is given, and the state feedback controller is obtained by solving the linear matrix inequalities （LMIs）. Simulation results verify the efficiency of the proposed scheme.     

Robust stabilization for nonholonomic systems with state delay and nonlinear drifts

This paper presents a control strategy for stabilization of the nonholonomic control systems with strongly nonlinear drifts and state delay.Applying a novel Lyapunov functional and backstepping recursive method,the design of robust nonlinear state feedback controllers is proposed,which can guarantee the stability of the closed-loop systems.Finally,a numerical example is provided to show the effectiveness of the method.     

Direct heuristic dynamic programming based on an improved PID neural network

In this paper,an improved PID-neural network(IPIDNN) structure is proposed and applied to the critic and action networks of direct heuristic dynamic programming(DHDP).As one of online learning algorithm of approximate dynamic programming(ADP),DHDP has demonstrated its applicability to large state and control problems.Theoretically, the DHDP algorithm requires access to full state feedback in order to obtain solutions to the Bellman optimality equation. Unfortunately,it is not always possible to access all the states in a real system.This paper proposes a solution by suggesting an IPIDNN configuration to construct the critic and action networks to achieve an output feedback control.Since this structure can estimate the integrals and derivatives of measurable outputs,more system states are utilized and thus better control performance are expected.Compared with traditional PIDNN,this configuration is flexible and easy to expand. Based on this structure,a gradient decent algorithm for this IPIDNN-based DHDP is presented.Convergence issues are addressed within a single learning time step and for the entire learning process.Some important insights are provided to guide the implementation of the algorithm.The proposed learning controller has been applied to a cart-pole system to validate the effectiveness of the structure and the algorithm.     

Predictive control and scheduling codesign in network control systems

A codesign approach combining predictive control compensation and network scheduling is presented in this paper to overcome the adverse influences of stochastic time delays and packet losses encountered in network-based real-time control systems. The state estimation and control prediction compensation algorithms are used for the random network delays in the feedback and forward channels, and the stability criteria are analyzed. The proper sampling rate is given with network scheduling to meet the desired system performance, while the network-induced delay is tolerated. Simulations show that the codesign approach works well with the bounded network delay.     

Robust synchronization of chaotic systems using slidingmode and feedback control

We propose a robust scheme to achieve the synchronization of chaotic systems with modeling mismatches and parametric variations. The proposed algorithm combines high-order sliding mode and feedback control. The sliding mode is used to estimate the synchronization error between the master and the slave as well as its time derivatives, while feedback control is used to drive the slave track the master. The stability of the proposed design is proved theoretically, and its performance is verified by some numerical simulations. Compared with some existing synchronization algorithms, the proposed algorithm shows faster convergence and stronger robustness to system uncertainties.     

Neural network solution for finite-horizon H-infinity constrained optimal control of nonlinear systems

In this paper, neural networks are used to approximately solve the finite-horizon constrained input H-infinity state feedback control problem. The method is based on solving a related Hamilton-Jacobi-Isaacs equation of the corresponding finite-horizon zero-sum game. The game value function is approximated by a neural network with time- varying weights. It is shown that the neural network approximation converges uniformly to the game-value function and the resulting almost optimal constrained feedback controller provides closed-loop stability and bounded L2 gain. The result is an almost optimal H-infinity feedback controller with time-varying coefficients that is solved a priori off-line. The effectiveness of the method is shown on the Rotational/Translational Actuator benchmark nonlinear control problem.     

Design of networked control systems with bounded arbitrary time delays

This paper is mainly concerned with the model predictive control (MPC) of networked control systems (NCSs) with uncertain time delay and data packets disorder. The network-induced time delay is described as bounded and arbitrary process. For the usual state feedback controller, by considering all the possibilities of delays, an augmented state space model of the closed-loop system, which characterizes all the delay cases, is obtained. The stability conditions are given according to the Lyapunov method based on this augmented model. The stability property is inherited in MPC which explicitly considers the physical constraints. A numerical example is given to demonstrate the effectiveness of the proposed MPC.     

Sliding mode disturbance observer-based backstepping control for a transport aircraft

This paper presents a novel control method by integrating the sliding mode disturbance observer- based control and the backstepping control technique, and successfully applies it to a flight control system for heavy cargo airdrop operations. The super-twisting second order sliding mode disturbance observer （SOSMDO） is employed to estimate bounded but otherwise arbitrary disturbances, thus ensuring asymptotic convergence of the estimation error to zero in a finite time. Besides, the integrated controller can significantly improve the robustness of the flight control system to modeling uncertainty and external disturbance in the presence of state/control constraints. The closed-loop stability is guaranteed in the sense of Lyapunov. In addition, the proposed approach can considerably reduce design cycles. The performance of the proposed control method is demonstrated in a very low altitude extraction airdrop simulation with a high-fidelity six-degree-of-freedom transport aircraft model.     

基于模型的输出反馈网络控制系统反馈调度研究

针对基于模型的网络控制系统缺乏应对动态变化的网络负载问题,设计反馈调度器,依据实际的网络拥塞情况,调整基于模型的网络控制系统的状态更新时间.为应对状态不完全可测的情况,在控制结构中使用了状态观测器,并证明了所提出系统在可变更新时间情况下的稳定性.仿真结果验证了稳定性条件的正确性和新网络控制系统结构的有效性.     

