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Using the Hamiltonian function method with different powers, this paper studies the observer-based finite-time robust control problem of general nonlinear uncertain systems with time delay and presents some new results on this issue. First, by applying Hamiltonian realization method, the paper develops an equivalent Hamiltonian form of original system and designs its observer system. Then, based on the observer system, we study finite-time control problem and present several finite-time stabilization and finite-time robust stabilization results by using the augmented technology and the Lyapunov method. Finally, a real unmanned vehicle is used to verify the performance of the observer-based finite-time robust stabilization controller. Different from the existing literature on Hamiltonian method, the Hamiltonian function in the paper has different powers, which implies that the results developed in the paper have a wider range of application. [ABSTRACT FROM AUTHOR]

This study provides an adaptive fuzzy control scheme for the class of nonlinear uncertain strict-feedback systems with external disturbances. The backstepping and synergetic control theories are used to develop a robust control approach for this class of systems. The unknown behaviors of the system are estimated through fuzzy systems. The stability is analyzed using the Lyapunov method, which shows that the proposed control method ensures that all the signals of the closed-loop system are bounded, and the tracking error converges approximately to zero. Two simulation examples are given to demonstrate the efficiency of the developed control approach. [ABSTRACT FROM AUTHOR]

This paper deals with modelling and robust control of linear multivariable uncertain systems based on the new linear multivariable ARX (Auto-Regressive with eXogenous input)-Laguerre model and the loop-shaping design procedure with the relative gain array theory. In fact, to guarantee significant parameter number reduction of the multivariable ARX-Laguerre model compared with the classic multivariable ARX model, we propose the optimization of the Laguerre poles of the model using the genetic algorithm where the Fourier coefficients are identified using the recursive least square method. Later, the optimized multivariable ARX-Laguerre model is exploited to propose a robust control algorithm for linear multivariable uncertain systems. Indeed, we propose to combine the loop-shaping design procedure approach and the relative gain array theory to develop a simplified final robust controller guaranteeing reference tracking and robust stability against parametric uncertainties. This proposition aims for the simplification of the control's scheme by adjusting the weighting matrix and the final robust controller calculated from the loop-shaping design procedure for a simpler and more experimentally applicable control scheme. The identification of Fourier coefficients and Laguerre poles as well as the robust multivariable control algorithm are experimentally validated on a laboratory coupled two-tank system showing good results in terms of modelling and attending desired control performances. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive robust control approach has been proposed for a class of uncertain mechanical systems. First, aiming for the unknown uncertainties which may be fast time-varying, a novel adaptive mechanism in leakage-type will be developed. Unlike the prevalent adaptive methods, this adaptive mechanism put forward mainly accounts for estimating all the lumped uncertainty terms, and does not require any information of uncertainties other than they are bounded; Second, through transforming the gait following assignment to the constraint control, an adaptive robust constraint-following controller constructed will render the constraints uniformly bounded and uniformly ultimately bounded. To testify the efficacy of proposed approach, a lower limb exoskeleton robot is considered as an illustrative example, the simulations indicate that the proposed control can indeed improve the level of passive rehabilitation training. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of robust preview tracking control for uncertain continuous-time interconnected systems. We adopt the technology of decentralized control. The tracking controller for each uncertain isolated subsystem is first designed. With the help of an uncertain error system, the considered tracking problem is converted into a regulation problem. The preview tracking controller for the nominal system is obtained by utilizing existing results. A novel method is proposed to tackle the uncertainty in the systems. In this method, the uncertainty bound is incorporated into the cost function. As a result, the preview tracking controller of the nominal system can be treated as a robust preview controller of the uncertain isolated subsystem. Then, the controllers of the uncertain isolated subsystems are assembled as a robust preview controller of uncertain interconnected systems. Finally, an academic numerical example is presented to show the effectiveness and superiority of the proposed controller. [ABSTRACT FROM AUTHOR]

This paper presents a robust adaptive sliding mode controller scheme as applied to a class of uncertain nonlinear systems with parametric uncertainties and external disturbances. First, a sliding mode control technique is designed. Then, the proposed robust adaptive control schemes are applied to estimate the parametric uncertainties and the upper bound value of the external disturbances by using adaptive laws, ensure robustness in presence of parametric uncertainties and external disturbances, and reduce chattering problem by introducing an hyperbolic tangent function. Lyapunov stability theory is used to analyze the stability of the closed-loop system. As an exemplar, the schemes have been applied to a quadrotor unmanned aerial vehicle (QUAV) model. Simulation results for the control of the QUAV model are provided to illustrate the performance of the proposed robust adaptive sliding mode control scheme and demonstrate that the proposed method has good tracking performance. The simulation results clearly prove the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

