In this work we propose a new practical synchronization protocol for multiple Euler Lagrange (EL) systems without structural linear-in-the-parameters (LIP) knowledge of the uncertainty and where the agents can be interconnected before control design by unknown state-dependent interconnection terms. This setting is meant to overcome two standard a priori assumptions in the literature concerning uncertainty with LIP structure and absence of interaction among agents before designing the synchronization protocol. To overcome these assumptions, we propose an adaptive distributed control mechanism having the purpose of estimating the coefficients of the resulting state-dependent uncertainty structure.

This article proposes a framework for adaptive synchronization of uncertain underactuated Euler-Lagrange (EL) agents. The designed distributed controller can handle both state-dependent uncertain system dynamics terms and state-dependent uncertain interconnection terms among neighboring agents. No structural knowledge of such terms is required other than the standard properties of EL systems (positive definite mass matrix, bounded gravity, velocity-dependent friction bound, etc.). The study of stability relies on a suitable analysis of the nonactuated and the actuated synchronization errors, resulting in stable error dynamics perturbed by parametrized state-dependent uncertainty. This uncertainty is tackled via appropriate adaptation laws, giving stability in the uniform ultimate boundedness sense, in line with available literature on state-dependent uncertain system dynamics and/or state-dependent uncertain interconnections. An example with a network of boom cranes is used to validate the proposed approach.

The high maneuverability of fixed-wing unmanned aerial vehicles (UAVs) exposes these systems to several dynamical and parametric uncertainties, severely affecting the fidelity of modeling and causing limited guidance autonomy. This article shows enhanced autonomy via adaptation mechanisms embedded in the guidance law: a vector-field method is proposed that does not require a priori knowledge of the UAV course time constant, coupling effects, and wind amplitude/direction. Stability and performance are assessed using the Lyapunov theory. The method is tested on software-in-the loop and hardware-in-the-loop UAV platforms, showing that the proposed guidance law outperforms state-of-the-art guidance controllers and standard vector-field approaches in the presence of significant uncertainty.

Effective design of autopilots for fixed-wing unmanned aerial vehicles (UAVs) is still a great challenge, due to unmodeled effects and uncertainties that these vehicles exhibit during flight. Unmodeled effects and uncertainties comprise longitudinal/lateral cross-couplings, as well as poor knowledge of equilibrium points (trimming points) of the UAV dynamics. The main contribution of this article is a new adaptive autopilot design, based on uncertain Euler-Lagrange dynamics of the UAV and where the control can explicitly take into account under-actuation in the dynamics, reduced structural knowledge of cross-couplings and trimming points. This system uncertainty is handled via appropriately designed adaptive laws: stability of the controlled UAV is analyzed. Hardware-in-the-loop tests, comparisons with an Ardupilot autopilot and with a robustified autopilot validate the effectiveness of the control design, even in the presence of strong saturation of the UAV actuators.

By increasingly relying on network-based operation for control, monitoring, and protection functionalities, modern wide-area power systems have also become vulnerable to cyber-attacks aiming to damage system performance and/or stability. Resilience in state-of-the-art methods mostly relies on known characteristics of the attacks and static control loops (i.e., with fixed input/output channels). This work proposes a 'dynamic loop' wide-area damping strategy, where input/output channel pairs are changed dynamically. We study 'reactive' dynamic switching in case of detectable attack and 'pro-active' dynamical switching, in case of undetectable (stealth) attacks. Stability of the dynamic loop is presented via Lyapunov theory, under parametric perturbations, average dwell time switching and external perturbations. Using two-and five-area IEEE benchmarks, it is shown that the proposed strategy provides effective damping and robustness under various detectable (e.g., false data injection, denial-of-service) and stealth (replay, bias injection) attacks.

Artificial-delay control is a method in which state and input measurements collected at an immediate past time instant (i.e. artificially delayed) are used to compensate the uncertain dynamics affecting the system at the current time. This work formulates an artificial-delay control method with adaptive gains in the presence of nonlinear (Euler-Lagrange) under-actuation. The appeal of studying Euler-Lagrange dynamics is to capture many robotics applications of practical interest, as demonstrated via stability and robustness analysis and via robotic ship and robotic aerial vehicle test cases.

Construction crane vessels make use of dynamic positioning (DP) systems during the installation and removal of offshore structures to maintain the vessel's position. Studies have reported cases of instability of DP systems during offshore operation caused by uncertainties, such as mooring forces. DP “robustification” for heavy lift operations, i.e., handling such uncertainties systematically and with stability guarantees, is a long-standing challenge in DP design. A new DP method, composed by an observer and a controller, is proposed to address this challenge, with stability guarantees in the presence of uncertainties. We test the proposed method on an integrated cranevessel simulation environment, where the integration of several subsystems (winch dynamics, crane forces, thruster dynamics, fuel injection system etc.) allow a realistic validation under a wide set of uncertainties.

Reliable guidance of fixed-wing Unmanned Aerial Vehicles (UAVs) is challenging, as their high maneuverability exposes them to several dynamical changes and parametric uncertainties. Reliability of state-of-the-art guidance methods is often at stake, as these methods heavily rely on precise UAV course dynamics, assumed in a decoupled first-order form with known time constant. To improve reliability of guidance for fixed-wing UAVs, this work proposes a novel vector field law that can handle uncertain course time constant and state-dependent uncertainty in the course dynamics arising from coupling. Stability is studied in the Lyapunov framework, while reliability of the proposed method is tested on a software-in-the loop UAV simulator. The simulations show that, in the presence of such uncertainty, the proposed method outperforms the standard vector field approaches.