We overview recent progress in the field of robust adaptive control with special emphasis on methodologies that use multiple-model architectures. We argue that the selection of the number of models, estimators and compensators in such architectures must be based on a precise definition of the robust performance requirements. We illustrate some of the concepts and outstanding issues by presenting a new methodology that blends robust non-adaptive mixed m-synthesis designs and stochastic hypothesis-testing concepts leading to the so-called robust multiple model adaptive control (RMMAC) architecture. A numerical example is used to illustrate the RMMAC design methodology, as well as its strengths and potential shortcomings. The later motivated us to develop a variant architecture, denoted as RMMAC/XI, that can be effectively used in highly uncertain exogenous plant disturbance environments.

This paper addresses the problem of dynamic positioning and way-point tracking of underactuated autonomous underwater vehicles (AUVs) in the presence of constant unknown ocean currents and parametric modelling uncertainty. A non-linear adaptive controller is proposed that steers an AUV along a sequence of way-points consisting of desired positions (x, y) in a inertial reference frame, followed by vehicle positioning at the final target point. The controller is first derived at the kinematic level assuming that the ocean current disturbance is known. An exponential observer for the current is then designed and convergence of the resulting closed-loop system trajectories is analysed. Finally, integrator backstepping and Lyapunov based techniques are used to extend the kinematic controller to the dynamic case and to deal with model parameter uncertainty. Simulation results with a dynamic model of an underactuated autonomous underwater shuttle for the transport of benthic labs are presented and discussed.

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This paper addresses the problem of steering a group of vehicles along given paths while holding a desired formation pattern. The solution to this problem, henceforth referred to as the Coordinated Path-Following problem, unfolds in two basic steps. First, a path-following control law is used that drives each vehicle to its assigned path regardless of the temporal speed profile adopted. This is done by making each vehicle approach a conveniently defined virtual target that moves along the path. In the second step, the speeds of the vehicles are adjusted so as to synchronize the positions of the corresponding virtual targets (also called coordination states) thus achieving coordination along the paths. In the problem formulation, it is explicitly considered that each vehicle transmits its coordination state to only a subset of the other vehicles, as determined by the communications topology adopted. It is shown that the system that is obtained by putting together the path following and coordination strategies can be naturally viewed as a feedback interconnected system. Using this result and recent results from nonlinear system and graph theory, conditions are derived under which the path following and the coordination errors are driven to a neighborhood of zero in the presence of communication failures and time delays. Two different situations are considered. The first captures the case where the communication graph is alternately connected and disconnected (brief connectivity losses). The second reflects an operating scenario where the union of the communication graphs over uniform intervals of time remains connected (uniformly connected in mean). To better ground the paper on a non-trivial design example, a coordinated path-following algorithm is derived for multiple underactuated Autonomous Underwater Vehicles (AUVs). Simulation results are presented and discussed.

This paper addresses the problem of steering a fleet of unmanned aerial vehicles along desired three-dimensional paths while meeting stringent spatial and temporal constraints. A representative example is the challenging mission scenario where the unmanned aerial vehicles are tasked to cooperatively execute collision-free maneuvers and arrive at their final destinations at the same time. In the proposed framework, the unmanned aerial vehicles are assigned nominal spatial paths and speed profiles along those, and then the vehicles are requested to execute cooperative path following, rather than open loop trajectory tracking maneuvers. This strategy yields robust behavior against external disturbances by allowing the unmanned aerial vehicles to negotiate their speeds along the paths in response to information exchanged over the supporting communications network. The paper considers the case where the graph that captures the underlying time-varying communications topology is disconnected during some interval of time or even fails to be connected at all times. Conditions are given under which the cooperative path-following closed-loop system is stable. Flight test results of a coordinated road-search mission demonstrate the efficacy of the multi-vehicle cooperative control framework developed in the paper.

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SUMMARYA new type of control law is derived to steer the dynamic model of a wheeled robot of unicycle type along a desired path. The methodology adopted for path following control deals explicitly with vehicle dynamics and plant parameter uncertainty. Furthermore, it overcomes stringent initial condition constraints that are present in a number of path following control strategies described in the literature. This is done by controlling explicitly the rate of progression of a 'virtual target' to be tracked along the path, thus bypassing the problems that arise when the position of the virtual target is simply defined by the projection of the actual vehicle on that path. In the paper, a nonlinear adaptive control law is derived that yields convergence of the (closed-loop system) path following error trajectories to zero. Controller design relies on Lyapunov theory and backstepping techniques. Simulation results illustrate the performance of the control system proposed.

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