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Recent work has formulated an MPC strategy for a sustainable development problem, focused on the control of a stochastic, non-linear, economic model called PROMETHEUS. This work also takes PROMETHEUS to be the system. To use MPC an analytic approximation of PROMETHEUS is required, with previous work focusing on linear and non-linear time invariant models. Global optimality of the solution could not be guaranteed, since to achieve reasonable accuracy in iterative fitting was required which destroyed convexity of the optimization. In this paper an LTV model is considered and is shown to reduce the approximation error. It is demonstrated that the model can be finitely parameterized and that the predominant time variation in PROMETHEUS is from a decaying exponential weighting on the predicted input sequence. It is suggested that this variation is a representation of inflation. The sustainable development MPC methodology is recast for such a model. Convexity of the MPC optimization with an LTV model guarantees global optimality; results are presented which demonstrate the improvement in performance which this allows. [ABSTRACT FROM AUTHOR]

Synchronous generators have a natural different time scale dynamics. That is why, for modelling and control design in such systems, the methods of singular perturbations are widely used. In this paper the possibilities of sliding mode control design for synchronous generators are analysed. With this aim the concept of singular perturbation is revised in order to use it for relay control systems. The obtained results are used for sliding mode control of synchronous generator. [ABSTRACT FROM AUTHOR]

This paper investigates the problem of Hankel norm model reduction for linear systems with time-varying delay in the state. For a given stable system, our attention is focused on the construction of reduced-order model, which guarantees the corresponding error system to be asymptotically stable and has a specified Hankel norm error performance. Two different approaches are proposed to solve this problem. One casts the model reduction into a convex optimization problem by using a linearization procedure, and the other is based on the cone complementarity linearization idea, which casts the model reduction into a sequential minimization problem subject to linear matrix inequality constraints. Both continuous and discrete time eases are considered. A numerical example is provided to show the effectiveness of the proposed theory. [ABSTRACT FROM AUTHOR]

Stable indirect and direct adaptive controllers are presented for a class of input-output feedback linearizable timevarying non-linear systems. The radial basis function neural networks are used as on-line approximators to learn the time-varying characteristics of system parameters. Stability results are given in the paper, and the performance of the indirect and direct adaptive schemes is demonstrated through a fault-tolerant engine control problem where the faults are naturally time-varying. [ABSTRACT FROM AUTHOR]