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This paper proposes a new robust output-feedback adaptive event-triggered control (AETC) method for linear systems with parametric uncertainties. The proposed event-triggered mechanism (ETM) is obtained based on the L 2 stability condition. In this paper, two design approaches are presented, the emulation approach (pre-designed control gain) and the co-design approach. In the latter, the event-triggering law and the output-feedback controller are designed simultaneously to have a more efficient control system. The proposed adaptive event-triggered mechanism (AETM) in both approaches leads to an increase in the average of inter-event times based on linear matrix inequalities (LMIs). Consequently, in the proposed methods, the stability of the event-based system is ensured, the Zeno phenomenon is avoided, the feasibility margin and the quality of the response are well maintained, and the number of samplings is reduced, considerably. Finally, to indicate the capability of the proposed methods, two numerical simulations are presented. [ABSTRACT FROM AUTHOR]

In this paper, we propose a nonlinear state feedback controller based on linear matrix inequality (LMI) for a class of nonlinear systems with parametric uncertainties and external disturbances. The primary goals of the proposed controller are to guarantee system stability and performance in the presence of system uncertainties and time-dependent disturbances. To meet the specified objectives, the LMI form is calculated as a hierarchical control structure. Using the Lyapunov stability function, the asymptotic stability of the nominal system obtained from the nonlinear state feedback is proven, and the LMI condition is attained. After applying the nonlinear state feedback controller, asymptotic stability conditions for the nominal system are constructed using the Lyapunov function, and the nonlinear state-feedback control mechanism is determined accordingly. Considering the external disturbance as input, the terms of the state matrices are substituted in the obtained LMI, and the LMI condition for a nominal system is achieved in the presence of disturbances. The asymptotic stability condition of the uncertain system in the presence of external disturbances is determined by adding uncertainties to the system. The proposed approach yields a simple control mechanism representing an independent of system order. The performance of the proposed approach was assessed using a simulation study of a ball and beam system. [ABSTRACT FROM AUTHOR]

In this article, we are dedicated to coping with the control design for underactuated mechanical system (UMS) with system uncertainties, which is also subject to a set of servo constraints. For the nominal system without any initial condition deviations, this article provides a general model-based servo control with the idea of second-order constraints, which can be derived from both holonomic and nonholonomic ones. For the uncertain system, with the uncertainties being separated into matched portions and mismatched portions, this article provides an adaptive robust control scheme to guarantee the UMS to attain the deterministic performance on the basis of nominal control. The leakage-type adaptive law is to size up the unknown uncertainty bounds. The cart-pole system and the rotational-translational actuator system are used to verify the availability of the proposed adaptive robust control. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive robust boundary controller is designed for an unstable heat equation with external disturbance flowing into the control end. The disturbance and uncertainty effect is cancelled out in the closed-loop system by using on-line approximation of disturbance upper-bound provided as an output of adaptive robust update law. The asymptotic stability of the controlled system in the presence of unknown disturbance and/or parametric uncertainties is proved utilising the Lyapunov theorem. Unlike previous researches, the control implementation does not require prior knowledge of unknown disturbance. Moreover, the controller does not require measuring the system states or estimating the system states by an observer. The adaptive robust controller can be regarded as a combination of the best qualities of the adaptive controller and the robust controller with no prior knowledge of system uncertainty, and asymptotic tracking error performance. Numerical simulations and comparisons are provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

An optimal control design for the uncertain Delta robot is proposed in the paper. The uncertain factors of the Delta robot include the unknown dynamic parameters, the residual vibration disturbances and the nonlinear joints friction, which are (possibly fast) time-varying and bounded. A fuzzy set theoretic approach is creatively used to describe the system uncertainty. With the fuzzily depicted uncertainty, an adaptive robust control, based on the fuzzy dynamic model, is established. It designs an adaptation mechanism, consisting of the leakage term and the dead-zone, to estimate the uncertainty information. An optimal design is constructed for the Delta robot and solved by minimizing a fuzzy set-based performance index. Unlike the traditional fuzzy control methods (if-then rules-based), the proposed control scheme is deterministic and fuzzily optimized. It is proven that the global solution in the closed form for this optimal design always exists and is unique. This research provides the Delta parallel robot a novel optimal control to guarantee the system performance regardless of the uncertainty. The effectiveness of the proposed control is illustrated by a series of simulation experiments. The results reveal that the further applications in other robots are feasible. [ABSTRACT FROM AUTHOR]

