Incremental Generalized Homogeneity,Observer Design and Semiglobal Stabilization

The notion of incremental generalized homogeneity is introduced, giving new results on semiglobal stabilization by output feedback and observer design and putting into a unifying framework the stabilization design for triangular (feedback/feedforward) and homogeneous systems. A state feedback controller and an asymptotic state observer are designed separately by dominating the generalized homogeneity degree of the nonlinearities with the degree of the linear approximation of the system and an output feedback controller is obtained according to a certainty‐equivalence principle.     

输出非对称死区的非严格反馈非线性系统控制

本文研究了具有输出非对称死区和状态含未知控制方向的非严格反馈非线性系统, 设计了稳定的自适应 神经网络控制器. 首先, 针对输出非对称死区的问题, 本文采用死区逆的方法, 构造光滑模型逼近原死区模型. 其 次, 在控制器设计过程中, 基于障碍Lyapunov函数的构造, 动态面控制和反步法, 设计出自适应控制信号, 虚拟控制 信号和实际控制信号. 通过稳定性分析, 证明所设计的神经网络控制器可以保证闭环系统内所有信号是半全局一致 最终有界. 最后, 通过MATLAB数值仿真, 说明所设计控制器的有效性.     

Output feedback stabilization for time-delay nonlinear interconnected systems using neural networks.

In this paper, dynamic output feedback control problem is investigated for a class of nonlinear interconnected systems with time delays. Decentralized observer independent of the time delays is first designed. Then, we employ the bounds information of uncertain interconnections to construct the decentralized output feedback controller via backstepping design method. Based on Lyapunov stability theory, we show that the designed controller can render the closed-loop system asymptotically stable with the help of the changing supplying function idea. Furthermore, the corresponding decentralized control problem is considered under the case that the bounds of uncertain interconnections are not precisely known. By employing the neural network approximation theory, we construct the neural network output feedback controller with corresponding adaptive law. The resulting closed-loop system is stable in the sense of semiglobal boundedness. The observers and controllers constructed in this paper are independent of the time delays. Finally, simulations are done to verify the effectiveness of the theoretic results obtained.     

Semiglobal stabilization of linearly uncontrollable and unobservable nonlinear systems via sampled‐data control

This article investigates the problem of using sampled‐data state/output feedback to semiglobally stabilize a class of uncertain nonlinear systems whose linearization around the origin is neither controllable nor observable. For any arbitrarily large bound of initial states, by employing homogeneous domination approach and a homogeneous version of Gronwall‐Bellman inequality, a sampled‐data state feedback controller with appropriate sampling period and scaling gain is constructed to semiglobally stabilize the system. In the case when not all states are available, a reduced‐order sampled‐data observer is constructed to provide estimates for the control law, which can guarantee semiglobal stability of the closed‐loop system with carefully selected sampling period and scaling gain.     

Extended filtering high‐gain output feedback controller for a class of uncertain nonlinear systems subject to external disturbances

In this article, an extended filtering high‐gain output feedback controller is developed for a class of uncertain nonlinear systems subject to external disturbances. The nonlinearities under consideration satisfy a semiglobal Lipschitz condition. The proposed control architecture integrates the extended state observer (ESO), high gain, and low‐pass filter together. None of them is used alone. The ESO can not only estimate the unknown internal state, but also deliver a good property of disturbance rejection simultaneously due to the presence of high gain. Since the high gain deteriorates the robustness of the system, a low‐pass filtering mechanism is added in the control law to filter away aggressive signals and recover the robustness. The filtering control law is designed to compensate the nonlinear uncertainties and deliver a good tracking performance with guaranteed stability. The matched uncertainties are canceled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. Since the virtual reference system defines the best performance that can be achieved by the closed‐loop system, the uniform performance bounds are derived for the states and control signals via comparison. Numerical examples are provided to illustrate the effectiveness of the novel design via comparisons with the model reference adaptive control method and L₁ adaptive controller.     

