In the last few years, researchers have applied ML strategies in the context of cooperative transportation to increase safety and efficiency. The RL paradigm has been successfully employed in the longitudinal spacing control of CACC systems, but ensuring disturbance rejection remains a challenge. Characteristics such as uncertain parameters in the formation and external interference may imposed constant errors, preventing the agents from reaching the desired formation when traveling at cruising speed. Therefore, this paper proposes a new training model for platooning, such that the acceleration command of each agent provides a stable formation, according to the nonlinear control formalism. The design methodology is general enough to be applied across multiple topologies. Also, the outputs of the controllers design by ML have a term with integral action capabilities, enabling corrective actions to withstand uncertainties that are constant. The effectiveness of our proposal has been illustrated with experiments using different network topologies, uncertain parameters, and external forces. Comparative analyses were conducted against the state-of-the-art, whose findings offer new insights concerning the generalization and robustness of using () in the control of autonomous platoons.

Autonomous platoons are good alternatives for cargo transportation, with many approaches to safety and efficiency issues. In the last decades, much effort has been employed in this context, with solutions ranging from low-level control laws to learning strategies. Although some papers have concentrated on methods robust to disturbances or resilient to disruptions, most of the current literature assumes that the platoon starts and remains connected all the time, even when subjecting vehicles to limited communication ranges and time-delay. Therefore, in this paper, we address the problem of connectivity maintenance and stability of platoons under the complete disconnection of vehicles. The main goal is to increase the tolerance to agent exits, external elements (such as traffic lights and human-driven vehicles), and non-ideal initial conditions, improving traffic safety in mixed traffic scenarios. By modeling the network connection as a Directed Acyclic Graph, we use a state-machine policy to recover connectivity and regulate the spacing distance to other vehicles under heterogeneous time-delays. We demonstrate that this state alteration can be interpreted as a reference tracking problem and propose a design procedure that provides stability and zero spacing error in steady-state. Our control protocol allows vehicles to reach a consensus with the team, even when they start disconnected from other ones. Results with agent-based and nonlinear simulations illustrate the effectiveness of our approach.

The problem of decentralized control of vehicular platoon is addressed, subject to non-uniform time-varying delays in the communication links among the vehicles. Our control goal is to drive the vehicles to reach a specified line formation, while moving forward according to the platoon leader speed. The control law in each vehicle is computed only by local relative information regarding its neighboring vehicles. The first result shows that communication delays cause steady-state formation error, whose value can be analytically calculated from the system data, and may lead to instability, if the delays values surpass a threshold. Hence, in the light of this finding, we present a decentralized control law to compensate for the delay effect, providing zero steady-state formation error. The paper is concluded by showing simulated experiments over six different communication topologies illustrating the applicability of our proposal.

This paper addresses a method for stabilizing the attitude of a quadrotor-like Unmanned Aerial Vehicle (UAV). Towards this end, a new fuzzy control technique based on Linear Matrix Inequalities (LMIs) is proposed, relying on a simple Takagi-Sugeno (TS) fuzzy model when compared to others found in the Literature. This strategy guarantees prescribed performance criteria, such as exponential decay rate, allowing stability under aggressive disturbances. The designed local gains are combined through membership functions of the model and are used to regulate the attitude of the vehicle on the hovering operating point, an important flight mode for this kind of robot. Numerical analysis reveal how the transient response can be tuned and tests with disturbances show the stability of the vehicle starting from different initial conditions, illustrating the merits of the proposed approach.

This paper presents a method for stabilizing the attitude of a Hybrid Unmanned Aerial Underwater Vehicle. Firstly, we present aerodynamic and hydrodynamic models for the angular motion of our robot, discussing effects like buoyancy force and added inertia. Next, we apply robust control techniques for both environment, aerial and underwater, based on linear uncertain models with only four vertices and well-defined stability criteria, such as D-stability and ℋ2performance. Gain matrices Kairand Kwatare computed and the attitude of the vehicle at the hovering operation point for each environment is controlled, respectively. Finally, a procedure is proposed to check the global stability for the switching control case, when the robot changes from air to water (or vice-versa). Numerical simulations with disturbances and switching control are presented to show the stability at different initial conditions.

The static output feedback control (SOFC) for Takagi-Sugeno (TS) fuzzy systems is addressed in this paper. Based on Lyapunov theory the proposed methods are formulated as Linear Matrix Inequalities (LMIs). To obtain less conservative conditions the properties of membership functions time-derivative are explored. Wiht this new methodology SOFC with higher H∞attenuation level can be designed. Moreover, the method is extended to local stabilization using the concepts of invariant ellipsoids and regions of stability. These local conditions overcome some difficulties associated with estimating bounds for the timederivative of the membership functions. Examples are given to illustrate the merits of the proposed approaches.

In this paper, the stability analysis and control design of Takagi-Sugeno (TS) fuzzy systems subject to uncertain time-delay are addressed. The proposed approach is based on linear matrix inequalities and the Lyapunov-Krasovskii theory, where a new fuzzy weighting-dependent Lyapunov-Krasovskii functional is introduced. By employing the Gu discretization technique and strategies to add slack matrix variables, less conservative conditions are obtained. Numerical experiments are performed to illustrate the effectiveness of the proposed methodology.