Abstract

With the advent of intelligent transport, quadrotors are becoming an attractive aerial transport solution during emergency evacuations, construction works etc. During such operations, dynamic variations in (possibly unknown) payload and unknown external disturbances cause considerable control challenges for path tracking algorithms. In fact, the statedependent nature of the resulting uncertainties makes stateof-the-art adaptive control solutions ineffective against such uncertainties that can be completely unknown and possibly unbounded a priori. This paper, to the best of the knowledge of the authors, proposes the first adaptive control solution for quadrotors, which does not require any a priori knowledge of the parameters of quadrotor dynamics as well as of external disturbances. The stability of the closed-loop system is studied analytically via Lyapunov theory and the effectiveness of the proposed solution is verified on a realistic simulator.

Abstract

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.

Abstract

he presence of switching/evolving topologies of the power system. In today’s smart grids, switching topologies often arise from reconfiguration and resilience against faults or from switching among different control ar

Abstract

In the presence of unmodelled dynamics and uncertainties with no a priori constant bounds, conventional robust adaptation strategies for switched systems cannot allow the control gains of inactive subsystems to remain constant during inactive intervals: vanishing gains are typically required in order to prove bounded stability. As a consequence, these strategies, known in literature as leakage-based adaptive methods, might introduce poor transients at each switching instant. Leakage-based adaptive control becomes even more problematic in the switched nonlinear case, where non-conservative state-dependent upper bounds for uncertainties and unmodelled dynamics are required. This work shows that, for a class of switched Euler–Lagrange systems, such difficulties can be mitigated: a novel leakage-based stable mechanism is introduced which allows the gains of inactive subsystems to remain constant. At the same time, unmodelled dynamics and uncertainties with no a priori bounds can be handled by a quadratic state-dependent upper bound structure that reduces conservativeness as compared to state-of-the-art structures. The proposed design is validated analytically and its performance is studied in simulation with a pick-and-place robotic manipulator example.

Abstract

This work presents a Lyapunov-based approach to adaptive control of uncertain Euler-Lagrange (EL) systems in a slow switching scenario. Fundamental trade-offs arising from considering uncertain dynamics with unknown uncertainty bounds are presented and discussed. Contrary to the nonswitched scenario, the use of acceleration feedback seems to be unavoidable in the switched scenario: this is due to the fact that an acceleration feedback and an appropriate Lyapunov function must be adopted to make the switching law independent from the unknown uncertainty bounds. In the absence of such feedback or using different Lyapunov functions, a stabilizing switching law would exist but could not be determined as it would depend on an unknown uncertainty bound.

Abstract

State-of-the-art adaptive or robust adaptive techniques for several classes of uncertain switched systems demand structural knowledge of the system dynamics in order to appropriately select the regressor terms in the adaptive law. As a result, the number of unknown parameters to be adapted increases with system complexity, which can lead to very complex adaptive laws. In this work we propose, for the relevant class of Euler-Lagrange systems subject to time-dependent slow switching, a switched robust adaptive control framework with reduced complexity: the number of unknown parameter to be adapted is independent on the system complexity, whereas the regressor terms in the adaptive laws do not require any structural knowledge of the system dynamics. Stability analysis is provided to illustrate the benefit of the proposed design, and the performance of the controller is verified using a switched system stemming from the combination of mooring and free-hanging operations in dynamic positioning of offshore ships

Abstract

A consideration of actuator saturation is an important aspect to study the effectiveness of a designed controller in practice. However, the conventional Lyapunov theory-based design is not always suitable to analyze the quantitative behavior of closed-loop system. This article presents a time-scale redesign-based saturated tracking controller for a class of feedback linearizable multi-input-multi-output (MIMO) nonlinear systems. The proposed controller is built upon the frameworks of contraction and partial contraction theories which ensures that bounded tracking performance as well as quantify the steady-state error bounds in terms of the various control design parameters. Notably, in contrast to the existing Lyapunov-method-based designs, the proposed approach allows to tune the controller performance without arbitrary reduction of singular perturbation parameters. Therefore, the vulnerability of the controller toward actuator saturation and noise, due to the ill-effects of high-gains stemming from the conventional high-gain controllers, are reduced. The extensive experimental results using a wheeled mobile robot are provided to demonstrate the effectiveness of the proposed controller.

Abstract

Underestimation and overestimation problems are commonly observed in conventional adaptive sliding mode control (ASMC). These problems refer to the fact that the adaptive controller gain unnecessarily increases when the states are approaching the sliding surface (overestimation) or improperly decreases when the states are getting far from it (underestimation). In this paper, we propose a novel ASMC strategy that overcomes such issues. In contrast to the state of the art, the proposed strategy is effective even when an a priori constant bound on the uncertainty cannot be imposed. Comparative results using a two-link manipulator demonstrate improved performance as compared to the conventional ASMC. Experimental results on a biped robot confirm the effectiveness and robustness of the proposed method under various practical uncertainties.

Abstract

The actual performance of model-based path-following methods for unmanned aerial vehicles (UAVs) shows considerable dependence on the wind knowledge and on the fidelity of the dynamic model used for design. This study analyzes and demonstrates the performance of an adaptive vector field (VF) control law which can compensate for the lack of knowledge of the wind vector and for the presence of unmodeled course angle dynamics. Extensive simulation experiments, calibrated on a commercial fixed-wing UAV and proven to be realistic, show that the new VF method can better cope with uncertainties than its standard version. In fact, while the standard VF approach works perfectly for ideal first-order course angle dynamics (and perfect knowledge of the wind vector), its performance degrades in the presence of unknown wind or unmodeled course angle dynamics. On the other hand, the estimation mechanism of the proposed adaptive VF effectively compensates for wind uncertainty and unmodeled dynamics, sensibly reducing the path-following error as compared to the standard VF.

Abstract

The adaptive sliding mode control technique relaxes the assumption of known bound of the disturbance in discrete-time systems. However, the existing technique of gain adaptation in discrete-time sliding mode control has issues of gain overestimation and underestimation. Therefore, this paper proposes a technique to adapt the switching gain such that the adaptive gain can tackle the uncertainty without any knowledge of the bound of uncertainty while overcoming the over- and under-estimation problems of switching gain.