125 research outputs found
Direct data-driven control of constrained linear parameter-varying systems: A hierarchical approach
In many nonlinear control problems, the plant can be accurately described by
a linear model whose operating point depends on some measurable variables,
called scheduling signals. When such a linear parameter-varying (LPV) model of
the open-loop plant needs to be derived from a set of data, several issues
arise in terms of parameterization, estimation, and validation of the model
before designing the controller. Moreover, the way modeling errors affect the
closed-loop performance is still largely unknown in the LPV context. In this
paper, a direct data-driven control method is proposed to design LPV
controllers directly from data without deriving a model of the plant. The main
idea of the approach is to use a hierarchical control architecture, where the
inner controller is designed to match a simple and a-priori specified
closed-loop behavior. Then, an outer model predictive controller is synthesized
to handle input/output constraints and to enhance the performance of the inner
loop. The effectiveness of the approach is illustrated by means of a simulation
and an experimental example. Practical implementation issues are also
discussed.Comment: Preliminary version of the paper "Direct data-driven control of
constrained systems" published in the IEEE Transactions on Control Systems
Technolog
Least costly energy management for series hybrid electric vehicles
Energy management of plug-in Hybrid Electric Vehicles (HEVs) has different
challenges from non-plug-in HEVs, due to bigger batteries and grid recharging.
Instead of tackling it to pursue energetic efficiency, an approach minimizing
the driving cost incurred by the user - the combined costs of fuel, grid energy
and battery degradation - is here proposed. A real-time approximation of the
resulting optimal policy is then provided, as well as some analytic insight
into its dependence on the system parameters. The advantages of the proposed
formulation and the effectiveness of the real-time strategy are shown by means
of a thorough simulation campaign
Performance-oriented model learning for data-driven MPC design
Model Predictive Control (MPC) is an enabling technology in applications
requiring controlling physical processes in an optimized way under constraints
on inputs and outputs. However, in MPC closed-loop performance is pushed to the
limits only if the plant under control is accurately modeled; otherwise, robust
architectures need to be employed, at the price of reduced performance due to
worst-case conservative assumptions. In this paper, instead of adapting the
controller to handle uncertainty, we adapt the learning procedure so that the
prediction model is selected to provide the best closed-loop performance. More
specifically, we apply for the first time the above "identification for
control" rationale to hierarchical MPC using data-driven methods and Bayesian
optimization.Comment: Accepted for publication in the IEEE Control Systems Letters (L-CSS
Model predictive control with dynamic move blocking
Model Predictive Control (MPC) has proven to be a powerful tool for the
control of systems with constraints. Nonetheless, in many applications, a major
challenge arises, that is finding the optimal solution within a single sampling
instant to apply a receding-horizon policy. In such cases, many suboptimal
solutions have been proposed, among which the possibility of "blocking" some
moves a-priori. In this paper, we propose a dynamic approach to move blocking,
to exploit the solution already available at the previous iteration, and we
show not only that such an approach preserves asymptotic stability, but also
that the decrease of performance with respect to the ideal solution can be
theoretically bounded.Comment: 7 page
Joint vehicle state and parameters estimation via Twin-in-the-Loop observers
Vehicular control systems are required to be both extremely reliable and
robust to different environmental conditions, e.g. load or tire-road friction.
In this paper, we extend a new paradigm for state estimation, called
Twin-in-the-Loop filtering (TiL-F), to the estimation of the unknown parameters
describing the vehicle operating conditions. In such an approach, a
digital-twin of the vehicle (usually already available to the car manufacturer)
is employed on-board as a plant replica within a closed-loop scheme, and the
observer gains are tuned purely from experimental data. The proposed approach
is validated against experimental data, showing to significantly outperform the
state-of-the-art solutions.Comment: Preprint under review at Vehicle Systems Dynamic
The Twin-in-the-Loop approach for vehicle dynamics control
In vehicle dynamics control, engineering a suitable regulator is a long and
costly process. The starting point is usually the design of a nominal
controller based on a simple control-oriented model and its testing on a
full-fledged simulator. Then, many driving hours are required during the
End-of-Line (EoL) tuning phase to calibrate the controller for the physical
vehicle. Given the recent technological advances, in this paper we consider the
pioneering perspective where the simulator can be run on-board in the
electronic control unit, so as to calculate the nominal control action in
real-time. In this way, it can be shown that, in the EoL phase, we only need to
tune a simple compensator of the mismatch between the expected and the measured
outputs. The resulting approach not only exploits the already available
simulator and nominal controller and significantly simplifies the design
process, but also outperforms the state-of-the-art in terms of tracking
accuracy and robustness within a challenging active braking control case study
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