This note investigates the dynamic positioning control problem of full-actuated marine vehicles in the presence of system uncertainties and input constraints. In the control algorithm, the ship model uncertainty is approximated by the radial-basis-function neural networks. The effect of input saturation is analyzed by the auxiliary system, states of the auxiliary system are employed to develop the control scheme. By virtue of the dynamic surface control technique, the inherent problem of "explosion of complexity" problem occurred during conventional Backstepping design framework is avoided. The semi-global uniformly ultimately bounded stability of the closed-loop is guaranteed by Lyapunov theory. Since the minimal learning parameterization based adaptive law is derived, the number of parameters updated online is reduced to 6. Consider the servo-system uncertainty, the control inputs of interest are achieved as the measurable propeller pitch whose characteristics would facilitate the implementation of the algorithm in the practical engineering. Finally, by employing a supply vessel as the plant, comparison simulations are conducted to demonstrate the effectiveness and robustness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Autonomous underwater vehicles (AUVs) are highly nonlinear underactuated systems with uncertain dynamics and a challenging control problem. The main focus of this paper is to present a control law that shows desirable performance in the presence of modeling uncertainties. In this study, uncertainties are considered to be bounded and the AUV mathematical model is obtained in the presence of such uncertainties. Forces and torques applied to the AUV are also designed using a nonlinear dynamic controller. Appropriate adaptive rules are also presented to overcome system uncertainties and external disturbances. The adaptive nonlinear dynamic controller is designed based on upper bounds of system uncertainties and its stability is proven using the Lyapunov theory. In this article, the performance of the proposed control algorithm for tracking reference trajectories in an obstacle-rich environment is investigated. Therefore, the control algorithm is combined with potential fields for obstacle avoidance. Obtained results show the efficiency of the proposed controller. [ABSTRACT FROM AUTHOR]

Rotor speed control of wind turbines is a key factor in achieving the maximum power of wind. It is known that a high-performance controller can significantly increase the amount of energy that can be captured from this source. The main problem regarding this issue is the lack of information about the correct dynamic model of the system. This uncertainty of the model is generally associated with unknown parameters (structured uncertainty) and/or external disturbances (unstructured uncertainty). Some adaptive and robust control approaches are developed in the literature in order to deal respectively with structured and unstructured uncertainties. In this paper, to compensate for both types of uncertainty, a robust controller, which includes an adaptive feedforward term, is proposed to track the optimal speed. In addition to considering the uncertainties, another advantage of the presented approach is that, using a smooth control effort, it provides global asymptotic tracking. A complete stability proof of the system is presented, and simulation results illustrate the effectiveness of the controller. [ABSTRACT FROM AUTHOR]

The paper presents an attitude control problem of reusable launch vehicles in reentry phase. The controller is designed based on synthesizing robust adaptive control into backstepping control procedure in the presence of input constraint, model uncertainty, and external disturbance. In view of the coupling between the states of translational motion and the states of attitude motion, the control-oriented model is developed, where the uncertainties do not satisfy linear parameterization assumption. The time derivative of the virtual control input is viewed as a part of uncertain term to facilitate the analytic computations and avoid the 'explosion of terms' problem. The robust adaptive backstepping control scheme is first proposed to overcome the uncertainty and external disturbance. The robust adaptive law is employed to estimate the unknown bound of the uncertain term. Furthermore, the attitude control problem subjects to input constraint is studied, and the constrained robust adaptive backstepping control strategy is proposed. Within the Lyapunov theory framework, the stability analysis of the closed-loop system is carried out, and the tracking error converges to a random neighborhood around origin. Six-degree-of-freedom reusable launch vehicle simulation results are presented to show the effectiveness of the proposed control strategy. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The sliding mode control (SMC) technique with a first-order low-pass filter (LPF) is incorporated with a new adaptive PID controller. It is proposed for tracking control of an uncertain nonlinear system. In the proposed control scheme, the adaptation law is able to update the PID controller online during the control process within a short period. The chattering phenomenon of the SMC can be alleviated by incorporation of a first-order LPF, while the robustness of the control system is similar to that of the sliding mode. In the closed-loop control analysis, the convergence condition in the reaching phase and the existence condition of the sliding mode were analyzed. The stability of the closed-loop control is guaranteed in the sense of Lyapunov’s direct method. The simulations and experimental applications of a speed tracking control of a spark ignition (SI) engine via electronic throttle valve control architecture are provided to verify the effectiveness and the feasibility of the proposed control scheme. [ABSTRACT FROM AUTHOR]