This paper proposes a robust feedback feed-forward proportional-derivative type (PD-type) iterative learning control (ILC) scheme for a class of linear discrete systems with polytopic uncertainty over finite frequency ranges. First, the ILC process is transformed into an equivalent discrete linear repetitive process. Then, with the help of the generalized Kalman–Yakubovich–Popov lemma, the issue of a robust control law design algorithm in the process model is converted into the problem of solutions to the corresponding linear matrix inequality conditions. This procedure not only meets the robust performance specifications along the trial, but also guarantees the monotonic converge of the trial-to-trial error dynamics over finite frequency ranges. Finally, the simulations for a direct current servo motor system are adopted to show the effectiveness and superiority of the proposed method. Compared with previously established works, the new algorithm is more effective in delivering higher performance and achieving better tracking effect. [ABSTRACT FROM AUTHOR]

In this paper, based on event-triggered (ET) mechanisms, the problem of output tracking for a class of fractional-order uncertain systems with order 0 < α < 1 is investigated. Owing to the difficulties of measuring system full-state in practice, output tracking error is firstly used to construct an ET condition, which eventually decides whether the current signal should be transmitted. Then, by utilizing the designed error-based feedback controller, some sufficient conditions are presented to ensure that the controlled system output asymptotically tracks the reference signal. Finally, numerical simulations are performed to validate the effectiveness of the theoretical formulation. [ABSTRACT FROM AUTHOR]

This paper is devoted to designing a robust model reference controller for uncertain second-order systems subject to parameter uncertainties. The system matrix of the first-order reference model is more general and the parameter uncertainties are assumed to be norm-bounded. The design of robust controller can be devided into two separate problems: problem robust stabilization and problem robust compensation. Based on the solution of generalized Sylvester matrix equations, we obtain some sufficient conditions to guarantee the complete parameterization of the controller. Then, the problem robust compensation of the closed-loop system is estimated by solving a convex optimisation problem with a set of linear matrix equations constraints. Two simulation examples are provided to illustrate the effectiveness of the proposed technique. [ABSTRACT FROM AUTHOR]

This article deals with a novel adaptive robust controller for uncertain nonlinear systems relying on a proportional–integral–derivative-type nonsingular fast terminal sliding mode control. In this nonsingular proportional–integral–derivative-type terminal sliding mode controller nonsingular fast terminal sliding mode control, the nonsingular fast terminal sliding mode control sliding surface is modified with integral to match with the proportional–integral–derivative-type structure to obtain the essential attributes, namely, quick transient response, finite-time convergence, negligible steady-state error, and chattering cancellation. Furthermore, a novel rapid reaching law is also suggested with dynamic proof for providing the robustness during transient phase. The controller stability and convergence is mathematically analyzed using the Lyapunov theory. The overall control structure is simulated on MATLAB® software and tested for trajectory tracking of a two-degree-of-freedom revolute–prismatic joint industrial robotic manipulator. The rigorous test results show the performance efficacy of the innovative controller. [ABSTRACT FROM AUTHOR]

In a feedback control loop, when there exists a delay in processing the control signal (often called computational delay), it is difficult to stabilize the system, particularly when the system exhibits uncertainty. To solve this problem, we proposed a new robust proportional integral control strategy for a class of uncertain systems exhibiting parametric uncertainty. A two-stage scheme is proposed in which the first stage identifies the worst plant that has the highest chance of facing instability; and in the second stage, based on the worst plant, the tuning parameters of the proportional integral controller are determined using the stability boundary locus approach under the desired closed-loop specifications of gain and phase margins. The efficiency of the proposed scheme is verified for servo and regulatory control problems. [ABSTRACT FROM AUTHOR]

This paper addresses the control of a continuous-time system with possibly large uncertainty of unknown internal dynamics or external disturbance. A novel control scheme is proposed to estimate and cancel the system uncertainty effectively so as to enhance disturbance rejection (DR) performance. Unlike asymptotic analysis with infinite gain in the literature, the estimation transient analysis is carried out for the proposed scheme with a finite estimator gain and the precise error formulas are derived, based on a classical low-order plant description. The control performance associated with a realizable gain is quantified by tight bounds with respect to the ideal case, which enables easy parameter tuning. The necessary and sufficient condition for the internal stability of the control system is established, along with a D-decomposition method for determining the complete set of the gain intervals that could internally stabilize the plant. In the presence of measurement noise, a low-pass filter is introduced to attenuate its adverse effect. Simulations and semi-realistic experiments are performed to demonstrate the effectiveness of the proposed scheme, which shows evident improvement on DR performance over the well-known active DR control. [ABSTRACT FROM AUTHOR]

