This study focuses on the design of fixed-time consensus for first-order multi-agent systems with unknown inherent non-linear dynamics. A distributed control protocol, based on local information, is proposed to ensure the convergence of the tracking errors in finite time. Some conditions are derived to select the controller gains in order to obtain a prescribed convergence time regardless of the initial conditions. Simulations are performed to validate the theoretical results.

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International audienceThis paper deals with robust fault detection for non-linear systems. This problem is usually solved by designing an observable subsystem which is only affected by the fault and not by the control and disturbance inputs. However, such a subsystem may not exist so that the so-called fundamental problem of residual generation (FPRG) is not solvable. The aim of the present paper is to design a fault detection filter when the conditions for the existence of a solution to the non-linear FPRG are not satisfied. Our approach is made in a geometric context. Under some decoupling assumptions, the design of sliding mode observers allows us to reconstruct the disturbance inputs and then to generate an effective residual. An illustrative example is given throughout the paper

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This paper focuses on the design of fixed-time consensus for multiple unicycle-type mobile agents. A distributed switched strategy, based on local information, is proposed to solve the leader-follower consensus problem for multiple nonholonomic agents in chained form. The switching times and the prescribed convergence time are explicitly given regardless of the initial conditions. Simulation results highlight the efficiency of the proposed method.

This paper deals with state and fault estimations for a class of uncertain Lipschitz nonlinear systems. The proposed scheme combines a descriptor form observer and an adaptive sliding mode observer. The adaptive descriptor sliding approach is proposed to jointly estimate the state and reconstruct the sensor fault while rejecting the effect of uncertainties and Lipschitz nonlinearities. Using a Lyapunov analysis, the stability condition is analyzed and the observer gains are designed such that the reduced-order system is practically stable. Then, an adaptive super-twisting observer is derived to reconstruct the actuator faults. The main feature of the proposed adaptive scheme is that it does not overestimate the observer gains, which mitigates chattering in the presence of bounded uncertainties, actuator, and sensor faults with unknown boundaries. Simulation results for a robotic manipulator show the effectiveness of the proposed method.constraining the system motion along manifolds of reduced dimensionality in the state space and is applicable to a broad variety of practical applications. Robustness properties against various kinds of uncertainties such as parameter perturbations and external disturbances can be guaranteed.Several SMO-based FDI approaches have been developed recently for linear systems and a class of nonlinear systems. The first-order SMOs in [14][15][16] are designed to directly tackle the actuator faults through the sliding mode terms via some transformations. Unfortunately, the realization of first-order sliding mode implies the undesirable chattering phenomenon, that is, high-frequency vibrations of the closed-loop system, which may excite unmodeled high-frequency dynamics, degrade the system performances, and lead to instability. Other techniques rely on a nonlinear coordinate transformation to obtain a form suite for the design of high-gain observer [17,18]. The gains are designed based on the inverse jacobian of the state transformation. However, this transformation is valid only locally and no sensor fault is considered. Recently, some SMOs have been proposed to reconstruct both the system state, actuator faults, and sensor faults. In [19,20], actuator fault is reconstructed with the so-called equivalent output estimation error injection concept. Then, sensor fault is estimated via a low-pass filter and appropriate secondary SMO. Nevertheless, the sensor fault estimation is very sensitive to the filter parameters, and no uncertainty acting on the system is considered. An SMO is proposed in [21,22] to solve this problem for nonlinear systems without uncertainty assuming some conditions on the fault distribution matrix. Descriptor sliding mode approaches are introduced in [5, 23] to simultaneously reconstruct state, sensor, and actuator faults for linear systems. In [24], a sliding mode unknown input observer is given for linear systems where the same additive perturbation affects both the dynamics and the output. In [25], a H 1 SMO is derived for a class of uncertain nonlinear Lipschitz sy...