Choosing an appropriate control scheme to alleviate nonlinearities and uncertainties is not a trivial task, especially when models are not easily available and practical evaluation provides the only means for actual performance assessment. Various factors can contribute to these nonlinearities and uncertainties, such as friction and stiction. Thus, this article investigates four different control schemes, namely PID, adaptive, conventional sliding mode control (SMC) and integral sliding mode control (ISMC) which are implemented in the Bristol Elumotion Robot Hand (BERUL) to analyse and overcome the aforementioned problems. The hand has five fingers with 16 joints and all fingers are underactuated. The implementation of the proposed control schemes are challenging since the BERUL fingers have significant friction, stiction and unknown parameters. The fingers are light in weight and fragile. Comparative performance characteristics have shown that the ISMC is the most suitable candidate to provide good experimental trajectory following and positioning control for underactuated BERUL fingers. [ABSTRACT FROM AUTHOR]

This brief describes the application of direct and indirect model reference adaptive control to a lightweight low-cost quadrotor unmanned aerial vehicle platform. A baseline trajectory tracking controller is augmented by an adaptive controller. The approach is validated using simulations and flight tested in an indoor test facility. The adaptive controller is found to offer increased robustness to parametric uncertainties. In particular, it is found to be effective in mitigating the effects of a loss-of-thrust anomaly, which may occur due to component failure or physical damage. The design of the adaptive controller is presented, followed by a comparison of flight test results using the existing linear and augmented adaptive controllers. [ABSTRACT FROM PUBLISHER]

In this paper, a robust adaptive neural control design approach is presented for a class of uncertain pure-feedback nonlinear systems. To reduce the complexity of the both controller structure and computation, only one neural network is used to approximate the lumped unknown function of the system at the last step of the recursive design process. By this approach, the complexity growing problem existing in conventional methods can be eliminated completely. Stability analysis shows that all the closed-loop system signals are uniformly ultimately bounded, and the steady state tracking error can be made arbitrarily small by appropriately choosing control parameters. Simulation results demonstrate the effectiveness and merits of the proposed approach. [ABSTRACT FROM AUTHOR]

This letter addresses the performance study of an adaptive controller in the presence of gain and delay uncertainties. The nonlinearities in the controller presented here do not allow an analytical study of the effect of uncertainties on performance. A numerical study is done instead using the sampling, learning, prediction, and optimization capabilities of the software Global Optimization, Sensitivity and Uncertainty in Models (GoSUM). [ABSTRACT FROM PUBLISHER]

In this brief, a fault detection and isolation (FDI) method is developed for aircraft engines by utilizing nonlinear adaptive estimation techniques. The fault diagnosis method follows a general architecture developed in previous papers, where a fault detection estimator is used for fault detection, and a bank of nonlinear adaptive fault isolation estimators are employed to determine the particular fault type/location. Each isolation estimator is designed based on the functional structure of a particular fault type under consideration. The general FDI architecture is applied to a realistic nonlinear aircraft engine model recently developed by NASA Researchers. In addition, the robustness of the fault diagnostic method with respect to normal engine health degradation is enhanced by adaptive thresholds and adaptive approximation techniques, hence accurate diagnostic performance is maintained while the engine continues to degrade over its lifetime. Some representative simulation results are given to show the effectiveness of the robust nonlinear FDI method. [ABSTRACT FROM PUBLISHER]

This paper dealswith the fault-tolerant control (FTC) problem for a class of linear time-invariant systemswith time-varying actuator faults and uncertainties. Formore general consideration, the faults and uncertainties are supposed to depend on the states of systems and unknownconstant bounds. For the sake of eliminating the effects of such state-dependent faults and uncertainties automatically, a switching control strategy which is formulated by a sign function is designed to configure controller based on system's states. And some adjustable control parameters are updated via designing adaptive laws. Based on the information from switching function and the adaptive estimation mechanism, the robust adaptive controllers are constructed to compensate for the effects of faults and uncertainties. Through Lyapunov functions and adaptive schemes, the asymptotic stability of the resulting adaptive FTC uncertain system can be achieved. The effectiveness of the proposed design is illustrated via a rocket fairing structural-acoustic model. [ABSTRACT FROM AUTHOR]