This paper describes a predictive extended state observer-based robust control for uncertain process control applications. The technique discussed in the article uses the extended state observer (ESO) that can estimate the dynamics of the system as well as total disturbance encountered in the system. The disturbances, parametric uncertainties associated with the processes are treated as an extended state variable to be estimated in real-time using ESO. With the implementation of a predictive algorithm with an ESO, the proposed control structure extends its applicability to time-delayed higher-order processes. The proposed control technique utilizes the simple first-order modified predictive ESO even in the case of higher-order processes. The novel predictive ESO is able to obtain a delay less estimation of total disturbance as compared with existing normal ESO. Also, novel predictive ESO maintains its stability margin in presence of time delay as well provides better response as compared with normal ESO. Numerical simulations show that the proposed scheme provides a significant improvement in transient response as compared with internal model control-based proportional-integral-derivative (IMC-PID) control. The proposed scheme requires less knowledge of the process as compared with the IMC-PID structure. The implementation of the proposed control is tested on a real-life single tank level control system. Because of its merit, the suggested technique can be used as automatic for online tuning, as it is less reliant on the process model. [ABSTRACT FROM AUTHOR]

Presented in this research paper is an integrated direct yaw moment control (DYC) and active front steering (AFS) for an uncertain vehicle lateral dynamics model considering network-induced communication delay, which is a time-varying continuous function with a known upper bound. Firstly, we consider tire cornering stiffness as a non-linear norm-bounded uncertain system that is modeled by fuzzy membership functions, and then vehicle lateral dynamics model is expressed by a set of linear Takagi-Sugeno (T-S) uncertain fuzzy models. Secondly, since the network-induced communication delay in vehicle control system is an inherent reason for stability and performance degradation, we derive a robust delay-dependent H ∞ control methodology via the Lyapunov-Krasovskii functional for stability and performance conditions of the closed-loop system. For the synthesis, the robust control method is employed within the T-S fuzzy-model-based analysis framework and formulations are performed based on the solution of delay-dependent linear matrix inequalities (LMIs). The simulation study is presented using MATLAB/Simulink to show the achieved improvements in tracking variables via the designed robust fuzzy H ∞ state-feedback controller. The proposed fuzzy robust delay-dependent controller is compared with a linear robust delay-dependent controller to clearly show the tracking improvements for different road conditions. Moreover, a performance-based analysis is carried out to demonstrate the advantage of the design with respect to different delay values. It is confirmed from the analysis results that the proposed fuzzy controller can successfully stabilize and possess improved tracking performance for vehicle lateral motion control. [ABSTRACT FROM AUTHOR]

In this paper, output tracking of a geometric path for a nonlinear uncertain system with input and state constraints is considered. We propose an enhanced two-loop model predictive control approach for output tracking of a nonlinear uncertain system. Additionally, we propose an optimal version of output path following control problem to improve the controller synthesis. Satisfaction of the dynamical constraints of a system such as velocity, acceleration and jerk limitations is added to the problem introducing a new augmented system. The recursive feasibility of the proposed method is demonstrated, and its robust stability is guaranteed such that relaxation on the terminal constraint and penalty are achieved. To validate the theoretical benefits of the proposed controller, it is simulated on a SCARA robot manipulator and the results are compared with a two-loop model predictive controller successfully. [ABSTRACT FROM AUTHOR]

This study uses a comprehensive control approach to deal with the trajectory tracking problem of a two-flexible-link manipulator subjected to model uncertainties. Because the control inputs of two-flexible-link manipulators are less than their state variables, the proposed controller should be able to tackle the stated challenge. Practically speaking, there is only a single control signal for each joint, which can be used to suppress link deflections and control joint trajectories. To achieve this objective, a novel optimal robust control scheme, with an updated gain under the adaptive law, has been developed in this work for the first time. In this regard, a nonsingular terminal sliding mode control approach is used as the robust controller and a control Lyapunov function is used as the optimal control law, to benefit from the advantages of both methods. To systematically deal with system uncertainties, an adaptive law is used to update the gain of nonsingular terminal sliding mode control. The advantage of this approach over the existing methods is that it not only can robustly and stably control an uncertain nonlinear system against external disturbances but also can optimally solve a quadratic cost function (e.g. minimization of control effort). The Lyapunov stability theory has been applied to verify the stability of the proposed approach. Moreover, to show the superiority of this method, the computer simulation results of the proposed method have been compared with those of an adaptive sliding mode control scheme. This comparison shows that the presented approach is capable of optimizing the control inputs while achieving the stability of the examined two-flexible-link manipulator in the presence of model uncertainties and external disturbances. [ABSTRACT FROM AUTHOR]

