arXiv subjects

Paolo Stegagno

Publications and source records attributed to Paolo Stegagno.

4 recordsLinked to original sources

arXiv2024

Adaptive Formation Learning Control for Cooperative AUVs under Complete Uncertainty

This paper presents a two-layer control framework for Autonomous Underwater Vehicles (AUVs) designed to handle uncertain nonlinear dynamics, including the mass matrix, previously assumed known. Unlike prior studies, this approach makes the controller independent of the robot's configuration and varying environmental conditions. The proposed framework applies across different environmental conditions affecting AUVs. It features a first-layer cooperative estimator and a second-layer decentralized deterministic learning controller. This architecture supports robust operation under diverse underwater scenarios, managing environmental effects like changes in water viscosity and flow, which impact the AUV's effective mass and damping dynamics. The first-layer estimator enables seamless inter-agent communication by sharing crucial system estimates without relying on global information. The second-layer controller uses local feedback to adjust each AUV's trajectory, ensuring accurate formation control and dynamic adaptability. Radial basis function neural networks enable local learning and knowledge storage, allowing AUVs to efficiently reapply learned dynamics after system restarts. Simulations validate the effectiveness of this framework, marking it as a significant advancement in distributed adaptive control systems for AUVs, enhancing operational flexibility and resilience in unpredictable marine environments.

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arXiv2024

Composite Distributed Learning and Synchronization of Nonlinear Multi-Agent Systems with Complete Uncertain Dynamics

This paper addresses the problem of composite synchronization and learning control in a network of multi-agent robotic manipulator systems with heterogeneous nonlinear uncertainties under a leader-follower framework. A novel two-layer distributed adaptive learning control strategy is introduced, comprising a first-layer distributed cooperative estimator and a second-layer decentralized deterministic learning controller. The first layer is to facilitate each robotic agent's estimation of the leader's information. The second layer is responsible for both controlling individual robot agents to track desired reference trajectories and accurately identifying/learning their nonlinear uncertain dynamics. The proposed distributed learning control scheme represents an advancement in the existing literature due to its ability to manage robotic agents with completely uncertain dynamics including uncertain mass matrices. This allows the robotic control to be environment-independent which can be used in various settings, from underwater to space where identifying system dynamics parameters is challenging. The stability and parameter convergence of the closed-loop system are rigorously analyzed using the Lyapunov method. Numerical simulations validate the effectiveness of the proposed scheme.

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arXiv2020

Cooperative Adaptive Learning Control for A Group of Nonholonomic UGVs by Output Feedback

A high-gain observer-based cooperative deterministic learning (CDL) control algorithm is proposed in this chapter for a group of identical unicycle-type unmanned ground vehicles (UGVs) to track over desired reference trajectories. For the vehicle states, the positions of the vehicles can be measured, while the velocities are estimated using the high-gain observer. For the trajectory tracking controller, the radial basis function (RBF) neural network (NN) is used to online estimate the unknown dynamics of the vehicle, and the NN weight convergence and estimation accuracy is guaranteed by CDL. The major challenge and novelty of this chapter is to track the reference trajectory using this observer-based CDL algorithm without the full knowledge of the vehicle state and vehicle model. In addition, any vehicle in the system is able to learn the knowledge of unmodeled dynamics along the union of trajectories experienced by all vehicle agents, such that the learned knowledge can be re-used to follow any reference trajectory defined in the learning phase. The learning-based tracking convergence and consensus learning results, as well as using learned knowledge for tracking experienced trajectories, are shown using the Lyapunov method. Simulation is given to show the effectiveness of this algorithm.

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arXiv2015

Decentralized Multi-Robot Encirclement of a 3D Target with Guaranteed Collision Avoidance

We present a control framework for achieving encirclement of a target moving in 3D using a multi-robot system. Three variations of a basic control strategy are proposed for different versions of the encirclement problem, and their effectiveness is formally established. An extension ensuring maintenance of a safe inter-robot distance is also discussed. The proposed framework is fully decentralized and only requires local communication among robots; in particular, each robot locally estimates all the relevant global quantities. We validate the proposed strategy through simulations on kinematic point robots and quadrotor UAVs, as well as experiments on differential-drive wheeled mobile robots.

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