This paper deals with the robust stabilizability and L2 disturbance attenuation for a class of time-delay Hamiltonian control systems with uncertainties and external disturbances. Firstly, the robust stability of the given systems is studied, and delay-dependent criteria are established based on the dissipative structural properties of the Hamiltonian systems and the Lyapunov-Krasovskii (L-K) functional approach. Secondly, the problem of L2 disturbance attenuation is considered for the Hamiltonian systems subject to external disturbances. An adaptive control law is designed corresponding to the time-varying delay pattern involved in the systems. It is shown that the closed-loop systems under the feedback control law can guarantee the γ-dissipative inequalities be satisfied. Finally, two numerical examples are provided to illustrate the theoretical developments. [ABSTRACT FROM AUTHOR]

In this article on-line parameter estimation of dynamical systems is addressed in the context of model-based networked control systems (MB-NCSs). Stability conditions that are robust to parameter uncertainties and lack of feedback for extended intervals of time are presented. The updated model is used to control the real system the next time feedback information is unavailable. Additionally, new estimation models are proposed that offer better convergence properties than typical state-space parameter estimation methods; common assumptions such as availability of persistently exciting inputs and estimation of only a canonical form of the system are relaxed. The implementation of upgraded models in MB-NCSs results in better usage of the network by allowing longer intervals without the need for measurement updates. [ABSTRACT FROM AUTHOR]

Due to simple construction with convex effect, the synchronous reluctance motor (SynRM) drive system has highly nonlinear uncertainties. The accurate mathematic models are difficult to be established for time-varying and nonlinear uncertainties of the actual SynRM drive system. In this paper, the robust backstepping control system using an adaptive modified Elman neural network (ENN) uncertainty observer for a SynRM drive system is proposed to achieve the better performance and robustness. Firstly, the field-oriented mechanism is applied to formulate the dynamic equation of the SynRM drive system. Secondly, the backstepping approach is proposed to control the motion of SynRM drive system. With proposed integral backstepping control system, the rotor position of the SynRM drive possesses the advantages of good control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Moreover, to further increase the robustness of the SynRM drive system for nonlinear uncertainties, an adaptive modified ENN uncertainty observer is proposed to estimate the required lumped uncertainty. The on-line adaptive law of the modified ENN is derived in accordance with Lyapunov function. The updated parameters of the modified ENN are used by the gradient descent method and the backpropagation algorithm. Finally, the effectiveness of the proposed control scheme is verified by some experimental results. [ABSTRACT FROM AUTHOR]

The problem of decentralised adaptive robust stabilisation is considered for a class of uncertain large-scale interconnected non-linear systems with time-varying delays. It is assumed that the upper bounds of the uncertainties and interconnection terms are unknown, and that the time-varying delays are any non-negative continuous and bounded functions, and do not require their derivatives to be less than one. In particular, it is only required that the non-linear interconnection terms, which can also include time-varying delays, are bounded in any non-negative non-linear functions, which are not required to be known for the system designer. For such a class of uncertain large-scale time-delay interconnected non-linear systems, a new method is presented whereby a class of continuous memoryless decentralised local adaptive robust state feedback controllers with a rather simpler structure is proposed. It is also shown that the solutions of uncertain large-scale time-delay interconnected systems can be guaranteed as uniformly exponentially convergent towards a ball that can be as small as desired. Finally, a numerical example is given to demonstrate the validity of the results. [ABSTRACT FROM AUTHOR]

A robust tracking controller for robot manipulators measuring only the angular positions and considering model uncertainties is presented. It is considered that the model is uncertain; that is, the system parameters, nonlinear terms, external perturbations, and the friction effects in each robot joint are considered unknown. The controller is composed by two parts, a linearizing-like control feedback and a high-gain estimator. The main idea is to lump the uncertain terms into a new state which represents the dynamics of the uncertainties. This new state is then estimated in order to be compensated. In this way the resulting controller is robust. A numerical example for a RR robot manipulator is provided, in order to corroborate the results. [ABSTRACT FROM AUTHOR]