一类不确定非线性系统的输出反馈半全局镇定

研究了一类不确定非线性系统的输出反馈半全局镇定问题. 不同于现有文献,本文研究的控制系统具有更强的非线性和未知控制系数,这增加了设计输出反馈控制器的难度. 基于反推方法和输出反馈占优方法,设计了输出反馈半全局控制器. 通过选取适当的设计参数,该控制器可以保证闭环系统的半全局渐近稳定. 仿真实例验证了理论结果的有效性.     

Robust approximation‐based adaptive control of multiple state delayed nonlinear systems with unmodeled dynamics

This paper addresses the problem of tracking control for a class of uncertain nonstrict‐feedback nonlinear systems subject to multiple state time‐varying delays and unmodeled dynamics. To overcome the design difficulty in system dynamical uncertainties, radial basis function neural networks are employed to approximate the black‐box functions. Novel continuous functions that deal with whole states uncertainties are introduced in each step of the adaptive backstepping to make the controller design feasible. The robust problem caused by unmodeled dynamics when constructing a stable controller is solved by employing an auxiliary signal to regulate its boundedness. A novel Lyapunov‐Krasovskii functional is developed to compensate for the delayed nonlinearity without requiring the priori knowledge of its upper bound functions. On the basis of the proposed robust adaptive neural controller, all the closed‐loop signals are semiglobal uniformly ultimately bounded with good tracking performance.     

Adaptive output feedback regulation of a class of nonlinearsystems: convergence and robustness

In this paper we consider a minimum-phase, input-output linearizable system that is represented globally by an nth-order differential equation. The nonlinearities of the system depend linearly on unknown parameters which belong to a known convex set. We design a semiglobal adaptive output feedback controller to ensure boundedness of all state variables and regulation of the output to zero (an open-loop equilibrium condition). It is shown that the adaptive controller is robust with respect to bounded disturbances in the sense that the mean square regulation error is of the order of the magnitude of the disturbance. Moreover, if the disturbance vanishes when the input and output are identically zero and if its slope is sufficiently small, then the adaptive controller will ensure convergence of the regulation error     

Adaptive output feedback control of nonlinear systems representedby input-output models

We consider a single-input-single-output nonlinear system which can be represented globally by an input-output model. The system is input-output linearizable by feedback and is required to satisfy a minimum phase condition. The nonlinearities are not required to satisfy any global growth condition. The model depends linearly on unknown parameters which belong to a known compact convex set. We design a semiglobal adaptive output feedback controller which ensures that the output of the system tracks any given reference signal which is bounded and has bounded derivatives up to the nth order, where n is the order of the system. The reference signal and its derivatives are assumed to belong to a known compact set. It is also assumed to be sufficiently rich to satisfy a persistence of excitation condition. The design process is simple. First we assume that the output and its derivatives are available for feedback and design the adaptive controller as a state feedback controller in appropriate coordinates. Then we saturate the controller outside a domain of interest and use a high-gain observer to estimate the derivatives of the output. We prove, via asymptotic analysis, that when the speed of the high-gain observer is sufficiently high, the adaptive output feedback controller recovers the performance achieved under the state feedback one     

AN ALGORITHM FOR DIGITAL IMPLEMENTATION OF TRAJECTORY TRACKING CONTROLLERS

In this work, we study the problem of the digital implementation of continuous‐time solutions to the trajectory tracking control problem for nonlinear systems. We exhibit an example that shows that, in general, no proper behavior of the tracking error can be expected when these solutions are implemented via the Sampling and Zero Order Hold technique. Inspired in some constructions developed in the context of Positional Games, we present a sampled‐data controller that, based on a continuous‐time solution of the tracking problem, assures semiglobal practical stability of the tracking error, with final error arbitrarily small if we choose a suitable sampling period. The controller is robust with respect to small external disturbances and small errors in the measurements.     

