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This work presents an approach to the design of a pressure-tracking controller for a pneumatic cylinder system for which the connection port comprises a non-negligible restriction. The pressure chamber of this system is difficult to control because the servovalve and the connection port together constitute a double restriction. Moreover, the pressure in the cylinder cannot be directly measured and cannot be expressed as an explicit function of measurable pressure. A model of this system can be approximated using the singular perturbation approach. The tracking control problem is further formulated as a stabilization of a nonlinear time-varying system. The control law is divided into two stages: the first stage linearizes the system, and the second stabilizes the tracking error, which is formulated as a linear time-varying differential equation. The gains of the stabilization control law are found using the Linear Matrix Inequality (LMI) method. Simulation and experimental results show that the proposed control system performs well. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear multivariable fitting model to decompose the optimal policies obtained by dynamic programming of a unique aggregated reservoir. The nonlinear functions are generated using radial basis functions (RBF) neural networks. In this method the potential energy of all the reservoirs in the hydropower system is added to form one equivalent reservoir. The operating policy of the equivalent reservoir is determined by stochastic dynamic programming, and finally the operating rules of each reservoir are determined using RBF neural networks. To improve the multivariable representation of the data, a series of piecewise RBF neural networks is determined using clustering analysis. A fuzzy clustering approach is used to determine the RBF's parameters. This approach has the advantages of being fast and simple to implement with well-established convergence properties. It also has a good representation of the covariance matrix, since all the data belong to all the classes at the same time with different membership grades. A comparison with the back propagation learning and principal components techniques is also reported for Québec's La Grande River installations. As a result, the proposed approach gives satisfactory operating rules compared with principal component analysis, and the CPU time is reduced by a factor of 15 to 20 compared with the back propagation technique. [ABSTRACT FROM AUTHOR]

This paper describes a nonlinear disaggregation technique for the operation of multireservoir systems. The disaggregation is done by training a neural network to give, for an aggregated storage level, the storage level of each reservoir of the system. The training set is obtained by solving the deterministic operating problem of a large number of equally likely flow sequences. The training is achieved using the back propagation method, and the minimization of the quadratic error is computed by a variable step gradient method. The aggregated storage level can be determined by stochastic dynamic programming in which all hydroelectric installations are aggregated to form one equivalent reservoir. The results of applying the learning disaggregation technique to Quebec's La Grande river are reported, and a comparison with the principal component analysis disaggregation technique is given. [ABSTRACT FROM AUTHOR]