In this paper, robust adaptive boundary control is developed for a class of flexible string-type systems under unknown time-varying disturbance. The dynamics of the string system is represented by a nonhomogeneous hyperbolic partial differential equation (PDE) and two ordinary differential equations. Boundary control is proposed at the right boundary of the string based on the original distributed parameter system model (PDE) to suppress the vibration excited by the external unknown disturbance. Adaptive control is designed to compensate the system parametric uncertainty. With the proposed robust adaptive boundary control, all the signals in the closed-loop system are guaranteed to be uniformly ultimately bounded. The state of the string system is proven to converge to a small neighborhood of zero by appropriately choosing design parameters. Simulations are provided to illustrate the effectiveness of the proposed control. [ABSTRACT FROM PUBLISHER]

The generalized projective synchronization GPS( of chaotic systems with uncertain parameter noise and external disturbance is discussed. Based on the adaptive technique, a response system is constructed, and a novel adaptive controller is designed to guarantee the GPS between the driveresponse systems, and to eliminate the effect of external disturbance and parameters noise on GPS. The conclusion is proved theoretically, and corresponding numerical simulations are provided to verify the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The adaptive robust control (ARC) for DC motors subjected to parametric uncertainties, disturbances and input saturation is considered in this study. To achieve high performance while keeping the control authority within saturation limit, a saturated ARC scheme is proposed. In this scheme, a variable-gain saturation function is introduced for the virtual control law, so that the amplitude of the virtual control and its derivative decrease when the control input approaches to the prescribed bound. Consequently, the virtual control and its derivative will not be excessively large, which is crucial for stabilising the system with a bounded input. We prove that the proposed controller cannot only assure global stability, but also provide desirable control performance, that is, the tracking error can be steered to the neighbourhood of the origin in finite time. Moreover, asymptotic tracking can be achieved in the presence of parametric uncertainties only. Finally, simulation results illustrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper presents a global task coordinate frame (TCF) (GTCF)-based integrated direct/indirect adaptive robust contouring controller for linear-motor-driven biaxial systems that achieves both stringent contouring performance and accurate parameter estimations. In contrast to the past research works those use various locally defined TCFs “attached” to the desired contour to approximately calculate the contouring error for feedback controller designs, this paper first formulates the contouring control problem using a recently developed GTCF, in which calculation of the contouring error is rather accurate and not affected by the curvature of the desired contour. A physical-model-based indirect-type parameter-estimation algorithm is then synthesized to obtain accurate online estimates of unknown physical model parameters. An integrated direct/indirect adaptive robust controller with dynamic-compensation-type fast adaptation is also constructed to preserve the excellent transient and steady-state performance of the direct adaptive robust control (ARC) designs. Comparative experimental results show that the proposed GTCF-based integrated direct/indirect ARC algorithm not only achieves the best contouring performance but also possesses rather accurate estimations of physical parameters. [ABSTRACT FROM PUBLISHER]

A robust adaptive neural network controller is proposed for a class of uncertain non-linear time-delay systems in strict feedback form with both completely unknown control gains and unknown non-symmetric dead-zone non-linearity based on backstepping design. The proposed design approach does not require a priori knowledge of the signs of the unknown control gains. The unknown time delays are compensated for constructing appropriate Lyapunov-Krasovskii functionals. By utilising integral Lyapunov design and sliding-mode control strategy, the controller singularity problem and the effect of dead-zone input non-linearity are avoided perfectly. From Lyapunov stability theorem, it is proved that the proposed design approach is able to guarantee semi-globally uniformly ultimately boundedness of all the signals in the closed-loop system, and the tracking error of the system is proven to be converged to a small neighbourhood of the origin. The simulation results demonstrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