具有执行器饱和特性的不确定系统预测控制器设计

针对具有执行器饱和特征的不确定系统,提出了一种带有状态观测器的新型预测控制器设计方法.该方法在滚动优化的每一步,采用带有饱和特性的反馈控制结构得到一个最优控制律.使无穷时域性能指标最小.考虑在状态不完全已知的情况下,设计了带有状态观测器的预测控制器,并通过观测器参数调整使闭环系统渐近稳定.通过仿真实验验证了所设计控制器的有效性.     

A New Neuroadaptive Control Architecture for Nonlinear Uncertain Dynamical Systems: Beyond $sigma $- and $e$-Modifications

This paper develops a new neuroadaptive control architecture for nonlinear uncertain dynamical systems. The proposed framework involves a novel controller architecture involving additional terms in the update laws that are constructed using a moving time window of the integrated system uncertainty. These terms can be used to identify the ideal system weights of the neural network as well as effectively suppress and cancel system uncertainty without the need for persistency of excitation. A nonlinear parametrization of the system uncertainty is considered and state and output feedback neuroadaptive controllers are developed. To illustrate the efficacy of the proposed approach we apply our results to a spacecraft model with unknown moment of inertia and compare our results with standard neuroadaptive control methods.      

线性离散时滞系统的D稳定容错控制

采用状态反馈和带时滞的状态反馈控制律，基于Lyapunov稳定性理论和Riccati方程，针对线性离散一步时滞系统，研究了执行器失效后有一定性能保证的D稳定容错控制问题。在给出对执行器失效具有完整性的D稳定容错控制系统需满足的一个充分条件的基础上，给出了控制器的设计方法和步骤，并推广至传感器失效情况。仿真结果证实了这种方法的有效性，与仅引入状态反馈控制律相比，此方法有更好的动态响应性能。最后指出了时滞状态反馈增益矩阵的选取原则。     

State feedback control of networked systems with uncertain plant

This article, considers the problem of state feedback control of networked systems with an uncertain plant. The signals for feedback periodically switch between the plant state and the state of a model of the plant according to whether the plant state is available from the communication network or not. The model is used to generate control signals when the plant state is not available from the network. A sufficient condition for the robust exponential stability of the closed-loop system is derived in terms of the network dwell time and the system parameters. Examples are also worked out to demonstrate numerical procedures for designing state feedback controller of the system based on the obtained results. Simulations show the feasibility and efficiency of the proposed methods.     

Data-based construction of feedback-corrected nonlinear prediction model using feedback neural networks

We propose to fit a recurrent feedback neural network structure to input–output data through prediction error minimization. The recurrent feedback neural network structure takes the form of a nonlinear state estimator, which can compactly represent a multivariable dynamic system with stochastic inputs. The inclusion of the feedback error term as an input to the model allows the user to update the model based on feedback measurements in real-time uses. The model can be useful in a variety of applications including software sensing, process monitoring, and predictive control. A dynamic learning algorithm for training the recurrent neural network has been developed. Through some examples, we evaluate the efficacy of the proposed method and the prediction improvement achieved by the inclusion of the feedback error term.     

基于模型的网络控制系统稳定性分析及应用

针对时延网络控制系统中被控对象状态无法直接测量的情况，提出了基于模型的时延网络控制系统模型。系统采用状态反馈控制器，在网络信号传输时间间隔内，以被控对象模型为依据，计算控制信号，以减少系统对网络的依赖。在此基础上，给出了网络控制系统全局指数稳定的充要条件，该条件受网络信号更新时间、被控对象模型误差及网络引起的时延等因素的影响。仿真示例验证了该条件的有效性，应用在工业控制系统中，取得了较好的控制效果。     

Adaptive Terminal Sliding Mode Control for Motion Tracking of a Micropositioning System

This paper presents the design of a novel adaptive terminal sliding mode controller (ATSMC) and its application to motion tracking control of a piezoelectric‐driven micropositioning system. A nonsingular terminal sliding surface is used to achieve fast and finite‐time convergence for the trajectory tracking, and also to avoid the singularity phenomenon in traditional terminal sliding mode design. An adaptive gain law is developed to update the gain of the proposed controller and to provide stable and chattering‐free control action. The stability of the control system has been demonstrated in the sense of Lyapunov. The ATSMC scheme is established based on the output feedback only, which does not require a state observer and facilitates an easy implementation. The proposed controller is implemented on a field‐programmable gate array (FPGA) platform. Comparison study with three conventional controllers has been conducted. Experimental results show the feasibility and effectiveness of the proposed control strategy.     

Output Feedback Adaptive Neural Control Without Seeking SPR Condition

For output‐feedback adaptive control of affine nonlinear systems based on feedback linearization and function approximation, the observation error dynamics usually should be augmented by a low‐pass filter to satisfy a strictly positive real (SPR) condition so that output feedback can be realized. Yet, this manipulation results in filtering basis functions of approximators, which makes the order of the controller dynamics very large. This paper presents a novel output‐feedback adaptive neural control (ANC) scheme to avoid seeking the SPR condition. A saturated output‐feedback control law is introduced based on a state‐feedback indirect ANC structure. An adaptive neural network (NN) observer is applied to estimate immeasurable system state variables. The output estimation error rather than the basis functions is filtered and the filter output is employed to update NNs. Under given initial conditions and sufficient control parameter constraints, it is proved that the closed‐loop system is uniformly ultimately bounded stable in the sense that both the state estimation errors and the tracking errors converge to small neighborhoods of zero. An illustrative example is provided to demonstrate the effectiveness of this approach.