1,721,069 research outputs found
Learning Time Optimal Control of Smart Actuators with Unknown Friction
No full textActive valves are most effective tools to control gas flow in compressors if fast transitions
between the open mode and closed mode are needed. Unfortunately, an accurate model including several
nonlinear effects and in particular the resistance and gas flow forces is not available, and this prevents
the use of standard model based approaches for time optimal control. However, the repetitive nature
of the operation of valves suggests the use of learning methods to track a reference in spite of the
insufficient information on the control behavior, thus shifting the problem from the search of the time
optimal control to the search of the reference corresponding to its solution. To this end, in this paper, a
previously proposed algorithm for the iterative determination of the fastest feasible trajectory is analyzed
in terms of convergence conditions and applied to the valve mode
Minimum-time control of a class of nonlinear systems with partly unknown dynamics and constrained input
No full textThe minimum-time problem frequently arises in the design of control for actuators, and is usually solved assuming to know the correct model of the system. In industrially important cases, however, important parts of the dynamics, like friction forces or disturbances by exosystems, are hardly known or even unknown. Against this background, this paper presents an iterative approach to achieve the minimum-time control for a nonlinear, single input second-order system with constrained input and partly unknown dynamics, effectively removing the requirement of perfect knowledge of the system and its parameters to achieve the minimum-time solution in application. First it is shown that, under reasonable assumptions about the unknown part of the dynamics, the optimal control exists for the presented class of systems and that it is a bang-bang control, with at most one switch. Then this property is exploited in the proposed algorithm, that finds the single optimal switching time by an iterative method, without involving any kind of identification of the unknown system parts
Noniterative direct data-driven controller tuning for multivariable systems: theory and application
No full textDirect Data Driven Control of Linear Time-Delay Systems
No full textThis paper presents an extension of the Virtual Reference Feedback Tuning (VRFT) methodology dedicated to linear time-delay systems with known delay and unknown dynamics. The standard VRFT is not well suited for systems with dominant time-delay as it yields high order controllers. The proposed direct approach, relying on a Smith Predictor structure, guarantees the same level of performance as the standard VRFT but with lower order controllers. The joint direct data-driven design of the controller and the predictor is facilitated by the introduction of an ad-hoc optimization initialization. Effectiveness and robustness to uncertainty in the time-delay estimation are shown in a vehicle dynamics control problem
New Regressors for the Direct Identification of Tire-Deformation in Road Vehicles via “in-Tire” Accelerometers
No full textThe interaction between the tire and the road is crucial for determining the dynamic behavior of a road vehicle, and the road–tire contact forces are key variables in the design of traction, braking, and stability control systems. Traditionally,
road–tire contact forces are indirectly estimated from vehicle-dynamics measurements (chassis accelerations, yaw-roll rates,
suspension deflections, etc.). The emerging of the “smart-tire”
concept (tire with embedded sensors and digital-computing capability) has made possible, in principle, a more direct estimation of contact forces. In this field—still in its infancy—the main open problems are the choice of the sensor(s) and the choice of the regressor(s) to be used for force estimation. The objective of this work is to present a new sensor–regressor choice, and to provide some preliminary experimental results, which confirm the validity of this choice. The idea is to use a wheel encoder and an accelerometer mounted directly in the tire. The measurement of the in-tire acceleration is transmitted through a wireless channel. The key innovative concept is to use the phase shift between the wheel encoder and the pulse-like signals provided by the accelerometer as the main regressor for force estimation
Adaptive robust stabilization of continuous casting
No full textContinuous casting processes can suffer from a disturbance effect, called ‘‘dynamic bulging’’, that causes
large oscillations of the mold level, significantly reduces the quality of the final product and may cause
instability and damages. In this paper this effect is analyzed and a new integrated control strategy is
presented capable of significantly reduce the dynamic bulging without slowing down the process. The
strategy is based on the superposition of two effects, one derived from the prediction of the periodic
components of the oscillations and the second based on a robust adaptation scheme. Simulation and
experimental results underline the advantages of the proposed metho
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