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ℒ 1 adaptive control method is a new and effective technique to perform a robust tracking response with fast adaptation, its applications were generalized for both integer order affine and non-affine nonlinear systems. However, ℒ 1 adaptive control scheme has not been yet investigated to control fractional order systems. In this work, an ℒ 1 adaptive controller is designed based on a fractional order sliding surface to control a class of fractional order systems with matched uncertainties and external disturbances. The developed controller is able to achieve closed-loop stability with uniform performance bounds for system signals. Such that, increasing the adaptation gain can dramatically enhance the performances for both transient and steady state responses. Finally, the validity and the effectiveness of the controller is tested using a numerical simulation.

In this paper, an adaptive fuzzy control scheme is developed for a class of uncertain multi-input multi-output (MIMO) nonlinear systems with uncertain actuator failures. Within this scheme, a fixed grouping of redundant actuators and a proportional actuation scheme for each group are considered. Afterward, a fuzzy system is used for the online approximation of a nominal control vector. The parameter adaptation laws for the nominal controller are driven by the prediction error, which is defined as the difference between the unknown nominal controller and the fuzzy controller. The proposed controller can compensate for both total and partial loss of effectiveness failure types. Closed-loop system stability and convergence properties of the tracking errors are proved based on Lyapunov theory. A piecewise analysis is carried out to prove that these properties hold despite the presence of parameter jumps caused by abrupt failures. A simulation study is carried out on a redundant joint two-link robot manipulator with joint failures at each link. Simulation results show the effectiveness of the proposed control scheme.

International audience; This paper presents a new fuzzy output feedback control design for autonomous vehicle steering under actuator saturation, unavailability of the sideslip angle measurement, unknown road curvature, and lateral wind force. To take into account the actuator constraint, the saturation effect is transformed into dead-zone nonlinearity. A static output controller based on non-compensation parallel distributed technic and a Takagi-Sugeno (T-S) model of vehicle lateral dynamics is proposed to consider the unavailability of some vehicle states. To avoid the problem of imposing bounds on membership functions time derivatives resulting from the use of the fuzzy Lyapunov approach, a proper integral structure based on the non-quadratic Lyapunov approach is investigated. The mixed [Formula: see text] stabilization conditions of the augmented closed-loop system are expressed in terms of linear matrix inequalities (LMIs). Finally, the robustness and the advantages of the proposed approaches are demonstrated through different tests.

International audience; This paper presents a new H∞ anti-windup dynamic output control developed for a class of nonlinear systems subject to both sensors and actuators saturations. First, the nonlinear system under consideration is represented by a Takagi-Sugeno (T-S) fuzzy model. Then, a dynamic output feedback (DOF) control strategy is proposed and the saturation constraints are transformed into dead-zone nonlinearities. Based on this transformation and by using the sector condition, the H∞ dynamic output stabilization conditions of the closed-loop system are given in terms of linear matrix inequalities (LMI). Finally, a numerical example is simulated to demonstrate the efficiency of the proposed design method.

This paper presents a new strategy for a robust maximum power point (MPP) tracking fuzzy controller for photovoltaic (PV) systems subject to actuator asymmetric saturation. A DC-DC boost converter is used to connect a PV panel with an output load. The output voltage of the DC-DC boost converter can be adjusted by duty ratio that is limited between 0 and 1. The aim of our control design is to track the MPP under atmospheric condition changes and the presence of the asymmetric saturation of the duty ratio. To minimize tracking error and disturbance effect, the dynamic behaviour of a PV system and its reference model are described by using Takagi–Sugeno fuzzy models. Then, a constrained control based on a fuzzy PI state feedback controller is proposed. The H∞ control approach is used in control design and stability conditions of the closed-loop system are formulated and solved in terms of linear matrix inequalities. Finally, simulation results are given to show the tracking performance of the control design.