Robust Output Feedback Tracking Control for Time-Delay Nonlinear Systems Using Neural Network

In this paper, the problem of robust output tracking control for a class of time-delay nonlinear systems is considered. The systems are in the form of triangular structure with unmodeled dynamics. First, we construct an observer whose gain matrix is scheduled via linear matrix inequality approach. For the case that the information of uncertainties bounds is not completely available, we design an observer-based neural network (NN) controller by employing the backstepping method. The resulting closed-loop system is ensured to be stable in the sense of semiglobal boundedness with the help of changing supplying function idea. The observer and the controller designed are both independent of the time delays. Finally, numerical simulations are conducted to verify the effectiveness of the main theoretic results obtained     

Robust nonlinear control of atomic force microscope via immersion and invariance

This paper reports an immersion and invariance (I&I)–based robust nonlinear controller for atomic force microscope (AFM) applications. The AFM dynamics is prone to chaos, which, in practice, leads to performance degradation and inaccurate measurements. Therefore, we design a nonlinear tracking controller that stabilizes the AFM dynamics around a desired periodic orbit. To this end, in the tracking error state space, we define a target invariant manifold, on which the system dynamics fulfills the control objective. First, considering a nominal case with full state measurement and no modeling uncertainty, we design an I&I controller to render the target manifold exponentially attractive. Next, we consider an uncertain AFM dynamics, in which only the displacement of the probe cantilever is measured. In the framework of the I&I method, we recast the robust output feedback control problem as the immersion of the output feedback closed‐loop system into the nominal full state one. For this purpose, we define another target invariant manifold that recovers the performance of the nominal control system. Moreover, to handle large uncertainty/disturbances, we incorporate the method of active disturbance rejection into the I&I output feedback control. Through Lyapunov‐based analysis of the closed‐loop stability and robustness, we show the semiglobal practical stability and convergence of the tracking error dynamics. Finally, we present a set of detailed, comparative software simulations to assess the effectiveness of the control method.     

A Stable Output Feedback Position Control With Integral Action For Robot Manipulators

In this paper, the problem of robust regulation of robot manipulators using only position measurements is addressed. The main idea of the control design methodology is to use an observer to estimate simultaneously the velocity and the modeling error signal induced by model/system mismatches. The controller is obtained by replacing the velocity and the modeling error in an inverse dynamics feedback by their estimates, which leads to a certainty equivalence controller. The resulting controller has a PID‐type structure which, under least prior knowledge, reduces to the PI₂D regulator studied in [20]. Moreover, the controller is endowed with a natural antireset windup (ARW) scheme to cope with control torque saturations. Regarding the closed‐loop behavior, it is proven that the region of attraction can be arbitrarily enlarged with high observer gains only, thus we prove semiglobal asymptotic stability. Our result supersedes previous works in the direction of performance estimates; specifically, it is also proven that the performance induced by a saturated inverse dynamics controller can be recovered by our PID‐type controller. In this sense, our work reveals some connections between PID‐type and inverse dynamics controllers.     

Asymptotic regulation of minimum phase nonlinear systems usingoutput feedback

We consider a single-input/single-output (SISO) nonlinear system which has a well-defined normal form with asymptotically stable zero dynamics. We allow the system's equation to depend on constant uncertain parameters and disturbance inputs which do not change the relative degree. Our goal is to design an output feedback controller which regulates the output to a constant reference. The integral of the regulation error is augmented to the system equation, and a robust output feedback controller is designed to bring the state of the closed-loop system to a positively invariant set. Once inside this set, the trajectories approach a unique equilibrium point at which the regulation error is zero. We give regional as well as semiglobal results     

Filtering adaptive neural network controller for multivariable nonlinear systems with mismatched uncertainties

This paper synthesizes a filtering adaptive neural network controller for multivariable nonlinear systems with mismatched uncertainties. The multivariable nonlinear systems under consideration have both matched and mismatched uncertainties, which satisfy the semiglobal Lipschitz condition. The nonlinear uncertainties are approximated by a Gaussian radial basis function (GRBF)‐based neural network incorporated with a piecewise constant adaptive law, where the adaptive law will generate adaptive parameters by solving the error dynamics between the real system and the state predictor with the neglection of unknowns. The combination of GRBF‐based neural network and piecewise constant adaptive law relaxes hardware limitations (CPU). A filtering control law is designed to handle the nonlinear uncertainties and deliver a good tracking performance with guaranteed robustness. The matched uncertainties are cancelled directly by adopting their opposite in the control signal, whereas a dynamic inversion of the system is required to eliminate the effect of the mismatched uncertainties on the output. Since the virtual reference system defines the best performance that can be achieved by the closed‐loop system, the uniform performance bounds are derived for the states and control signals via comparison. To validate the theoretical findings, comparisons between the model reference adaptive control method and the proposed filtering adaptive neural network control architecture with the implementation of different sampling time are carried out.     

