Self-tuning Minimum Variance Research Articles

Iterative Self-Tuning Minimum Variance Control of a Nonlinear Autonomous Underwater Vehicle Maneuvering Model

This paper addresses the problem of control design for a nonlinear maneuvering model of an autonomous underwater vehicle. The control algorithm is based on an iteration technique that approximates the original nonlinear model by a sequence of linear time-varying equations equivalent to the original nonlinear problem and a self-tuning control method so that the controller is designed at each time point on the interval for trajectory tracking and heading angle control. This work makes use of self-tuning minimum variance principles. The benefit of this approach is that the nonlinearities and couplings of the system are preserved, unlike in the cases of control design based on linearized systems, reducing in this manner the uncertainty in the model and increasing the robustness of the controller. The simulations here presented use a torpedo-shaped underwater vehicle model and show the good performance of the controller and accurate tracking for certain maneuvering cases.

Considerations Regarding the Design of a Minimum Variance Control System for an Induction Generator

This paper presents a comparative analysis regarding a self-tuning minimum variance control system of a double-fed induction generator with load and connected to a power system through a long transmission line. A new complex nonlinear model describing this relationship between the induction generator, electrical consumer, transmission line, and power system is designed and implemented to simulate the controlled plant behavior. Starting from a simplified linear model of this complex plant, obtained through linearization of its nonlinear model around an operating point, the minimum variance control law design is performed by minimizing a cost criterion function. The main goal and also the paper novelty consists of the identification of a minimum order of this linearized model used to design a reduced order control law, which can still provide good control performance.

Wiener-filter-based Minimum Variance Self-tuning Regulation

This paper presents a new direct discrete-time self-tuning minimum variance control scheme for not necessarily minimum-phase SISO systems that are operating in a stochastic environment. It is assumed that the open-loop system is asymptotically stable, or can be stabilized by a fixed controller, and that the noise model satisfies the standard SPR properties. No assumptions are made regarding the zero polynomial of the controlled plant. The proposed direct self-tuning scheme consists of two-parameter adaptation algorithms (PAA) running simultaneously. The first PAA is an adaptive whitening filter. The adaptive whitening filter error residual is fed into an adaptive finite impulse response (FIR) filter, which generates the control signal. The second PAA estimates the parameters of the optimal Wiener FIR filter which minimizes the variance of the system output. A complete stability analysis and a discussion of the convergence and self-tuning properties of the proposed self-tuning scheme is included.

Convergence rate for RLS-based direct self-tuning minimum-variance regulation of ARMAX minimum phase plants

An upper bound for the error convergence rate of recursive least squares (RLS)-based direct self-tuning minimum-variance (DSTMV) regulation of minimum phase multiple time-delay autoregressive moving average with exogenous input (ARMAX) plant is derived. The RLS algorithm used is k-interlaced, with k being the plant time-delay. The bound is derived for known fixed b/sub 0/ by extending a previously proposed methodology. The bound provides a joint explanation of DSTMV regulation stability and parameter estimate convergence. The paper demonstrates that self-tuning is based on convergence properties of RLS as well as on the excitation quality of plant white noise, which generates (via controller feedback) the plant input.

Self-Tuning Control of the Nuclear Reactor Power

Problems of self-tuning minimum variance control of the thermal power of nuclear reactors are considered. The self-tuning control system is based on a prediction model, its parameters being estimated in the process of identification in the closed loop. New algorithms for the determination of reactivity control values and control rod positions with respect to the restrictions for the external reactivity introduction rate were synthesized. The processes of the self-tuning control of the thermal power are illustrated for the cases of thermal neutron and fast breeder reactors.

Simultaneous Adaptive Control of chemical composition, Fineness and Maximum Quantity of Ground Materials at a Closed Circuit Ball Mill

A new, modified self-tuning minimum variance regulator algorithm developed for multiple input multiple output systems is considered with required average for finite time. This control strategy has been developed for the optimal control of chemical composition at cement raw material blending with batch operation. By application of self-tuning regulators using feedback and feedforward information a new control system is introduced for simultaneous adaptive control of fineness and maximum quantity of ground materials at a closed circuit ball mill. The results obtained by real-time experiments are also presented.