Abstract: The reduced complexity auto-tuning process controller system and method is a control scheme in which, for each process variable, a single gain K, which is initialized with a sufficiently small value, is iteratively auto-tuned using a predetermined discrete sample time and learning rate. A plant error is calculated and summed with an output of a one-sample delay, the sum being input to the one-sample delay. The combined output of the one-sample delay is multiplied by the input signal, i.e., used as a gain constant of the control input signal. The control input signal times this gain constant is fed to the plant input, thereby reducing error in the plant.

Abstract: The reduced complexity auto-tuning process controller system and method is a control scheme in which, for each process variable, a single gain K, which is initialized with a sufficiently small value, is iteratively auto-tuned using a predetermined discrete sample time and learning rate. A plant error is calculated and summed with an output of a one-sample delay, the sum being input to the one-sample delay. The combined output of the one-sample delay is multiplied by the input signal, i.e., used as a gain constant of the control input signal. The control input signal times this gain constant is fed to the plant input, thereby reducing error in the plant.

Abstract: The method for Hammerstein modeling of a steam generator plant includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN). Particle swarm optimization (PSO), typically known for its heuristic search capabilities, is used for estimating the parameters of the RBFNN. Parameters of the linear part are estimated using a numerical algorithm for subspace state-space system identification (N4SID).

Abstract: The method for the identifying of multiple input, multiple output (MIMO) Hammerstein models that includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN).

Abstract: The identification of Hammerstein models relates to a computerized method for identifying Hammerstein models in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN), and in which a particle swarm optimization (PSO) algorithm is used to estimate the neural network parameters and a numerical algorithm for subspace state-space system identification (N4SID) is used for estimation of parameters of the linear part.

Abstract: The computerized method for identifying Hammerstein models is a method in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN). Accurate identification of a Hammerstein model requires that output error between the actual and estimated systems be minimized. Thus, the problem of identification is an optimization problem. A hybrid algorithm, based on least mean square (LMS) principles and the Subspace Identification Method (SIM) is developed for the identification of the Hammerstein model. LMS is a gradient-based optimization algorithm that searches for optimal solutions in the negative direction of the gradient of a cost index. In the method, LMS is used for estimating the parameters of the RBFNN. For estimation of state-space matrices, the N4SID algorithm for subspace identification is used.

Abstract: The method for the identifying of multiple input, multiple output (MIMO) Hammerstein models that includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN).

Abstract: The identification of Hammerstein models relates to a computerized method for identifying Hammerstein models in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN), and in which a particle swarm optimization (PSO) algorithm is used to estimate the neural network parameters and a numerical algorithm for subspace state-space system identification (N4SID) is used for estimation of parameters of the linear part.

Abstract: The computerized method for identifying Hammerstein models is a method in which the linear dynamic part is modeled by a space-state model and the static nonlinear part is modeled using a radial basis function neural network (RBFNN). Accurate identification of a Hammerstein model requires that output error between the actual and estimated systems be minimized. Thus, the problem of identification is an optimization problem. A hybrid algorithm, based on least mean square (LMS) principles and the Subspace Identification Method (SIM) is developed for the identification of the Hammerstein model. LMS is a gradient-based optimization algorithm that searches for optimal solutions in the negative direction of the gradient of a cost index. In the method, LMS is used for estimating the parameters of the RBFNN. For estimation of state-space matrices, the N4SID algorithm for subspace identification is used.

Abstract: The method for Hammerstein modeling of a steam generator plant includes modeling of the linear dynamic part of a Hammerstein model with a state-space model, and modeling the nonlinear part of the Hammerstein model with a radial basis function neural network (RBFNN). Particle swarm optimization (PSO), typically known for its heuristic search capabilities, is used for estimating the parameters of the RBFNN. Parameters of the linear part are estimated using a numerical algorithm for subspace state-space system identification (N4SID).