In this paper, we focus on the direct adaptive fuzzy control design for a class of uncertain MIMO nonlinear systems with indefinite control gain matrix and unknown control direction. The control design is based on the approximation of an unknown ideal control law that can meet the control objective by using fuzzy systems. The adjustable parameters of the used fuzzy systems are adjusted online using the error between the unknown ideal controller and the fuzzy controller. In this paper, unlike most existing works, the Nussbaum gain technique is not used to overcome the obstacle of the unknown control direction. In fact, with the help of a matrix decomposition technique, the unknown control direction is redefined as an unknown constant vector, which is estimated online by a suitable update law. The stability of the closed-loop system is studied using the Lyapunov direct approach. Numerical simulation results are provided to illustrate the effectiveness of the proposed control design approach.

This paper addresses the design of an observer-based adaptive neural network chaos synchronization scheme for a general class of uncertain chaotic systems. The controller consists of an adaptive neural network control law and an extended state observer. The parameterization of the designed extended observer and the sufficient stability conditions are derived in the light of the singular perturbation theory. The extended observer is incorporated into the controller to reconstruct the synchronization error vector as well as to estimate the error between an ideal control law and the actual control. These estimated error signals are utilized in the adaptation mechanism of the neural network weight vector. In the presented chaos synchronization method, the knowledge of the models of the master–slave systems is not required and the controller only needs the projective synchronization error for its implementation. Numerical simulations are performed along with a comparative study to demonstrate the efficiency and effectiveness of the suggested chaos synchronization approach.

This paper investigates the depth control of unmanned underwater vehicle (UUV) in the presence of external disturbances, time delay, and actuator saturation. Firstly, a Takagi–Sugeno model is elaborated based on the speed variation. Then, using the polyquadratic representation with Lyapunov-Krasovskii function, an H∞ criteria is applied to design a new LMI based stabilization to calculate PDC controller gains taking into consideration the input limitation. Finally, simulation results demonstrate the superiority of the proposed approach.

This paper presents an immersion and invariance based adaptive dynamic control scheme for a class of parametric strict-feedback nonlinear systems. By using dynamic surface control, the problem of explosion of complexity inherent in the conventional backstepping control method is avoided. The uniformly ultimately boundedness (UUB) of all signals in the closed-loop system is performed based on the Lyapunov stability analysis theory. Simulation results for the control of a one-link manipulator actuated by a brush DC motor are provided to demonstrate the effectiveness of the proposed adaptive control method.

International audience; In this paper, we present a new scheme for designing a H∞ stabilizing controller for discrete-time Takagi-Sugeno fuzzy systems with actuator saturation and external disturbances. The weighting-dependent Lyapunov functions approach is used to design a robust static output-feedback controller. To address the input saturation problem, both constrained and saturated control input cases are considered. In both cases, stabilization conditions of the fuzzy system are formulated as a convex optimization problem in terms of linear matrix inequalities. Two simulation examples are included to illustrate the effectiveness of the proposed design methods. A comparison with the results given in recent literature on the subject is also presented.

This paper addresses the design of a new adaptive attitude tracking control strategy for a flexible spacecraft system in the presence of external disturbances and uncertain failures of redundant reaction wheels. The proposed strategy does not depend on the exact model of the spacecraft and can compensate for a more general class of time-varying and state-dependent failures that reaction wheels may undergo in a practical environment. The controller is built around an integrated adaptive approximation based design that accommodates for both system and actuator failure uncertainties. The controller parameter update law is derived to minimise the control prediction error. Unlike most existing actuator failure compensation techniques which are limited to special cases of actuator failures, the proposed controller can compensate for a larger set of actuator failures with time-varying patterns. Closed-loop system stability and tracking performance are proved using Lyapunov stability theory. Parameter jumps caused by abrupt failures are also taken into consideration during the stability proof. A simulation study is performed on a spacecraft with flexible solar array actuated by redundant reaction wheels. The results show the effectiveness and feasibility of the proposed actuator failure compensation controller compared to other controllers from the literature.

