Stability analysis of a new non-linear pole-zero placement controller incorporating neural networks

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 ...

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.

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.

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.

This article presents the derivation of a new robust multivariable adaptive controller, which minimizes a cost function, incorporating system input, system output, and set point. It provides an adaptive mechanism that ensures that both the closed-loop poles and zeros are placed at their prespecified positions. The proposed design overcomes the shortcomings of other pole placement designs by combining the robustness of classical control strategy of pole-zero placement with the flexibility of self-tuning generalized minimum variance control. It tracks set-point changes with the desired speed of response, penalises excessive control action, and can be applied to nonminimum phase systems. Additionally, at steady state, the controller has the ability to regulate the constant load disturbance to zero. Example simulation results using both simulated and real plant models demonstrate the effectiveness of the proposed controller.

The design of reliable navigation and control systems for Unmanned Aerial Vehicles (UAVs) based only on visual cues and inertial data has many unsolved challenging problems, ranging from hardware and software development to pure control-theoretical issues. This chapter addresses these issues by developing and implementing an adaptive vision-based autopilot for navigation and control of small and mini rotorcraft UAVs. The proposed autopilot includes a Visual Odometer (VO) for navigation in GPS-denied environments and a nonlinear control system for flight control and target tracking. The VO estimates the rotorcraft ego-motion by identifying and tracking visual features in the environment, using a single camera mounted on-board the vehicle. The VO has been augmented by an adaptive mechanism that fuses optic flow and inertial measurements to determine the range and to recover the 3D position and velocity of the vehicle. The adaptive VO pose estimates are then exploited by a nonlinear hierarchical controller for achieving various navigational tasks including take-off, landing, hovering, trajectory tracking, target tracking, etc. Furthermore, the asymptotic stability of the entire closed-loop system has been established using systems in cascade and adaptive control theories. Experimental flight test data over various ranges of the flight envelope illustrate that the proposed vision-based autopilot performs well and allows a mini rotorcraft UAV to achieve autonomously advanced flight behaviours by using vision.

The design of reliable navigation and control systems for Unmanned Aerial Vehicles (UAVs) based only on visual cues and inertial data has many unsolved challenging problems, ranging from hardware and software development to pure control-theoretical issues. This paper addresses these issues by developing and implementing an adaptive vision-based autopilot for navigation and control of small and mini rotorcraft UAVs. The proposed autopilot includes a Visual Odometer (VO) for navigation in GPS-denied environments and a nonlinear control system for flight control and target tracking. The VO estimates the rotorcraft ego-motion by identifying and tracking visual features in the environment, using a single camera mounted on-board the vehicle. The VO has been augmented by an adaptive mechanism that fuses optic flow and inertial measurements to determine the range and to recover the 3D position and velocity of the vehicle. The adaptive VO pose estimates are then exploited by a nonlinear hierarchical controller for achieving various navigational tasks such as take-off, landing, hovering, trajectory tracking, target tracking, etc. Furthermore, the asymptotic stability of the entire closed-loop system has been established using systems in cascade and adaptive control theories. Experimental flight test data over various ranges of the flight envelope illustrate that the proposed vision-based autopilot performs well and allows a mini rotorcraft UAV to achieve autonomously advanced flight behaviours by using vision.

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.

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.< >

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

An adaptive system for linear systems with unknown parameters is a nonlinear system. The analysis of such adaptive systems requires similar techniques to analyse nonlinear systems. Therefore it is natural to treat adaptive control as a part of nonlinear control systems. Nonlinear and Adaptive Control Systems treats nonlinear control and adaptive control in a unified framework, presenting the major results at a moderate mathematical level, suitable for MSc students and engineers with undergraduate degrees. Topics covered include introduction to nonlinear systems; state space models; describing functions for common nonlinear components; stability theory; feedback linearization; adaptive control; nonlinear observer design; backstepping design; disturbance rejection and output regulation; and control applications, including harmonic estimation and rejection in power distribution systems, observer and control design for circadian rhythms, and discrete-time implementation of continuous-time nonlinear control laws.

We show how frequency domain design procedures can be effectively used in nonlinear fuzzy control systems. For this purpose, theoretical justifications of the approach discussed here are provided to show that solid notions from nonlinear system and control theory can be successfully applied in the fuzzy control domain. The methodology proposed is very simple, but powerful, effective and practical. In addition, it supports systematic control system design and stability analysis in the frequency domain for systems described by mathematical, linguistic or experimental models. Several case studies are included to illustrate the usefulness of frequency response in conventional fuzzy control systems analysis and design.

Theory of nonlinear control

MTN optimal control of MIMO non-affine nonlinear time-varying discrete systems for tracking only by output feedback