Direct data-driven filter design for automotive controlled suspensions

This paper investigates the filter design problem for automotive controlled suspensions when no mathematical model of the system is available, but a set of initial experiments can be performed, where also the variable to be estimated is measured. The problem of designing suitable linear time-invariant filters is here investigated, focusing the attention on the estimation of the relative vertical speed between chassis and wheel, using the data provided by two accelerometers measuring the chassis and wheel accelerations. Disturbances and noises are supposed to be norm-bounded and optimality refers to the minimization of the induced norm from disturbances to the estimation error. A Set Membership formulation is followed and, for classes of filters with exponentially decaying impulse response, an approximating set is determined guaranteed to contain all the solutions to the optimal filtering problem. A method is proposed for designing almost-optimal filters with finite impulse response, whose worst-case estimation error is at most twice the lowest achievable one. Numerical simulations using standard "benchmark" road profiles illustrate the effectiveness of the proposed solutions

Nonlinear Model Predictive Control: an Optimal Search Domain Reduction

Nonlinear Model Predictive Control (NMPC) is a powerful control method, used in many industrial contexts. NMPC is based on the online solution of a suitable Optimal Control Problem (OCP) but this operation may require high computational costs, which may compromise its implementation in “fast” real-time applications. In this paper, we propose a novel NMPC approach, aiming to improve the numerical efficiency of the underlying optimization process. In particular, a Set Membership approximation method is applied to derive from data tight bounds on the optimal NMPC control law. These bounds are used to restrict the search domain of the OCP, allowing a significant reduction of the computation time. The effectiveness of the proposed NMPC strategy is demonstrated in simulation, considering an overtaking maneuver in a realistic autonomous vehicle scenario

Direct data-driven filter design for automotive controlled suspensions

This paper investigates the filter design problem for automotive controlled suspensions when no mathematical model of the system is available, but a set of initial experiments can be performed, where also the variable to be estimated is measured. The problem of designing suitable linear time-invariant filters is here investigated, focusing the attention on the estimation of the relative vertical speed between chassis and wheel, using the data provided by two accelerometers measuring the chassis and wheel accelerations. Disturbances and noises are supposed to be norm-bounded and optimality refers to the minimization of the induced norm from disturbances to the estimation error. A Set Membership formulation is followed and, for classes of filters with exponentially decaying impulse response, an approximating set is determined guaranteed to contain all the solutions to the optimal filtering problem. A method is proposed for designing almost-optimal filters with finite impulse response, whose worst-case estimation error is at most twice the lowest achievable one. Numerical simulations using standard "benchmark" road profiles illustrate the effectiveness of the proposed solutions

Adaptive longitudinal control of an autonomous vehicle with an approximate knowledge of its parameters

This paper explores the longitudinal control problem of an autonomous car in legal speed range. The goal is to develop a longitudinal controller that does not rely on vehicle identification parameters, while being capable of tracking the speed profile with comfort acceleration. A modification of a Model Reference Adaptive Control (MRAC) technique found in literature has been deeply studied and implemented, by setting the proper initial conditions for the target application. The proposed architecture is capable of controlling a vehicle whose parameters are known approximately. A CarSim-Simulink joint simulation verifies the feasibility of the proposed strategy and evaluates performances of vehicles at low and high dynamics conditions

Optimality, approximation, and complexity in set membership H∞ identification

Investigates the set membership identification of time-invariant, discrete-time, exponentially stable, possibly infinite-dimensional, linear systems from time or frequency-domain data, corrupted by deterministic noise. The aim is to deliver not a single model, but a set of models whose size in H∞ norm measures the uncertainty in the identification. The main focus of the note is on the optimality properties for finite data and on the tradeoff between optimality and complexity of approximated low order model sets. A method is given for evaluating convergent and computationally efficient inner and outer approximations of the value set for a given frequency. Such approximations allow one to compute, within any desired accuracy, the identification error of any identified model, and to evaluate an optimal model at any given number of frequencies. By suitably approximating these values, model sets with nominal models in RH∞ are then derived, whose order is selected by trading off between model set complexity and identification accuracy degradation. This degradation is evaluated by computing the optimality level, defined as the ratio between the reduced model identification error and the optimal one. A numerical example demonstrates the effectiveness of the presented results

H-infinity Set Membership Identification: a survey

Robustness had become in past years a central issue in system and control theory, focusing the attention of researchers from the study of a single model to the investigation of a set of models, described by a set of perturbations of a "nominal" model. Such a set, often indicated as an uncertainty model set or model set for short, has to be suitably constructed to describe the inherent uncertainty about the system under consideration and to be used for analysis and design purposes. H-infinity identification methods deliver uncertainty model sets in a suitable form to be used by well-established robust design techniques, based on H-infinity or "mu" optimization methods. The literature on H-infinity identification is now very extensive. In this paper, some of the most relevant contributions related to assumption validation, evaluation of bounds on unmodeled dynamics, convergence analysis and optimality properties of linear, two-stage and interpolatory algorithms are surveyed from a deterministic point of view