Andrea Lecchini

Brief Virtual reference feedback tuning: a direct method for the design of feedback controllers

This paper considers the problem of designing a controller for an unknown plant based on input/output measurements. The new design method we propose is direct (no model identification of the plant is needed) and can be applied using a single set of data generated by the plant, with no need for specific experiments nor iterations. It is shown that the method searches for the global optimum of the design criterion and that, in the case of restricted complexity controller design, the achieved controller is a good approximation of the restricted complexity global optimal controller. A simulation example shows the effectiveness of the method.

An application of the virtual reference feedback tuning method to a benchmark problem

Virtual Reference Feedback Tuning (VRFT) is a general methodology for the design of a controller when the plant transfer function is unknown, proposed by the same authors in previous contributions. It is a direct method that aims at minimizing a control cost of the 2-norm type by using a batch of data collected from the plant. The minimisation is conducted in one-shot (the method is not iterative) and this makes VRFT particularly handy in many practical applications. This paper presents an application of VRFT to a benchmark active suspension system. As a by-product, this paper also delivers a new extension of VRFT that permits to cope with constraints on the input-sensitivity.

Asymptotic accuracy of iterative feedback tuning

Iterative feedback tuning (IFT) is a widely used procedure for controller tuning. It is a sequence of iteratively performed special experiments on the plant interlaced with periods of data collection under normal operating conditions. In this note, we derive the asymptotic convergence rate of IFT for disturbance rejection, which is one of the main fields of application.

A model reference approach to safe controller changes in iterative identification and control

A controller change from a current controller which stabilises the plant to a new controller, designed on the basis of an approximate model of the plant and with guaranteed bounds on the stability properties of the true closed loop, is called a safe controller change. In this paper, we present a model reference approach to the determination of safe controller changes on the basis of approximate closed loop models of the plant and robust stability results in the @n-gap.

Prefiltering in iterative feedback tuning: optimization of the prefilter for accuracy

Iterative feedback tuning (IFT) is a data-based method for the tuning of restricted complexity controllers. At each iteration, an update for the controller parameters is estimated from data obtained partly from the normal operation of the closed loop system and partly from a special experiment, in which the output signal obtained under normal operation is fed back at the reference input. The choice of a prefilter for the input data to the special experiment is a degree of freedom of the method. In this note, the prefilter is designed in order to enhance the accuracy of the IFT update. The optimal prefilter produces a covariance of the new controller parameter vector that is strictly smaller than the covariance obtained with the standard constant prefilter.

On iterative feedback tuning for non-minimum phase plants

The iterative feedback tuning (IFT) is a data-based method for the tuning of restricted-complexity controllers. In the classical formulation, the IFT aims at minimizing a certain model-reference criterion in which the reference-model is chosen by the user. This minimization is based on signal information only. In this paper we formulate a new criterion for the IFT method. In the new criterion, some freedom is given to the reference-model in order to let it reproduce the features of the unknown plant (i.e. the delay and non-minimum phase zeros) which the controller should not attempt to change. It is shown that using the new criterion corresponds to giving more emphasis to the placement of the closed loop poles.

Sensitivity shaping via virtual reference feedback tuning

The virtual reference feedback tuning (VRFT) is a data based method for the design of feedback controllers. In previous papers, the VRFT has been presented for the solution of the one degree of freedom model-reference control problem in which the objective is to shape the I/O transfer function of the control system. In this paper, the VRFT approach is extended so that it can be used for the shaping of the sensitivity function.

Explicit expression of the parameter bias in identification of Laguerre models from step responses

This paper delivers an analysis of the least-square estimation of the Laguerre coefficients of a linear discrete-time system from step response data. The original contribution consists in an explicit formula for the bias error on the estimated coefficients due to the under-modelling of the system. The formula, jointly with some a-priori information on the neglected dynamics, can be used to construct bounds on this error. The results presented in this paper are illustrated with a simulation example

Safe adaptive controller changes based on reference model adjustments

A controller change from a current controller which stabilizes the plant to a new controller, designed on the basis of an approximate model of the plant and with guaranteed bounds on the stability properties of the true closed loop, is called a safe controller change. In this paper, we present a model reference approach to the determination of safe controller changes on the basis of approximate closed loop models of the plant and robust stability results in the /spl or/-gap.

Air-traffic control in approach sectors: simulation examples and optimisation

In this contribution we consider the approach to the runway as a case study of our research on conflict resolution for Air-Traffic Control with stochastic models. We simulate the approach for landing and optimise the maneuver through a simulation based optimisation strategy.