This brief proposes a new adaptive-robust control (ARC) architecture for a class of uncertain Euler-Lagrange (EL) systems where the upper bound of the uncertainty satisfies linear in the parameters structure. Conventional ARC strategies either require structural knowledge of the system or presume that the overall uncertainties or its time derivative are norm bounded by a constant. Due to the unmodeled dynamics and modeling imperfection, true structural knowledge of the system is not always available. Furthermore, for the class of systems under consideration, prior assumption, regarding the uncertainties (or its time derivative) being upper bounded by a constant, puts a restriction on the states beforehand. Conventional ARC laws invite overestimation-underestimation problem of switching gain. Toward this front, adaptive switching-gain-based robust control (ASRC) is proposed, which alleviates the overestimation-underestimation problem of switching gain. Moreover, ASRC avoids any presumption of constant upper bound on the overall uncertainties and can negotiate uncertainties regardless of being linear or nonlinear in parameters. Experimental results of ASRC using a wheeled mobile robot note improved control performance in comparison with the adaptive sliding mode control.

This paper proposes a new adaptive-robust control (ARC) strategy for a tracking control problem of a class of uncertain Euler-Lagrange systems. The proposed adaptive-robust time-delay control (ARTDC) amalgamates the ARC strategy with the time-delay control (TDC). It comprises three parts: a time-delay estimation part, a desired dynamics injection part, and an adaptive-robust part. The main feature of the proposed ARTDC is that it does not involve any threshold value in its adaptive law; thus, it allows the switching gain to increase or decrease whenever the error trajectories move away or close to the switching surface, respectively. Thus, compared with the existing ARC schemes, ARTDC is able to alleviate the over- and underestimation problems of the switching gain. Moreover, the stability analysis of ARTDC provides an upper bound for the selection of sampling interval and its relation with controller gains. The proposed ARTDC shows improved tracking performance compared with the TDC and the existing adaptive sliding-mode control in simulations as well as in experiments with a multiple-degree-of-freedom system.

The actual performance of model-based path-following methods for Unmanned Aerial Vehicles (UAVs) show considerable dependence on the wind knowledge and on the fidelity of the dynamic model used for design. This work 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 unmodelled 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 unmodelled course angle dynamics. On the other hand, the estimation mechanism of the proposed adaptive VF effectively compensates for wind uncertainty and unmodelled dynamics, sensibly reducing the path-following error as compared to the standard VF.

This brief proposes a new adaptive-robust formulation for time-delay control (TDC) under a less-restrictive stability condition. TDC relies on estimating the unknown system dynamics via the artificial introduction of a time delay, often referred to as time-delay estimation (TDE). In conventional TDC, the estimation error, called TDE error, is taken to be upper bounded by a constant under the assumption of small time delay and, most importantly, of a priori bounded states. We highlight the issues of such a conventional methodology via an unstable counterexample. Consequently, a new less-restrictive structure for the upper bound of the TDE error is formulated, which has an explicit dependence on system states and is valid for any chosen time delay. This insight leads to a new TDC design, namely, time-delayed adaptive-robust control (TDARC). The effectiveness of TDARC is substantiated via a multiple-degrees-of-freedom robot.