A new predictive scheme is proposed for the control of Linear Time Invariant (LTI) systems with a constant and known delay in the input and unknown disturbances. It has been achieved to include disturbances effect in the prediction even though there are completely unknown. The Artstein reduction is then revisited thanks to the computation of this new prediction. An extensive comparison with the standard scheme is presented throughout the article. It is proved that the new scheme leads to feedback controllers that are able to reject perfectly constant disturbances. For time-varying ones, a better attenuation is achieved for a wide range of perturbations and for both linear and nonlinear controllers. A criterion is given to characterize this class of perturbations. Finally, some simulations illustrate the results.

International audienceIt is well-known that standard predictive techniques are not very robust to parameter uncertainties and to external disturbances. Furthermore, they require the exact knowledge of the delay. In practice, these constraints are rarely satisfied. In this paper, solutions are presented to allow the use of predictive control in presence of external disturbances, parameter uncertainties and an unknown input delay. First, a recent predictive control method developed to attenuate the effect of external disturbances is shown to be also robust to parameter uncertainties. In addition, a delay estimator is presented to estimate unknown time-varying delays. Theoretical results are widely illustrated through experimental tests on a DC motor

A clinical pathway in a post-anaesthesia care unit can significantly reduce length of stay and can improve post-operative outcome.

This article deals with robust fixed-time stability and stabilization. First, new global robust fixed-time stability results are proposed for scalar systems by using constant and variable exponent coefficients. Then, they are applied to global robust fixed-time stabilization of a class of uncertain nonlinear secondorder systems by using sliding mode control. All the results are illustrated in simulation.

A predictor-based controller combined with two event-triggering mechanisms is proposed in order to control an LTI system over a network. The controller is designed in the discrete-time domain which allows to deal with a long sampling period. Similarly large input and output delays can be compensated thanks to the use of a predictor-based method. Two eventtriggering mechanisms, in the sensor-to-controller and controller-to-actuator channels are introduced in order to limit the number of packets sent over the network while preserving the ultimate boundedness of the solutions. The effect of input and output quantization introduced by the network is considered in the stability analysis. The results are illustrated by simulation.

No abstract

This article provides an online time-varying delay estimation method by using an external signal sent along with control inputs and output measurements. Since the external signal is isolated from the system, the linearity and delay-identifiability of the system are no longer required. Moreover, the sliding mode method guarantees the finite-time convergence of the delay estimation. By comparing with the standard sliding mode method, the super-twisting algorithm reduces the chattering and provides better performances. Furthermore, the super-twisting algorithm based delay estimator is implemented on a real remote data transmission system and its performances are illustrated by experimental results.

SUMMARYThis paper deals with the problem of state and delay estimation for SISO nonlinear systems with an unknown time-varying delay in the input. The main idea is to approximate the delayed input by using Taylor's theorem and to create an extended system with the delay as part of the extended state. Then, the construction of an observer is proposed to estimate both state and delay. The results are illustrated by simulations.