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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.

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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

SUMMARYThis paper addresses model-based predictive regulation for a linear discrete-time system in the presence of unknown but bounded disturbances, partial state information and state/control constraints. The proposed nonlinear dynamic compensator uses a set-valued estimator, which recursively updates the membership set of the plant state, along with a receding-horizon regulator which selects on-line the control variable depending upon the current state membership set. It is shown that the overall scheme preserves feasibility if this is assumed from the outset, and hence guarantees closed-loop stability and constraint ful"lment. These properties rely on exact set-membership estimation. A simple approximation scheme which avoids setmembership estimation but preserves stability is also proposed and the relative performance/complexity tradeo!s are discussed. Simulation results demonstrate the e!ectiveness of the proposed method.

SUMMARYPredictive control of nonlinear systems is addressed by embedding the dynamics into an LPV system and by computing robust invariant sets. This mitigates the on-line computational burden by transferring most of the computations off-line. Benefits and conservatism of this approach are discussed in relation with the control of a critical mechanical system.

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