This paper introduces an adaptive command filtered backstepping control approach for lower triangular uncertain nonlinear dynamic systems. In this approach, the drawback of explosion of terms in the conventional adaptive backstepping control strategy is avoided. By using the Lyapunov stability analysis method, it is shown that all the closed-loop system signals are uniformly ultimately bounded (UUB) and the convergence of the system tracking errors to a small neighborhood of zero. The proposed adaptive filtered control strategy has been applied to a permanent magnet synchronous generator (PMSG) based wind energy conversion system (WECS) model. Numerical simulation results for the control of PMSG based WECS model are provided to highlight the performance of the adaptive command filtered based backstepping control strategy. The numerical simulations results clearly prove the efficiency of the proposed adaptive filtered control strategy.

International audience; In this paper, a direct adaptive fuzzy control scheme is presented for a class of uncertain MIMO nonlinear systems with unknown sign of the control gain matrix. Within this scheme, fuzzy systems are employed to approximate an unknown ideal controller which can achieve the control objectives. The free parameters of the used fuzzy systems are adjusted online using the error between the unknown ideal controller and the fuzzy controller. Unlike previous works, the Nussbaum function technique is not used to deal with the unknown sign of the control gain matrix. In fact, in this paper, the unknown control direction is considered as an unknown constant parameter and it is estimated online by an appropriate adaptation law. The stability of the closed-loop system is analyzed using a Lyapunov approach. Simulation results are given to demonstrate the effectiveness of the proposed control design.

An adaptive state observer is an adaptive observer that does not require the persistent excitation condition to estimate the state. The usual structural requirement for designing this kind of observers is that the unknown parameters explicitly appear in the measured state dynamics. This paper deals with the problem of adaptive state observer synthesis for a class of nonlinear systems with unknown parameters in unmeasured state dynamics. The novelty of the proposed approach is that it requires neither a canonical form nor the approximation of some of the output’s time derivatives. Firstly, we establish a new matrix equality that characterizes the structure of almost all systems found in the very small literature dealing with this problem. Then, this equality is exploited in the construction of the adaptation law. This simplifies the design procedure and makes it very similar to the conventional adaptive state observer design procedure. The problem of finding the observer gains is expressed as a linear matrix inequalities optimization problem. Two examples are given to demonstrate the validity of the proposed scheme.

In this paper, a composite adaptive dynamic surface control scheme is developed for a class of parametric strict-feedback nonlinear systems. The proposed composite adaptation law uses both the surface error and the estimation error to update the parameters. In addition, by using the dynamic surface control technique, the problem of the explosion of complexity in the adaptive backstepping design is avoided. It is proved that the proposed scheme guarantees uniform ultimate boundedness of all signals in the closed-loop system with arbitrary small surface error by adjusting the design parameters. Simulation results demonstrate the effectiveness of the proposed approach for an electrohydraulic actuator system.

This paper proposes a direct adaptive fuzzy position controller for an electropneumatic actuator. This control approach is based on the estimation of an ideal inverse dynamic control, which is capable of satisfying the control objectives in terms of tracking desired trajectories and stability. For this purpose, a fuzzy system is used to approximate as best as possible this ideal inverse dynamic control. The adjustable parameters of the used fuzzy system are updated online with a stable adaptation law. This latter is developed in order to minimize the error between the unknown ideal inverse dynamic control and the used fuzzy controller. The general non-affine model of the controlled actuator is used to develop the control law and its adaptation mechanism, meaning that the presented adaptive fuzzy position controller does not rely on a physical model of the pneumatic actuator. The stability of the closed-loop system is performed using a Lyapunov approach. Finally, real-time experimental results are presented to show the performances of the proposed position controller.

This paper proposes an ℒ1 fuzzy adaptive controller for a class of uncertain continuous-time single-input single-output nonaffine nonlinear systems. The structure of this controller is derived based on ℒ1 adaptive control design methodology and integrates a fuzzy system. The latter is used to approximate as best as possible a function of an unknown ideal implicit controller, which provides good results and improves the performance significantly. The ℒ1 fuzzy adaptive controller consists of a predictor, a control law and its adaptive laws. The major advantage of the proposed control scheme is its ability to guarantee uniformly bounded transient and tracking performance for the controlled system. These performance bounds can be rendered arbitrarily small by the systematic choice of design parameters. The effectiveness and feasibility of the proposed ℒ1 fuzzy adaptive controller are examined experimentally in the position control of a pneumatic actuator system.