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Abstract: In this paper we show some applications of Advanced and Parallel Computing to the study of mathematical models in Systems Biology and in particular of the network of biochemical reactions occurring inside a cell. Due to their high complexity, the numerical study of these systems must be approached by means of sophisticated Advanced Computing tools. In a deterministic framework, we show two examples of application of Optimal Control techniques to the study of the effects of a drug on enzyme reactions occurring inside a cell, where “ad-hoc” algorithms for the numerical solution of the Hamilton–Jacobi–Bellman equation are used in a parallel environment. [ABSTRACT FROM AUTHOR]

Higher order iterative learning control (HO-ILC) algorithms use past system control information from more than one past iterative cycle. This class of ILC algorithms have been proposed aiming at improving the learning efficiency and performance. This paper addresses the optimality of HO-ILC in the sense of minimizing the trace of the control error covariance matrix in the presence of a class of uncorrelated random disturbances. It is shown that the optimal weighting matrices corresponding to the control information associated with more than one cycle preceding the current cycle are zero. That is, an optimal HO-ILC does not add to the optimality of standard first-order ILC in the sense of minimizing the trace of the control error covariance matrix. The system under consideration is a linear discrete-time varying systems with different relative degree between the input and each output. [ABSTRACT FROM AUTHOR]

In this paper, a moving algorithm for on-line identification of continuous-time systems is developed. With the proposed algorithm, the observed input–output data can be directly used to estimate the system parameters without any numerical pre-processing, and by means of a recursive formula the estimates can be updated step by step without repeatedly computing the matrix inversion. In this way, the use of both computer memory and computing time can be reduced. Besides, the computations are simple and straightforward. From the moving identification algorithm, a linear moving model can be obtained to represent the control systems. The on-line optimal control algorithm is also developed via the linear moving model. A slider-crank motion control system is used to illustrate that the proposed on-line identification and optimal control algorithms can give satisfactory results. [Copyright &y& Elsevier]