The article applies the robust generalized dynamic inversion control methodology to the problem of stabilizing upright equilibrium configuration of the under-actuated rotary inverted pendulum system while tracking rotary motion of the actuated arm. The proposed robust generalized dynamic inversion control law comprised equivalent and switching control parts. The equivalent control part works to enforce a virtual constraint dynamics of the controlled state variables by means of Moore– Penrose generalized inversion. The switching control part is of the sliding mode type, and it improves robustness against unmodeled system dynamics, parametric uncertainties, and external disturbances. The robust generalized dynamic inversion control design on the linearized model of the under-actuated rotary inverted pendulum is shown to guarantee semi-global asymptotically stable tracking performance. Numerous computer simulations and experiments are conducted on the Quanser rotary inverted pendulum system, revealing that the proposed algorithm has better convergence and tracking performance than conventional sliding mode and generalized dynamic inversion control strategies when both are applied separately. [ABSTRACT FROM AUTHOR]

In this paper, the robust control problem is tackled by employing the state-dependent Riccati equation (SDRE) for uncertain systems with unmeasurable states subject to mismatched time-varying disturbances. The proposed observer-based robust (OBR) controller is applied to two highly nonlinear, coupled and large robotic systems: namely a manipulator presenting joint flexibility due to deformation of the power transmission elements between the actuator and the robot known as flexible-joint robot (FJR) and also an FJR incorporating geared permanent magnet DC motor dynamics in its dynamic model called electrical flexible-joint robot (EFJR). A novel state-dependent coefficient (SDC) form is introduced for uncertain EFJRs. Rather than coping with the OBR control problem for such complex uncertain robotic systems, the main idea is to solve an equivalent nonlinear optimal control problem where the uncertainty and disturbance bounds are incorporated in the performance index. The stability proof is presented. Solving the complicated robust control problem for FJRs and EFJRs subject to uncertainty and disturbances via a simple and flexible nonlinear optimal approach and no need of state measurement are the main advantages of the proposed control method. Finally, simulation results are included to verify the efficiency and superiority of the control scheme. [ABSTRACT FROM AUTHOR]

The problem of H ∞ control is investigated for turbofan systems with uncertain parameters and multiple disturbances in this paper. Some disturbances with partly known information are described via an external system, and other disturbances are assumed to be L 2 norm bounded. According to the disturbance-observer-based-control (DOBC) method and resilient H ∞ control technique, a robust resilient controller is designed to reject and attenuate the influence of these disturbances, and guarantees that closed-loop systems are asymptotically stable with H ∞ performance. Some solvable sufficient conditions are obtained based on the linear matrix inequality (LMI) technique and Lyapunov stability theory. Finally, a simulation is presented to show the robustness and effectiveness of the developed resilient anti-disturbance H ∞ control method. [ABSTRACT FROM AUTHOR]

The complexity of the closed-loop system, short transient response time, and fast synchronization error convergence rates are the three basic parameters that limit hacking in the data encryption and secure the communication systems. This paper addresses the following two challenges: The full-order synchronization (FOS) of two parametrically excited second-order nonlinear pendulum (PENP) chaotic systems with uncertain parameters. The reduced-order synchronization (ROS) between the canonical projection part of an uncertain third-order chaotic Rossler and the uncertain PENP systems. This article designs a new robust adaptive synchronization control (RASC) algorithm to address the above two challenges. The proposed controller achieves the FOS and ROS in a shorter transient time, and the synchronization error signals converge to the origin with faster rates in the presence of bounded unknown state-dependent and time-dependent disturbances. The Lyapunov direct method verifies this convergence behavior. The paper provides parameters updated laws that confirm the convergence of the uncertain parameters to some fixed values. The controller does not cancel the nonlinear terms of the plant; this property of the controller keeps the nonlinear terms in the closed-loop that results in the enhanced complexity of the dynamical system. The proposed RASC strategy is successful in synthesizing oscillation free convergence of the synchronization error signals to the origin for reducing the transient time and guarantees the asymptotic stability at the origin. The simulation results endorse the theoretical findings. [ABSTRACT FROM AUTHOR]

