G. Zappa

Systems with persistent disturbances: predictive control with restricted constraints

Predictive regulation of linear discrete-time systems subject to unknown but bounded disturbances and to state/control constraints is addressed. An algorithm based on constraint restrictions is presented and its stability properties are analysed.

Sequential approximation of feasible parameter sets for identification with set membership uncertainty

In this paper the problem of approximating the feasible parameter set for identification of a system in a set membership setting is considered. The system model is linear in the unknown parameters. A recursive procedure providing an approximation of the parameter set of interest through parallelotopes is presented, and an efficient algorithm is proposed. Its computational complexity is similar to that of the commonly used ellipsoidal approximation schemes. Numerical results are also reported on some simulation experiments conducted to assess the performance of the proposed algorithm.

Brief paper: Recursive state bounding by parallelotopes

In this paper, the problem of recursively estimating the state uncertainty set of a discrete-time linear dynamical system is addressed. A novel approach based on minimum-volume bounding parallelotopes is introduced and an algorithm of polynomial complexity is derived. Simulation results and performance comparisons with ellipsoidal recursive state-bounding algorithms are also given.

Paper: Performance improvements of self-tuning controllers by multistep horizons: The MUSMAR approach

The problem of adaptively controlling a linear multivariable plant according to a quadratic cost functional defined over a control horizon of arbitrary length is discussed. In this context, the proposed algorithm, referred to by the acronym MUSMAR, is shown to be a natural generalization of standard self-tuning controllers. By increasing the control horizon length, the MUSMAR closely approximates a steady-state LQG controller inheriting the intrinsic robustness of LQG design. Analysis and simulations give evidence of several attractive features of the MUSMAR self-tuner when applied to plants for which standard adaptive controllers fail to converge or yield an unacceptable performance.

Block recursive parallelotopic bounding in set membership identification

In this paper, a procedure for the recursive approximation of the feasible parameter set of a linear model with a set membership uncertainty description is provided. Approximating regions of parallelotopic shape are considered. The new contribution of this paper consists in devising a general procedure allowing for block processing of q > 1 measurements at each recursion step. Based on this, several approximation strategies for polytopes are presented. Simulation experiments are performed, showing the effectiveness of the algorithm as compared to the original algorithm processing one measurement at each step.In this work, we formulate a new approach to simultaneous constrained model predictive control and identification (MPCI). The proposed approach relies on the development of a persistent excitation (PE) criterion for processes described by DARX models. That PE criterion is used as an additional constraint in the standard on-line optimization of MPC. The resulting on-line optimization problem of MPCI is handled by successively solving a series of semi-definite programming problems. Advantages of MPCI in comparison to other closed-loop identification methods are (a) Constraints on process inputs and outputs are handled explicitly, (b) Deterioration of output regulation is kept to a minimum, while closed-loop identification is performed. The applicability of the method is illustrated by a number of simulation studies. Theoretical and computational issues for further investigation are suggested.

Robustness of multipredictor adaptive regulators: MUSMAR

Abstract Effects of multipredictor information in adaptive control are studied, particularly from the standpoint of plant structural uncertainties and unmodelled dynamics. By an ODE analysis it is shown that, for an ARMAX plant, the equilibria of a multipredictor based LQ self-tuning regulator, namely, the MUSMAR algorithm, are the extrema of a receding horizon variant of the cost. Further, if the control horizon is large enough, the MUSMAR possible convergence points tightly approximate the local minima of the unconditional quadratic cost constrained to the chosen regulator regressor.

NARX models of an industrial power plant gas turbine

This brief reports the experience with the identification of a nonlinear autoregressive with exogenous inputs (NARX) model for the PGT10B1 power plant gas turbine manufactured by General Electric-Nuovo Pignone. Two operating conditions of the turbine are considered: isolated mode and nonisolated mode. The NARX model parameters are estimated iteratively with a Gram-Schmidt procedure, exploiting both forward and stepwise regression. Many indexes have been evaluated and compared in order to perform subset selection in the functional basis set and determine the structure of the nonlinear model. Various input signals (from narrow to broadband) for identification and validation have been considered.

Conditional central algorithms for worst case set-membership identification and filtering

This paper deals with conditional central estimators in a set membership setting. The role and importance of these algorithms in identification and filtering is illustrated by showing that problems like worst case optimal identification and state filtering, in contexts in which disturbances are described through norm bounds, are reducible to the computation of conditional central algorithms. The conditional Chebyshev center problem is solved for the case when energy norm-bounded disturbances are considered. A closed-form solution is obtained by finding the unique real root of a polynomial equation in a semi-infinite interval.

Dual mode predictive tracking of piecewise constant references for constrained linear systems

In this paper, the attention is focused on the tracking of a piecewise constant reference for an LTI system in presence of state-control constraints and bounded disturbance inputs. Exploiting robust controlled invariant sets in the extended state-setpoint space, we propose a novel predictive tracking algorithm which yields robust constraint satisfaction and good tracking performance with a reasonable on-line computational burden. The main idea is to adopt a dual-mode controller that operates as a predictive regulator whenever the desired setpoint is feasible or in feasibility recovery mode otherwise. Simulation results demonstrate the improved tracking performance of the proposed method with respect to available techniques.

Knowledge-based system for multi-target tracking in a littoral environment

The paper addresses how to efficiently exploit the knowledge-base (KB), e.g. environmental maps and characteristics of the targets, in order to gain improved performance in the tracking of multiple targets via measurements provided by a ship-borne radar operating in a littoral environment. In this scenario, the nonhomogeneity of the surveillance region makes the conventional tracking systems (not using the KB) very sensitive to false alarms and/or missed detections. It is demonstrated that an effective use of the KB can be exploited at various levels of the tracking algorithms so as to significantly reduce the number of false alarms, missed detections, and false tracks and improve true target track life. The KB is exploited at two different levels. First, some key parameters of the tracking system are made dependent upon the track location, e.g., sea, land, coast, meteo zones (i.e., zones affected by meteorological phenomena) etc. Second, modifications are introduced to cope with a priori identified regions nit hi high clutter density (e.g. littoral areas, roads, meteo zones etc.). To evaluate the behavior of the proposed knowledge-based tracking systems, extensive results are presented using both simulated and real radar data