Simulation Experiments with Self Tuning PSD Control Algorithm

ABSTRACTThe main purpose of this paper is to design a self-tuning control algorithm for an adaptive cruise control (ACC) system that can adapt its behaviour to variations of vehicle dynamics and uncertain road grade. To this aim, short-time linear quadratic form (STLQF) estimation technique is developed so as to track simultaneously the trend of the time-varying parameters of vehicle longitudinal dynamics with a small delay. These parameters are vehicle mass, road grade and aerodynamic drag-area coefficient. Next, the values of estimated parameters are used to tune the throttle and brake control inputs and to regulate the throttle/brake switching logic that governs the throttle and brake switching. The performance of the designed STLQF-based self-tuning control (STLQF-STC) algorithm for ACC system is compared with the conventional method based on fixed control structure regarding the speed/distance tracking control modes. Simulation results show that the proposed control algorithm improves the performance of throttle and brake controllers, providing more comfort while travelling, enhancing driving safety and giving a satisfactory performance in the presence of different payloads and road grade variations.

A system consisting of two AT&T personal computers and a Motorola DSP56001 digital signal processor for implementation of self-tuning pole placement control law is presented. One of the AT&T personal computers is used as a variable parameter digital plant to be controlled. The Motorola DSP56001 digital signal processor is used to compute the plant parameters in real-time from input-output data. The computed plant parameters are used to tune the parameters of a pole placement controller implemented on the Motorola DSP56001 digital signal processor. The other AT&T personal computer is used as a host computer to develop machine code of the parameter identification and self-tuning control algorithms. Results on self-tuning control of a second order plant are presented.

The ability to compensate for disturbances resulting from nonlinear phenomena such as Coulomb friction in inertial stabilization systems has been demonstrated to be feasible when the plant dynamics of the system are accurately modeled. However, for cases where the plant is unknown or changing, a self-tuning control algorithm is desired to provide robustness. This paper formulates such a self-tuning control algorithm with specific application to systems with inherent nonlinearities. The ability of the algorithm to self-tune and compensate for nonlinear induced disturbances is demonstrated for an inertial stabilization gimbal control system with Coulomb type bearing friction.

This paper presents an investigation into the development of an active control mechanism for noise cancellation and vibration suppression within an adaptive control framework. An active noise control (ANC) system is designed utilising a SIMO control structure to yield optimum cancellation of broadband noise, emanating from a point source, at a set of observation points in a linear free-field propagation medium. The controller design relations are formulated such as to allow online design and implementation and, thus, yield a self-tuning control algorithm. The algorithm is implemented on a digital processor and its performance assessed in the cancellation of broadband noise. A cantilever beam system in transverse vibration is considered for vibration suppression. Such a system has an infinite number of modes although in most cases the lower modes are the dominant ones requiring attention. The unwanted vibrations in the structure are assumed to be caused by a single point disturbance of broadband nature. These are detected and suitable suppression signals generated via point actuators to yield vibration suppression over a broad frequency range along the beam. The selftuning control algorithm developed within the ANC system is implemented within a SIMO vibration control structure and its performance assessed.< >

The aim of this paper is to investigate the application possibilities of a self-tuning control method to a pressurized-water reactor (PWR) nuclear power plant. A self-tuning control algorithm which incorporates pole assignment into the generalized minimum variance strategy with a particular form of cost function is employed. This algorithm enables closed-loop system stability characteristics to be readily specified and facilitates reference following. The control system design is based on a second-order linear model with unknown, time-varying parameters. To ensure that this low-order model describes the complex real dynamics well enough for control purposes, control parameters are updated on-line with a recursive estimation sequences of the extended least-squares method. Weighting polynomials are also adjusted on-line to keep closed-loop poles at the desired location and to satisfy the zero steady-state condition and disturbance rejection. The purpose of this control system is to hold the average coolant ...

A Self-Tuning Control Algorithm in the Presence of Plant-Model Order Mismatch

A novel self-tuning controller adopting the diminishing probing signal methods, givcn in [12] and [13] is proposed. It can control unstable and/or non minimum phase multivariable stochastic systems with a general time delay structure. It is shown that this self-tuning control algorithm not only has global convergence properties but also generates, strongly consistent parameter estimates. Simulation results are presented to demonstrate the new algorithm to be effective.

According to practical rolling condition and the requirement of shape control, an adaptive global flatness control system is designed to realize automatic shape control of strip. The set point can be provided automatically for each actuator by a set-up model, and the adjustment of each actuator is obtained using fuzzy recognition method. The self-tuning controller with pole placement is then used for the hydraulic system of the rolling mill. The control system is shown to be effective by the simulation experiments.

Self-Tuning Control of Blood Pressure: Single Variable and Multivariable Studies

Adaptive Self-Tuning PIDD2 Algorithm

Step responses of nonlinear non-minimum phase systems

Design and real-time implementation of a self-tuning flexible ac transmission system (FACTS) controller is illustrated for power oscillation damping. Although the system model is not required for self-tuning control design, it is shown to perform similar to a model-based design. For parameter estimation, the classical recursive least square (RLS) is supplemented by a random walk (RW) with a switched structure and compared to standard variable forgetting factor (VFF) approach. It is shown that the RW improves the accuracy and convergence of the estimated system parameters, which is critical to self-tuning control following large disturbances. The performance is validated in real time using a commercial real-time simulation platform. The control computation time is shown to be considerably less than the typical sampling time used for power oscillation damping applications demonstrating the feasibility of self-tuning FACTS controllers in practice.

The stability analysis and parameter convergence of a newly reported self-tuning pole-zero placement controller algorithm for non-linear dynamic systems are studied. The original controller overcomes the shortcomings of other linear designs and provides an adaptive mechanism, which ensures that both the closed-loop poles and zeros are placed at their prespecified positions. The closed loop system stability and the parameter convergence are derived by assuming that the reference signal is bounded and the process non-linearity is globally bounded.

According to principle of bearingless motor, this paper proposes the configuration and the operating principle of AC 2-degree freedom hybrid magnetic bearing (HMB), which is driven by a 3-phase AC power inverter. By using equivalent magnetic circuit method, the mathematics models of the system are deduced. Then the parameter self-tuning fuzzy PID control algorithm for HMB is researched. In addition, a digital control system of HMB is designed based on parameter self-tuning fuzzy PID and TMS320F2812 DSP. The simulation results show the proposed algorithm has superior dynamic and static performance as well as strong disturbance rejection ability.

Self-tuning adaptive control algorithms, that employ explicit dead-time identification techniques, have been discussed in the literature vis-a-vis the diesel-driven power prime-mover. Such algorithms are numerically intensive owing to the complicated identification algorithms. This paper discusses the implementation of the above mentioned algorithms on a Texas Instruments' DSP 320C30 based system. This digital signal processor has sufficient numerical capability to carry out all the real-time computations at sampling intervals of 10 ms. It can also perform well in the matters of speed measurement and control generation without any significant additional hardware. The results of real-time tests are compared with that obtained using the Woodward Governor Company's 701 prime-mover controller.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>