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.

This 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.

The control of electric-power-assisted steering (EPAS) systems is a challenging problem due to multiple objectives and the need for several pieces of information to implement the control. The control objectives are to generate assist torque with fast responses to driver's torque commands, ensure system stability, attenuate vibrations, transmit the road information to the driver, and improve the steering wheel returnability and free-control performance. The control must also be robust to modeling errors and parameter uncertainties. To achieve these objectives, a new control strategy is introduced in this paper. A reference model is used to generate an ideal motor angle that can guarantee the desired performance, and then, a sliding-mode control is used to track the desired motor angle. This reference model is built using a dynamic mechanical EPAS model, which is driven by the driver torque, the road reaction torque, and the desired assist torque. To implement the reference model with a minimum of sensors, a sliding-mode observer with unknown inputs and robust differentiators are employed to estimate the driver torque, the road reaction torque, and the system's states. With the proposed control strategy, there is no need for different algorithms, rules for switching between these algorithms, or fine-tuning of several parameters. In addition, our strategy improves system performance and robustness and reduces costs. The simulation results show that the proposed control structure can satisfy the desired performance.

In this paper, decentralized motion planning and scheduling of automated guided vehicles (AGVs) in a flexible manufacturing system (FMS) is proposed. A motion planner is combined with a scheduler allowing each AGV to update its destination resource during navigation in order to complete the transported product. The proposed strategy is based on two steps. The first step consists in planning a presumed trajectory to avoid collision conflicts previously detected by a central supervisor, enabling more appropriate decentralized scheduling by AGVs. The presumed trajectories, which represent the intentions of AGVs, are then exchanged with neighboring AGVs. The second step uses the presumed trajectories of neighbors to compute a collision-free trajectory according to the priority policy. Numerical and experimental results are provided to show the pertinence and the feasibility of the proposed strategy.