Browsing by Author "Lekutai, Gaviphat"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Adaptive Self-Tuning Neuro Wavelet Network Controllers
    Lekutai, Gaviphat (Virginia Tech, 1997-03-31)
    Single layer feed forward neural networks with hidden nodes of adaptive wavelet functions (wavenets) have been successfully demonstrated to have potential in many applications. Yet applications in the process control area have not been investigated. In this paper an application to a self-tuning design method for an unknown nonlinear system is presented. Different types of frame wavelet functions are integrated for their simplicity, availability, and capability of constructing adaptive controllers. Infinite impulse response (IIR) recurrent structures are combined in cascade to the network to provide a double local structure resulting in improved speed of learning. In particular, neuro-based controllers assume a certain model structure to approximate the system dynamics of the "unknown" plant and generate the control signal. The capability of neuro-controllers to self-tuning of an unknown nonlinear plants is then illustrated through design examples. Simulation results demonstrate that the self-tuning design methods are directly applicable for a large class of nonlinear control systems.
  • Thumbnail Image
    Kalman filtering in noisy nonlinear systems using a jump matrix approach
    Lekutai, Gaviphat (Virginia Tech, 1993-05-05)
    A computationally efficient estimation technique is presented for a class of nonlinear systems consisting of memoryless nonlinearities combined with linear dynamic processes. The modeling approach is based on a useful sampled-data method for simulating such systems by adding a system state for each nonlinear element. The nonlinear estimator is next developed along the lines of the Kalman filter, but in contrast to the Extended Kalman Filter (EKF) the present approach does not require the linearization step after each recursive cycle. In addition, it also appears free from the well known divergence problems associated with the EKF. It is demonstrated that this new method is directly applicable to those feedback systems with both major nonlinearities, for example saturating gain blocks, and stochastic disturbances-- an example extremely difficult to handle with EKF techniques.