In this paper, a three-degree-of freedom (3 DOF) integrated vehicle lateral, yaw, roll dynamics model with optimal control design have been proposed to improve the bus lateral stability and handling performance. First, a 3 DOF vehicle model for a passenger bus is introduced. The 3 DOF model dynamics include the vehicle steering wheel angle, forward speed, yaw motion, sideslip angle, lateral acceleration and the rolling motion. Then, the presented 3 DOF model is used to design the robust static output feedback H ∞ controller for both nominal system and uncertain system. The proposed controller is designed to improve the bus lateral stability and handling performance by controlling the yaw rate during normal driving and maneuvers. For the robust static output feedback H ∞ controller, the norm bounded uncertainty is considered to simulate the variation of vehicle forward velocity uncertainty. The robust controller is designed to check the lateral stability of the bus at different forward velocity and at different velocity uncertainty. The controllers are synthesized within the H ∞ control approach and the controllers' design conditions are given within the Linear Matrix Inequalities (LMIs) framework. Numerical simulations have been carried out to demonstrate the effectiveness of the proposed controllers. The obtained simulation results show that the designed nominal and robust controllers enhance the lateral stability of the bus by reducing the amplitude of the yaw rate, lateral acceleration and rolling motion. Hence, the improvements in bus lateral stability and handling performance are achieved. [ABSTRACT FROM AUTHOR]

This paper puts forward a switching rule stabilization design of the robust integral sliding mode control for uncertain switched systems. A kind of common robust integral sliding mode (CRISM) is firstly designed and the system matrices of subsystems under the sliding mode comprise a robust stable matrix set. The stability of the switched system (SS) under the sliding mode is then analyzed by multiple Lyapunov functions (MLF) method. Based on the presented design of CRISM, a sliding mode controller is devised so that the sliding mode can be reached. Finally, the correctness of the proposed method is verified through results of numerical and application simulations. [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]

This paper proposes a new robustification strategy of a primary H ∞ controller based on a systematic selection of adequate fractional weights. Its robust performance (RP) margin is well enhanced with respect to unstructured multiplicative uncertainties presented in linear feedback systems. The proposed robustification strategy provides a robustified H ∞ controller when following proposed hierarchical control is well respected. First, the primary H ∞ controller is synthesized from solving a weighted-mixed sensitivity H ∞ problem using initial integer weights. Thus, an initial RP margin is obtained. Second, an automatic selection of adjustable fractional weights is performed by the particle swarm optimization algorithm, in which some proposed tuning rules are accordingly well satisfied. Third, frequency response data of these weights are computed and then fitted by corresponding approximated integer weights using a frequency identification technique. Finally, these weights reformulate a new weighted-mixed sensitivity problem. The optimal solution to this problem updates the previous initial RP margin. These last three steps are repeated as the updated RP margin is diminished. Otherwise, the proposed hierarchical control is achieved by selecting the best adjustable fractional weights, providing, therefore, the best approximated integer weights and leading, therefore, to the robustified H ∞ controller. In order to confirm the effectiveness of our proposed hierarchical control, primary and robustified H ∞ controllers are applied on a permanent magnet synchronous motor where its actual behavior is modeled by an unstructured multiplicative uncertain model. The results obtained are compared in frequency domains using the singular value plots of their sensitivity functions. Otherwise, the same results are compared in time domains using the Powersim ® software. [ABSTRACT FROM AUTHOR]

This article presents an improved robust tracking control for uncertain robot manipulators. An approximate terminal sliding mode control is proposed by integrating a terminal sliding surface with a novel nonlinear function. Lyapunov stability theory is employed to prove the global approximate finite-time stability ensuring that the tracking errors can globally converge to an arbitrary small ball centred at the origin within a finite time and thereafter arrive at the origin asymptotically. The benefits of this integrated design are that it can ensure fast transient and high steady-state tracking precision. Extensive simulations and experimental results are presented to demonstrate the effectiveness and improved performance of the proposed approach. [ABSTRACT FROM AUTHOR]