Robust semiglobal swarm tracking of coupled harmonic oscillators with input saturation and external disturbance

The robust semiglobal swarm tracking problem of N coupled harmonic oscillators and 1 actual leader with input saturation and external disturbance on a directed communication topology is considered, in which the N coupled harmonic oscillators are referred to followers. First, the low‐and‐high gain feedback technique is introduced to construct a relative state‐dependent control algorithm. Then, an observer‐based control algorithm is designed based on the low‐and‐high gain feedback technique and the high‐gain observer design methodology. Sufficient conditions are derived to guarantee robust semiglobal swarm tracking for state‐feedback control and output‐feedback control, respectively. Numerical simulations are finally provided to verify the theoretic results.     

Output feedback semiglobal practical consensus of linear systems with relative state‐dependent uncertainties

This paper proposes an output feedback consensus control law for linear multiagent systems with relative state‐dependent uncertainties. To achieve output feedback control and uncertainty attenuation, the theories of extended high‐gain observer and structural decomposition are employed. Under the proposed control scheme, semiglobal practical consensus is achieved in the sense that the synchronization errors are ultimately bounded. Besides, the synchronization errors can be kept arbitrarily small by a proper choice of tuning parameters. Finally, a simulation example is provided to verify the theoretical results.     

Neural observer-based adaptive compensation control for nonlinear time-varying delays systems with input constraints

This paper describes a neural network state observer-based adaptive saturation compensation control for a class of time-varying delayed nonlinear systems with input constraints. An advantage of the presented study lies in that the state estimation problem for a class of uncertain systems with time-varying state delays and input saturation nonlinearities is handled by using the NNs learning process strategy, novel type Lyapunov-Krasovskii functional and the adaptive memoryless neural network observer. Furthermore, by utilizing the property of the function tan h²(?/?)/?, NNs compensation technique and backstepping method, an adaptive output feedback controller is constructed which not only efficiently avoids the problem of controller singularity and input saturation, but also can achieve the output tracking. And the proposed approach is obtained free of any restrictive assumptions on the delayed states and Lispchitz condition for the unknown nonlinear functions. The semiglobal uniform ultimate boundedness of all signals of the closed-loop systems and the convergence of tracking error to a small neighborhood are all rigorously proven based on the NN-basis function property, Lyapunov method and sliding model theory. Finally, two examples are simulated to confirm the effectiveness and applicability of the proposed approach.     

Output feedback second‐order sliding mode control of the cart on a beam system

We propose an output feedback second‐order sliding mode controller to stabilize the cart on a beam system. A second‐order sliding mode controller is designed using a Lyapunov function‐based switching surface and finite‐time controllers, while the state estimator is designed based on the Luenberger‐like observer. The proposed observer extends the applicability of Luenberger‐like observer to nonlinear systems that are not input–output linearizable, but can be approximately input–output linearized. The approximation is based on the physical property of the system, wherein certain terms in the total energy are neglected. Extensive numerical simulations validate the robustness of the proposed controller to parametric uncertainties using estimated states. Copyright © 2009 John Wiley & Sons, Ltd.     

Universal integral controllers with non-linear integral gains

We consider a single-input single-output minimum-phase non-linear system which has a normal form. In previous work, integral control and high-gain observers were used to achieve robust regulation under output feedback. To improve the transient performance, we propose the use of integrators with non-linear gains. We prove that the controller achieves regional and semiglobal regulation. Simulation results are presented showing the improved overshoot and settling time.