D I Soloway

Bio: D I Soloway is an academic researcher from Langley Research Center. The author has contributed to research in topics: FSA-Red Algorithm & Model predictive control. The author has an hindex of 1, co-authored 1 publications receiving 106 citations.

Neural Generalized Predictive Control: A Newton-Raphson Implementation

Abstract: An efficient implementation of Generalized Predictive Control using a multi-layer feedforward neural network as the plant''s nonlinear model is presented. In using Newton-Raphson as the optimization algorithm, the number of iterations needed for convergence is significantly reduced from other techniques. The main cost of the Newton-Raphson algorithm is in the calculation of the Hessian, but even with this overhead the low iteration numbers make Newton-Raphson faster than other techniques and a viable algorithm for real-time control. This paper presents a detailed derivation of the Neural Generalized Predictive Control algorithm with Newton-Raphson as the minimization algorithm. Simulation results show convergence to a good solution within two iterations and timing data show that real-time control is possible. Comments about the algorithm''s implementation are also included.

New Chaotic PSO-Based Neural Network Predictive Control for Nonlinear Process

Abstract: In this letter, a novel nonlinear neural network (NN) predictive control strategy based on the new tent-map chaotic particle swarm optimization (TCPSO) is presented. The TCPSO incorporating tent-map chaos, which can avoid trapping to local minima and improve the searching performance of standard particle swarm optimization (PSO), is applied to perform the nonlinear optimization to enhance the convergence and accuracy. Numerical simulations of two benchmark functions are used to test the performance of TCPSO. Furthermore, simulation on a nonlinear plant is given to illustrate the effectiveness of the proposed control scheme

Model-following neuro-adaptive control design for non-square, non-affine nonlinear systems

Abstract: A new model-following adaptive control design technique for a class of non-affine and non-square nonlinear systems using neural networks is proposed. An appropriate stabilising controller is assumed available for a nominal system model. This nominal controller may not be able to guarantee stability/satisfactory performance in the presence of unmodelled dynamics (neglected algebraic terms in the mathematical model) and/or parameter uncertainties present in the system model. In order to ensure stable behaviour, an online control adaptation procedure is proposed. The controller design is carried out in two steps: (i) synthesis of a set of neural networks which capture matched unmodelled (neglected) dynamics or model uncertainties because of parametric variations and (ii) synthesis of a controller that drives the state of the actual plant to that of a desired nominal model. The neural network weight update rule is derived using Lyapunov theory, which guarantees both stability of the error dynamics (in a practical stability sense) and boundedness of the weights of the neural networks. The proposed adaptation procedure is independent of the technique used to design the nominal controller, and hence can be used in conjunction with any known control design technique. Numerical results for two challenging illustrative problems are presented, which demonstrate these features and clearly bring out the potential of the proposed approach.

Nonlinear control and synchronization of multiple Lagrangian systems with application to tethered formation flight spacecraft

Abstract: Thesis (Sc. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.