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This brief addresses the robust lateral control problem for self-driving racecars. It proposes a discrete-time estimation and control solution consisting of a delayed unknown input-state observer (UIO) and a robust tracking controller. Based on a nominal vehicle model, describing its motion with respect to a generic desired trajectory and requiring no information about the surrounding environment, the observer reconstructs the total force disturbance signal, resulting from imperfect knowledge of the time-varying tire-road interface characteristics, presence of other vehicles nearby, wind gusts, and other model uncertainty. Then, the controller actively compensates the estimated force and asymptotically steers the tracking error to zero. The brief also presents a closed-loop stability proof of the method, ensuring perfect asymptotic estimation and tracking by the controlled vehicle. The proposed solution advantageously needs no a-priori information about the total disturbance boundedness, additional variables to model uncertainty, or observer parameters to be tuned. Its effectiveness and superiority to existing methods are studied in theory and shown in simulations where a full racecar model, based on the vehicle dynamics blockset, is required to track aggressive maneuvers. Through a faster and more accurate disturbance estimation, the solution robustly ensures better dynamic responses even with measurement noise.

The control of articulated soft robots, i.e. robots with flexible joints and rigid links, presents a challenge due to their in- trinsic elastic elements and nonlinear force-deflection dependency. This letter first proposes a discrete-time delayed unknown input- state observer based on a nominal robot model that reconstructs the total torque disturbance vector, resulting from the imperfect knowledge of the elastic torque characteristic, external torques, and other model uncertainties. Then, it introduces a robust controller, that actively compensates for the estimated uncertainty and allows bounded stability for the tracking of independent link position and joint stiffness reference signals. The convergence of the disturbance estimator and the overall system’s stability in closed loop is proven analytically, while the effectiveness of the proposed control design is first evaluated in simulations with respect to large uncertainty conditions, and then demonstrated through experiments on a real multi-degree-of-freedom articulated soft robot.

This paper focuses on racecar longitudinal control with highly-varying driving conditions. The main factors affecting the dynamic behavior of a vehicle, including aerodynamic forces, wheel rolling resistance, traction force resulting from changing tire-road interaction as well as the occurrence of sudden wind gusts or the presence of persistent winds, are considered and assumed to have unknown models. By exploiting the theory on delayed input-state observers and using measurement data about the vehicle and wheel speeds, a dynamic filter that allows the online reconstruction of the above-mentioned unknown time-varying quantities is derived. Moreover, by exploiting the notion of effective tire radius, a reduced-degree-of-freedom model for the longitudinal vehicle dynamics is obtained, which is independent of the traction force and that enables, when used with the observer filter described above, an accurate speed control compensating for the resistance forces. One appealing feature of the proposed estimation and control method is that it requires no model information about such forces, for which, at the state-of-the-art, only heuristic approximations to be a-priori identified are available. Its effectiveness is shown via the simulation of scenarios where the car is required to execute aggressive maneuvers and the asphalt road surface abruptly changes from dry to wet, snowy, and icy. The evaluation also reveals that the proposed estimation technique outperforms standard solutions even in the presence of measurement noise.

This paper focuses on the control of longitudinal self-driving racecar models with model uncertainty and pro- poses a robust solution that comprises an online disturbance estimator and a nonlinear compensation control feedback law. By modeling all uncertainty with respect to a nominal model as suitably disturbance signals and afterward exploiting unknown- input state observer theory, a lean and fast estimator is derived for the racecar model. The estimator does not require a priori knowledge of the uncertainty. Closed-loop stability of the proposed controller ensuring the asymptotic reconstruction of the system state and disturbance inputs as well as asymptotic tracking of desired longitudinal speed is proved. Simulations are presented to exemplify the functioning of the proposed solution and show its validity.

International audience