To guarantee convergent state estimates and exact approximations, it is highly desirable that observers can independently dominate the effects of unmodelled dynamics. Based on adaptive nonlinear approximation, this paper presents a robust H ∞ gain neuro-adaptive observer (R H ∞GNAO) design methodology for a large class of uncertain nonlinear systems in the presence of time-varying unknown parameters with bounded external disturbances on the state vector and on the output of the original system. The proposed R H ∞GNAO incorporates radial basis function neural networks (RBFNNs) to approximate the unknown nonlinearities in the uncertain system. The weight dynamics of every RBFNN are adjusted online by using an adaptive projection algorithm. The asymptotic convergence of the state and parameter estimation errors is achieved by using Lyapunov cogitation under a well-defined persistent excitation condition, and without recourse to the strictly positive real condition. The repercussions of unknown disturbances are reduced by integrating an H ∞ gain performance criterion into the proposed estimation approach. The condition imposed by this proposed observer approach, such that all estimated signals are uniformly ultimately bounded, is expressed in the form of the linear matrix inequality problem and warrants the demanded performances. To evaluate the performance of the proposed observer, various simulations are presented. [ABSTRACT FROM AUTHOR]

This paper addresses the non-fragile control problem for a class of uncertain linear systems subject to model uncertainty, controller perturbations, fault signals and input constraints. The controller to be designed is supposed to have additive gain perturbations. A novel state feedback controller is proposed based on the exact available expectation of a Bernoulli random variable, which is introduced to model the feature of the controller gain perturbation that randomly occurs. By using Lyapunov stability theory, new sufficient conditions are derived to design non-fragile controller for a class of uncertain linear systems considering input constraints. Compared with the existing non-fragile state feedback controller methods, the non-fragile property is fully considered to improve the tolerance of uncertainties in the controller, where the conservativeness can be reduced via the Bernoulli random variable. The effectiveness of the proposed control strategy is illustrated by two numerical examples. [ABSTRACT FROM AUTHOR]

This paper presents a new robust decentralized control of nonlinear interconnected systems, which is applied and validated on a large scale power system. Our work is performed in three steps. Firstly, we have developed the polynomial description of the nonlinear uncertain and interconnected system using odd Kronecker power of state vectors, which is an easy-manipulation model for such complex systems. Then we applied Lyapunov's direct method of stability analysis, associated with a quadratic function, in order to determine a sufficient condition for global asymptotic stability by applying a nonlinear, decentralized and optimal polynomial control. Finally, we carried out a simulation study on a nonlinear uncertain power system with three interconnected machines. We considered different cases of perturbations on its state variables as well as different cases of fault locations. We prove via advanced simulations, the effectiveness of the proposed control technique which is able to mitigate the successive amplitudes of the oscillations, to limit the control actions and to enhance the power system transient stability. [ABSTRACT FROM AUTHOR]

In this paper, the stability issue and tracking control problem for a class of switched nonlinear systems with uncertain input delay and disturbance are investigated. First, it is assumed a piecewise Lyapunov function exists for the nominal system (system without input delay and disturbance), and then, under the terms of the hypothetical piecewise Lyapunov function, a new Lyapunov-Krasovskii functional is constructed. By employing this new function and a switching signal which satisfies the average dwell time, the delay-dependent input-to-state stability under a certain delay bound is provided; also, a mechanism is invented to find the upper bound for input delay of switched system. In the next step, tracking control problem for this system is studied. Finally, a kind of state feedback control law which ensures conditions of aforesaid hypothetical piecewise Lyapunov function is proposed to guarantee the input-to-state stability and the robust model reference tracking performance. Simulation examples are given to illustrate the effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Large-scale morphing aircraft can adaptively alter configuration according to the different flight conditions or a variety of missions to ensure the optimal aerodynamic performance. However, this will certainly put forward a higher request to modeling method and controller performance. In this paper, we propose an adaptive sliding mode control strategy with bounded L2 gain performance based on linear-parameter-varying methodology for the robust control of a morphing aircraft with variable span and variable sweep angle. Using the Kane method, the longitudinal dynamic model of the morphing aircraft is derived. Moreover, the linearized linear-parameter-varying model of the aircraft in the wing varying process is formulated for controller synthesis. The adaptive sliding mode control synthesis for this uncertain linear-parameter-varying system consists of two steps. Firstly, based on the full block S-procedure, the sufficient condition in form of linear matrix inequality constrains is derived for the existence of a reduced-order sliding mode dynamics. Then, the synthesized adaptive sliding mode control is proved to drive the linear-parameter-varying system trajectories onto the predefined switching surface without the information of upper bound of external disturbances. The performance and robustness of the proposed controller are verified by the numerical results. [ABSTRACT FROM AUTHOR]

