Robust Discrete-Time Lateral Control of Racecars by Unknown Input Observers
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 as…
Racecar Longitudinal Control in Unknown and Highly-Varying Driving Conditions
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 tir…
Robust and Decoupled Position and Stiffness Control for Electrically-Driven Articulated Soft Robots
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.…
Robust Control of Nonlinear Systems: An Unknown Input Observer Based Approach
Questa tesi propone un controllore robusto per sistemi non lineari tramite l'utilizzo di un osservatore ad ingressi sconosciuti. Attraverso un'opportuna riformulazione dinamica del modello, un generico sistema non lineare viene descritto come la somma di due funzioni, la prima lineare e nota e la seconda altamente non lineare e sconosciuta, derivante dalla conoscenza imperfetta del modello dinamico, dei parametri, o di eventuali disturbi esogeni agenti sul sistema etc., modellabili come perturbazioni (disturbi) della funzione lineare. Questa semplificazione consente una vantaggiosa descrizione dinamica del sistema in forma matriciale. Le informazioni necessarie sono stimate attraverso l'uti…
Robust Longitudinal Control of Self-Driving Racecar Models
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 longitudi…