Leonardo Amaral Mozelli

This paper presents a new methodology for the design of PID controllers based on Linear Matrix Inequalities (LMIs). The proposed method can be applied to arbitrary-order SISO time-delay systems. The core of the approach relies on an innovative state-space control architecture that enables the integration of arbitrary-order systems with PI/PID controllers combined with different measurement noise filters, or alternatively, with a pure PI controller. The methodology allows the consideration of multiple design objectives, including disturbance rejection, reference tracking, measurement noise attenuation, the imposition of an exponential decay rate, the guarantee of positive PID controller gains, and robustness margins in the frequency domain, by imposing upper bounds on the H∞ norm of the main sensitivity functions. Moreover, the approach explicitly accounts for parametric uncertainties. For validation, numerical examples were carried out ranging from low-order to higher-order models, combining different controller structures and multiple objectives simultaneously. The results were compared with recent and relevant methods from the literature, demonstrating the effectiveness and superiority of the proposed methodology.

The synthesis of adaptive gain-scheduling controller is discussed for continuous-time linear models characterized by polytopic uncertainties. The proposed approach computes the control law assuming the parameters as uncertain and adaptively provides an estimate for the gain-scheduling implementation. Conservativeness is reduced using our recent results on describing uncertainty: i) a structural relaxation that casts the parameters as outer terms and introduces slack variables; and ii) a precise topological representation that describes the mismatch between the uncertainty and its estimate. Numerical examples illustrate a high degree of relaxation in comparison with the state-of-the-art.

This note proposes a new type of Parallel Distributed Controller (PDC) for Takagi-Sugeno (TS) fuzzy models. Our idea consists of using two control terms based on state feedback, one composed of a convex combination of linear gains weighted by the normalized membership grade, as in traditional PDC, and the other composed of linear gains weighted by the time-derivatives of the membership functions. We present the design conditions as Linear Matrix Inequalities, solvable through numerical optimization tools. Numerical examples are given to illustrate the advantages of the proposed approach, which contains the the traditional PDC as a special case.

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.

In this paper, we study the application of DRL algorithms in the context of local navigation problems, in which a robot moves towards a goal location in unknown and cluttered workspaces equipped only with limited-range exteroceptive sensors, such as LiDAR. Collision avoidance policies based on DRL present some advantages, but they are quite susceptible to local minima, once their capacity to learn suitable actions is limited to the sensor range. Since most robots perform tasks in unstructured environments, it is of great interest to seek generalized local navigation policies capable of avoiding local minima, especially in untrained scenarios. To do so, we propose a novel reward function that incorporates map information gained in the training stage, increasing the agent's capacity to deliberate about the best course of action. Also, we use the SAC algorithm for training our ANN, which shows to be more effective than others in the state-of-the-art literature. A set of sim-to-sim and sim-to-real experiments illustrate that our proposed reward combined with the SAC outperforms the compared methods in terms of local minima and collision avoidance.

This paper addresses the application of Deep Reinforcement Learning (DRL) methods in the context of local navigation, i.e., a robot moves towards a goal location in unknown and cluttered workspaces equipped only with limited-range exteroceptive sensors. Collision avoidance policies based on DRL present advantages, but they are quite susceptible to local minima, once their capacity to learn suitable actions is limited to the sensor range. We address this issue by means of reward shaping in actorcritic networks. A dense reward function, that incorporates map information gained in the training stage, is proposed to increase the agent's capacity to decide about the best action. Also, we offer a comparison between the Twin Delayed Deep-Deterministic Policy Gradient (TD3) andSoft Actor-Critic (SAC) algorithms for training our policy. A set of sim-to-sim and sim-to-real trials illustrate that our proposed reward shaping outperforms the compared methods in terms of generalization, by arriving at the target at higher rates in maps that are prone to local minima and collisions.

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.