In this paper, finite-time robust control problems are considered for a class of uncertain systems subject to exogenous disturbances, and an input–output finite-time stabilization (IO-FTS) method is presented with finite-time boundedness (FTB) constraints. For an uncertain system, a state feedback controller is designed, via linear matrix inequalities (LMIs), to ensure the IO-FTS and FTB of the closed-loop system. By using Lyapunov stability theory, sufficient conditions for IO-FTS and FTB are given. These conditions are expressed as a feasibility problem of a set of LMIs. The direct current motor control problem and roll autopilot design of a skid-to-turn missile are considered for a case study. Simulation results are presented to show the effectiveness of the proposed method as a promising approach to controlling similar uncertain systems. [ABSTRACT FROM AUTHOR]

This paper points out some technical errors in a recent paper that appeared in Transactions of the Institute of Measurement and Control entitled ‘An LMI approach to non-fragile robust optimal guaranteed cost control of uncertain 2-D discrete systems with both state and input delays’ by Akshata Tandon and Amit Dhawan (http://dx.doi.org/10.1177/0142331216667476). We reveal that the upper bound of the closed-loop cost function provided by their Lemma 4 is erroneous. Some critical issues associated with the system initial conditions assumed in their paper are highlighted. The closed-loop cost bound claimed by their Theorem 1 is found to be incorrect. The optimization problem formulated in their Theorem 2 for the selection of an optimal guaranteed cost controller is erroneous. Finally, the corrections over their results are made available. [ABSTRACT FROM AUTHOR]

In this paper, we present a solution to the problem of non-fragile robust optimal guaranteed cost control for a class of uncertain two-dimensional(2-D) discrete systems described by the general model (GM) subject to both state and input delays. The parameter uncertainties are assumed norm-bounded. A linear matrix inequality (LMI)-based sufficient condition for the existence of non-fragile robust guaranteed cost controller is established. Furthermore, a convex optimization problem with LMI constraints is proposed to select a non-fragile robust optimal guaranteed cost controller stabilizing the uncertain 2-D discrete system with both state and input delays as well as achieving the least guaranteed cost for the resulting closed-loop system. The effectiveness of the proposed method is demonstrated with an illustrative example. [ABSTRACT FROM AUTHOR]

In this paper, a model predictive control algorithm is presented for linear parameter varying systems with both state delays and randomly occurring input saturation. The input saturation is assumed to be occurred randomly with Bernoulli-distributed white sequences. A constant sate feedback law is designed at each time instant to ensure the robust stability of the closed-loop system with respect to polytopic uncertainties. The optimization of model predictive controller is cast into solving a linear matrix inequalities optimization problem. Then, the results are extended to gain-scheduled approach in which a set of state feedback laws are designed for each vertex of the system model. The state feedback law is scheduled by the time varying model parameters to achieve less conservatism in controller design. Finally, two examples are employed to show the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, the robust non-fragile stabilisation and H∞ control problem is investigated for a class of uncertain discrete-time stochastic systems with Markovian jumping parameters and time-varying delay. The parameter uncertainties are supposed to be time-varying as well as norm-bounded. The aim of the robust non-fragile stabilisation problem is to design a non-fragile state feedback controller which guarantees the robust stability of the closed-loop system for all admissible uncertainties. At the same time, in addition to the robust stability requirement, a prescribed H∞ performance level is required to be achieved for the robust H∞ control problem. By Lyapunov stability theory, delay-segment-dependent conditions for the solvability of these problems are formulated in terms of linear matrix inequality technique. Finally, numerical examples are shown to demonstrate the usefulness and applicability of the proposed design method. [ABSTRACT FROM AUTHOR]

A systematic method is developed to design new robust tracking control for an electro-hydraulic system. In this article, the model dynamics is represented in strict feedback form to be linearly parameterized with regard to model parameters to consider its uncertainties and the separate error dynamics are derived for the tracking of states. The proposed method is intuitively similar to a previously developed integrator backstepping approach since it designs fictitious controls as desired states for recursive procedures. However, unlike previous works that have relied exclusively on Lyapunov analysis using the sum of the squares of state errors, this work utilizes a passivity approach with an energy function for stability. The advantage of the proposed method is that the fictitious control can be designed in a decoupled form and an L2-gain analysis can be applied to guarantee the robustness to model uncertainties. For the separate error dynamics, one part of the fictitious control is designed using stability analysis with a strict passivity formulation for error convergence; this leads to a separation of the other fictitious control design for robustness from the effects of other equations of dynamics. Hence, this method makes it possible to treat the terms caused by the model uncertainties as disturbance and to design the fictitious robust control using the L2-gain analysis for the disturbance attenuation, which does not require the bound function of the disturbance to be known. The validity of the proposed method is shown through simulations. [ABSTRACT FROM AUTHOR]

