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International audience; In this paper, an event-triggered joint adaptive high-gain observer design method is proposed for a class of nonlinear systems that are characterized by an unknown parameter entering the system state equations. The main difficulty in the observer design is that the event-triggered mechanism (ETM) is affected by variable delayed-sampled data and the system’s unknown parameter. To overcome this difficulty, a closed-loop output predictor is incorporated into the design of the event-triggered mechanism to compensate for the sampling and the delay affecting the system outputs. To prevent the Zeno phenomenon, and to guarantee the exponential convergence of the observer, an exponential decay factor is considered in the ETM. The effectiveness of our proposed observer is demonstrated through numerical simulations, experiments and performances comparison with previous works in the literature.

International audience; This paper investigates a distributed fuzzy adaptive formation control for quadrotor multiple unmanned aerial vehicles under unmodeled dynamics and switching topologies. The unmanned aerial vehicles dynamics model is described by the Newton-Euler formula, and the actuator faults are considered in the system model in the form of multiplicative factors and additive factors. Due to the underactuated characteristics of the unmanned aerial vehicles, two objective attitude commands are generated by designing a virtual control signal, which are transmitted to the attitude subsystem, and then the position controller is solved. By constructing a distributed communication mechanism between unmanned aerial vehicles, an adaptive formation control strategy is proposed, which can enable unmanned aerial vehicles to update their position and speed online according to their neighbor information, and then achieve the required formation. In addition, a fuzzy adaptive sliding mode controller is designed to ensure that the tracking errors of unmanned aerial vehicles converge to the neighborhood of the origin. Finally, the simulation results verify the effectiveness of the proposed control strategy.

International audience; This paper concerns optimal prescribed-time forma- tion control for a class of nonlinear multi-agent systems (MASs). Optimal control depends on the solution of the Hamilton-Jacobi- Bellman equation, which is hard to be calculated directly due to its inherent nonlinearity. To overcome this difficulty, the rein- forcement learning strategy with fuzzy logic systems is proposed, in which identifier, actor, and critic are used to estimate unknown nonlinear dynamics, implement control behavior, and evaluate system performance, respectively. Different from the existing optimal control algorithms, a new performance index function considering formation error cost and control input energy cost is constructed to achieve optimal formation control of MASs within a prescribed time. The presented control strategy can ensure that the formation error converges to the desired accuracy within a prescribed time. Finally, the validity of the presented strategy is verified via a simulation example.

International audience; The adaptive fuzzy predefined-time tracking control problem for a class of nonlinear systems with output hysteresis is investigated in this article. An inverse model is utilized to capture the output hysteresis phenomenon, and then the Nussbaum-type function technique is utilized to overcome the difficulty of unknown time-varying control gain caused by output hysteresis. An adaptive fuzzy control scheme under the backstepping framework is developed using the predefined-time stability criterion. Different from the existing predefined-time design approaches, the adaptive law designed in this article is represented as a nonlinear differential equation. Theoretical analysis demonstrates that all signals of the closed-loop systems are bounded, and the tracking error can converge to the neighborhood near origin within an expected settling time. The developed scheme's feasibility is verified by an example of an electromechanical system.

International audience; Exponentially weighted moving average (EWMA) control charts for time-between-events (TBE) are commonly suggested to monitor high-quality manufacturing processes for the early detection of process deteriorations. In this study, an enhanced one-sided EWMA TBE control chart is developed for rapid detection of increases or decreases in the process mean. The use of the truncation method helps to improve the sensitivity of the proposed scheme in the process mean detection. Moreover, by taking the effects of parameter estimation into account, the proposed scheme with estimated parameters is also investigated in this paper. Both the average run length (ARL) and standard deviation of run length (SDRL) performances of the proposed scheme with known and estimated parameters are studied using the Markov chain method, respectively. Furthermore, an optimal design procedure is developed for the recommended one-sided EWMA TBE chart based on ARL. Numerical results show that the proposed optimal one-sided EWMA TBE chart is more sensitive than the existing optimal one-sided exponential EWMA chart in monitoring both upward and downward mean shifts. Meanwhile, it also performs better than the existing comparative scheme in resisting the effect of the parameter estimation. Finally, two illustrative examples are considered to show the implementation of the recommended one-sided EWMA TBE scheme for simulated and real datasets. For the real dataset of F-16 aircraft, the accident occurrences of F-16 are very low, in which case monitoring the time between two successive F-16 accidents is more efficient than the traditional monitoring schemes. Moreover, the usage of the truncation method makes the proposed one-sided EWMA TBE chart more appropriate for monitoring those two datasets with known shift directions, and the operation of the proposed scheme is simple which does not need to predetermine the reflecting boundary of the existing comparative scheme.

International audience; In this article, an observer‐based adaptive boundary iterative learning control law is developed for a class of two‐link rigid‐flexible manipulator with input backlash, the unknown external disturbance, and the endpoint constraint. To tackle the backlash nonlinearities and ensure the vibration suppression, the disturbance observers based upon the iterative learning conception are considered in the adaptive boundary control design. A barrier Lyapunov function is incorporated with boundary control law to restrict the endpoint state. Based on the defined barrier composite energy function, the tracking angle error convergence of the rigid part is guaranteed, and the vibrations of the flexible part are suppressed through the rigorous analysis. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed control.

International audience; Exponential type charts are useful tools to monitor the time between events in high-quality processes with a low defect rate. Most studies on exponential charts are 1 designed with the average run length (ARL) metric. The only use of ARL in the design of control charts is sometimes criticized because the shape of the run length (RL) distribution of control charts changes with the shift size. In fact, the RL distribution of the exponential exponentially weighted moving average (EWMA) chart is skewed, especially when the process is in-control. Hence, the median run length (M RL) serves as a more meaningful indicator. Moreover, in some situations, the shift size in the process is unknown in advance. Under this case, the expected median run length (EM RL) can be used as the metric. In this paper, the RL properties of both the upper-and lower-sided exponential EWMA charts are studied through a Markov chain approach. Two optimal design procedures are developed for one-sided exponential EWMA charts based on the M RL and EM RL, respectively. The choices of reflecting boundaries for one-sided exponential EWMA charts are discussed through many numerical studies. The M RL and EM RL performances of the one-sided exponential EWMA charts are investigated.

International audience; The design of fast and effective coordination among sensors and actuators in Cyber-Physical Systems (CPS) is a fundamental, but challenging issue, especially when the system model is a priori unknown and multiple random events can simultaneously occur. We propose a novel collaborative state estimation and actuator scheduling algorithm with two phases. In the first phase, we propose a Gaussian Mixture Model (GMM)-based method using the random event physical field distribution to estimate the locations and the states of events. In the second phase, based on the number of identified events and the number of available ac-tuators, we study two actuator scheduling scenarios and formulate them as Integer Linear Programming (ILP) problems with the objective to minimize the actuation delay. We validate and demonstrate the performance of the proposed scheme through both simulations and physical experiments for a home temperature control application.

International audience; A new generic object recognition (GOR) method for robot perception is proposed in this paper, based on multi-feature fusion of two-dimensional (2D) and 3D scale invariant feature transform descriptors drawn from 2D images and 3D point clouds. The trained support vector machine is utilized to construct multi-category classifiers that recognize the objects. According to our results, this new GOR approach achieves higher recognition rates than classical methods tested, even when one has large intra-class variations, or high inter-class similarities of the objects. Simulation results demonstrate the effectiveness and efficiency of the proposed GOR approach.