To overcome the signal-to-interference-and-noise ratio (SINR) performance degradation in the presence of large steering vector mismatches, we propose an iterative robust Capon beamformer (IRCB) with adaptive uncertainty level. The approach iteratively estimates the actual steering vector (SV) based on conventional robust Capon beamformer (RCB) formulation that uses an uncertainty sphere to model the mismatch between the actual and presumed SV. At each iteration, the adaptive uncertainty algorithm self-adjusts the uncertainty sphere according to the estimated mismatch SV. This estimation is derived based on the geometrical interpretation of the mismatch and can be expressed as a simple closed-form expression as a function of the presumed SV and the signal-subspace projection. The other variant of the proposed algorithm that uses a flat ellipsoid to model the mismatch is also proposed. Simulation results show that the proposed approaches offer better interference suppression capability and achieve higher output SINR, as compared to other diagonal-loading-based approaches. [ABSTRACT FROM AUTHOR]

This article presents a robust adaptive control scheme for a discrete-time plant that is subjected to both coprime factor perturbations and unknown exogenous disturbances. With the proposed control scheme, all the variables in the closed-loop system are bounded in the presence of the perturbations and disturbances, and an a priori computable upper bound on the size of the nonparametric dynamical uncertainty, for which stability is ensured, is provided. Moreover, one can guarantee a priori bound on the asymptotic performance of the overall adaptive system which is arbitrarily close to that of the corresponding nonadaptive control system. In addition, it is shown that the ℓ1 optimal robust controller design is continuous as a map from the plant to the optimal closed-loop solution. Furthermore, if the set of plants is compact, then the ℓ1 optimal robust controller design is uniformly continuous on the set of plants. These properties are necessary for analysing the interplay between identification and control in the overall adaptive system. [ABSTRACT FROM AUTHOR]

This paper proposes a robust self-organizing neural-fuzzy-control (RSONFC) scheme for a class of uncertain nonlinear multiple-input–multiple-output (MIMO) systems. We first develop a self-organizing neural-fuzzy network (SONFN) with concurrent structure and parameter learning. The fuzzy rules of SONFN are generated or pruned systematically. The proposed RSONFC scheme comprises an SONFN identifier, an uncertainty observer, and a supervisory controller. The SONFN identifier functions as the principal controller, and the uncertainty observer is designed to oversee uncertainties within the compound system. The supervisory controller combines sliding-mode control (SMC) and an adaptive bound-estimation scheme with various weights to achieve H_\infty tracking performance with a desired level of attenuation. Projection-type adaptation laws of network parameters developed using the Lyapunov's synthesis approach guarantee the stability of the overall control system. Simulation studies on a single-link flexible-joint manipulator and a two-link robot demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

In this paper, the problems of stability and control for a class of uncertain nonlinear systems with unknown state time-delay are studied by using the fuzzy logic systems. Because the dynamic surface control technique is introduced to deal with the uncertain time-delay systems, the designed adaptive fuzzy controller can avoid the issue of "explosion of complexity", which comes from the traditional backstepping design procedure. Compared with the existing results in the literature, the robustness to the fuzzy approximation errors is improved by adjusting the estimations of the unknown bounds for the approximation errors. It is shown that the resulting closed-loop system is stable in the sense that all the signals are bounded and the system output track the reference signal in a small neighborhood of the origin by choosing design parameters appropriately. Three simulation examples are given to demonstrate the effectiveness of the proposed techniques. [ABSTRACT FROM AUTHOR]

Abstract-A robust adaptive control approach is proposed to solve the consensus problem of multiagent systems. Compared with the previous work, the agent's dynamics includes the uncertainties and external disturbances, which is more practical in real-world applications. Due to the approximation capability of neural networks, the uncertain dynamics is compensated by the adaptive neural network scheme. The effects of the approximation error and external disturbances are counteracted by employing the robustness signal. The proposed algorithm is decentralized because the controller for each agent only utilizes the information of its neighbor agents. By the theoretical analysis, it is proved that the consensus error can be reduced as small as desired. The proposed method is then extended to two cases: Agents form a prescribed formation, and agents have the higher order dynamics. Finally, simulation examples are given to demonstrate the satisfactory performance of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, we study the limitations of the capability of the feedback mechanism in dealing with structural uncertainty for a class of pth order non-linear autoregressive control systems. The size of the structural uncertainty is characterized by multiple Lipschitz constants (say L1,L2,...,Lp) of the uncertain non-linear function. It is shown that if L1,L2,...,Lp satisfy certain algebraic conditions, then there exists no globally stabilizing feedback control law for the corresponding uncertain systems. The result here is an extension of the previous result obtained by Zhang and Guo (2002). [ABSTRACT FROM AUTHOR]