This paper presents the robust delay-dependent H∞ filter design for uncertain stochastic systems with time-varying delay. The systems under consideration are subject to time-varying norm-bounded parameter uncertainties and time-varying delays in both the state and measurement equations. The aim is to design a stable full-order linear filter assuring robustly asymptotical stability in mean-square and a prescribed H∞ performance level for the filtering error systems. Based on the delay decomposition approach and a new integral inequality, delay-dependent sufficient conditions for the existence of H∞ filters are proposed in terms of linear matrix inequalities. Finally, two numerical examples demonstrate that the proposed approaches are effective and are an improvement over existing ones. [ABSTRACT FROM AUTHOR]

This paper considers the problem of time-varying force control for robot manipulators in the presence of uncertainties from both the robotic model and working environment. The position-based impedance control (PBIC) method is employed and in order to achieve accurate time-varying force tracking, an improved PBIC is proposed. A neural-network-based robust controller is proposed to compensate for the system uncertainties, and an adaptive law is developed to identify the uncertain environmental parameters. Simulation results on a two-link robot manipulator confirm the effectiveness of the method in achieving time-varying force tracking. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of non-fragile robust optimal guaranteed cost control for a class of two-dimensional discrete systems described by the general model with norm-bounded uncertainties. Based on Lyapunov method, a new linear matrix inequality (LMI)-based criterion for the existence of non-fragile state feedback controller is established. Furthermore, a convex optimization problem with LMI constraints is formulated to select a non-fragile robust optimal guaranteed cost controller, which minimizes the upper bound of the closed-loop cost function. The merit of the proposed criterion in aspect of conservativeness over a recently reported criterion is demonstrated with the help of illustrative examples. [ABSTRACT FROM AUTHOR]

In this paper a robust H2/H∞ multi-objective state-feedback controller and tracking design are presented for a class of multiple input/multiple output nonlinear uncertain systems. First, some states (error of tracking) are augmented to the system in order to improve tracking control. Next, uncertain parameters and the quantification of uncertainty on physical parameters are defined by the affine parameter-dependent systems method. Then, to apply the H2/H∞ controller, the uncertain nonlinear system is approximated by the Takagi–Sugeno fuzzy model. After that, based on each local linear subsystem with augmented state, an H2/H∞ multi-objective state-feedback controller is designed by using a linear matrix inequalities approach. Finally, parallel distributed compensation is used to design the controller for the overall system and the total linear system is obtained by use of the weighted sum of the local linear subsystems. Several results show that the proposed method can effectively meet performance requirements such as robustness, good load disturbance rejection, good tracking and fast transient responses for a three-phase interior permanent magnet synchronous motor system. [ABSTRACT FROM AUTHOR]

An approach for the design of a momentum unloading system for a spacecraft controlled by momentum exchange actuators is proposed. The approach is based on formulating momentum unloading as a control problem of a parametrically perturbed periodic system. An equivalent discrete-time robust control design problem is formulated using a point-mapping-based numerical algorithm. Established robust control analysis can then be used to develop and analyse the robustness of the unloading control law. The method is demonstrated on an Earth-pointing satellite with magnetic torquers serving as unloading actuators. Earth's magnetic field is modelled as a tilted dipole that consists of a periodic term and bounded perturbations. A discrete-time representation of the system is used to develop an unloading control law and the structured singular value theory is employed to analyse its robustness to parametric uncertainties. [ABSTRACT FROM AUTHOR]

The paper considers control systems with parametric as well as unstructured uncertainty. Parametric uncertainty is modelled by a transfer function whose numerator and denominator polynomials are independent uncertain polynomials of the form of P (s, q) = l [sub 0] (q) + l[sub 1] (q)s + … + l[sub n] (q)s[sup n] where the coefficients depend linearly on q = [q [sub 1], q[sub 2], …, q[sub q]][sup T] and the uncertainty box is Q = {q: q[sub i]∈[q[sub i], q[sub i]], i = 1, 2, …, q}. The unstructured uncertainty is modelled as H[sub ∞] norm bounded perturbations and perturbations consisting of a family of nonlinear sector bounded feedback gains. Using the geometric structure of the value set of P(s, q), some results are presented for determination of the robust small gain theorem, robust performance, strict positive realness and absolute stability problem of control systems with parametric as well as unstructured uncertainty. Numerical examples are given to illustrate application of the proposed methods. [ABSTRACT FROM AUTHOR]