Digital Self-tuning Controllers Research Articles

Comparison of adaptive pitch control strategies for a wind turbine benchmark model

PurposeThe wind turbine (WT) is a complex system subjected to wind disturbances. Because the aerodynamics is nonlinear, the control is thus challenging. For the variation of wind speed when rated power is delivered at rated wind speed, the power is limited to the rate by the pitching of the blades of the turbine. This paper aims to address pitch control with the WT benchmark model. The possible use of appropriate adaptive controller design that modifies the control action automatically identifying any change in system parameters is explored.Design/methodology/approachTo deal with pitch control problem when wind speed exceeds the rated wind speed of the WT, six digital self-tuning controller (STC) with different structures such as proportional integral (PI), proportional derivative (PD), Dahlin’s, pole placement, deadbeat and Takahashi has been taken herein. The system model is identified as a second-order autoregressive exogenous (ARX) model by three techniques for comparison: recursive least square method (RLS), RLS with exponential forgetting and RLS with adaptive directional forgetting identification methods. A comparative study of three identification methods, six adaptive controllers with the conventional PI controller and sliding mode controller (SMC), are shown.FindingsAs per the results, the best improvement in control of the output power by pitching in full load region of benchmark model is achieved by self-tuning PD controller based on RLS with adaptive directional forgetting method. The adaptive control design has a future in WT control applications.Originality/valueA comparative study of identification methods, six adaptive controllers with the conventional PI controller and SMC, are shown here. As per the results, the best improvement in control of the output power by pitching in the full load region of the benchmark model has been achieved by self-tuning PD controller. The best identification method or the system is RLS with an adaptive directional forgetting method. Instead of a step input response design for the controllers, the controller design has been carried out for the stochastic wind and the performance is adjudged by the normalized sum of square tracking error (NSSE) index. The validation of the proposed self-tuning PD controller has been shown in comparison to the conventional controller with Monte-Carlo analysis to handle model parameter alteration and erroneous measurement issues.

Automatic Control System for Thermal Comfort Based on Predicted Mean Vote and Energy Saving

For human-centered automation, this study presents a wireless sensor network using predicted mean vote (PMV) as a thermal comfort index around occupants in buildings. The network automatically controls air conditioning by means of changing temperature settings in air conditioners. Interior devices of air conditioners thus do not have to be replaced. An adaptive neurofuzzy inference system and a particle swarm algorithm are adopted for solving a nonlinear multivariable inverse PMV model so as to determine thermal comfort temperatures. In solving inverse PMV models, the particle swarm algorithm is more accurate than ANFIS according to computational results. Based on the comfort temperature, this study utilizes feedforward-feedback control and digital self-tuning control, respectively, to satisfy thermal comfort. The control methods are validated by experimental results. Compared with conventional fixed temperature settings, the present control methods effectively maintain the PMV value within the range of ± 0.5 and energy is saved more than 30% in this study.

The Digital Self-Tuning Control of Step a Down DC-DC Converter

A digital self-tuning control technique of DC-DC Buck converter is considered and thoroughly analyzed in this paper. The development of the small-signal model of the converter, which is the key of the control design presented in this work, is based on the state-space averaged (SSA) technique. Adaptive control has become a widely-used term in DC-DC conversion in recent years. A digital self-tuning Dahlin PID and a /Keviczky PID controller based on recursive least- squares estimation are developed and designed to be applied to the voltage mode control (VMC) approach operating in a continuous conduction mode (CCM). A comparative study of these two digital self-tuning controllers for step change in input voltage magnitude or output load is also carried out. The simulation results obtained using a Matlab SimPowerSystems toolbox to validate the effectiveness of the proposed strategies are also given and discussed extensively.

Digital Self-Tuning PID Control of Pressure Plant with Closed-Loop Optimization. Information Technology and Control

In this paper we propose a method for optimization of closed-loop parameters and continuous-time sampling period by digital self-tuning PID control of pressure plant. The quality of pressure plant control is experimentally compared between two modifications of digital self-tuning PID controllers. The results of adaptive pressure plant control show that the optimization of closed-loop parameters and sampling period provides significantly improved control performance.http://dx.doi.org/10.5755/j01.itc.40.3.628

Multivariable Self-Tuning Control of a Turbine Generator System

Results from a collaborative research and development program devoted to turbine generator (TG) control are described. Digital self-tuning excitation controllers were designed for a generator during the initial phase of the project, with the design subsequently extended to cover multivariable control of the TG, which is the topic of this paper. Simulations and tests on a laboratory-scale machine have been accomplished successfully and a prototype multivariable self-tuning controller has been built. A set of typical results is given, covering responses to fault conditions of the power system. The multivariable self-tuning controller is shown to have good potential for commercial use for the TG.

Decentralized control of nonlinear systems using selftuning controllers

This contribution presents a usage of Self-Tuning Controllers Simulink Library (STCSL) for control of the nonlinear systems using decentralized approach. The STCSL was created for design, simulation verification and especially real-time implementation of single-input/single-output (SISO) digital self-tuning controllers (STC). The proposed self-tuning controllers what are included in the library can be divided into three groups. The first group covers self-tuning PID controllers, the second group of described controllers is based on the polynomial approach and the third group contains the controllers derived on the other approaches (minimum variance control etc.). This paper is focused on decentralized control of multi-input/multi-output (MIMO) nonlinear systems in real time applications. The aim is to investigate simple discrete STC with respect to their usage in real-time decentralized control. A supervisory logic block is used in the control scheme to increase the stability of the recursive identification process by ensuring that just one of the controllers is in adaptive mode at a particular time. The logical supervisor is purposed as a simple and reliable technique for designing the supervisory system. The basic idea of the logical supervisor is based on assigning priorities to individual outputs of controlled multivariable system and on-line evaluation of criterions for each subsystem. The usage of this approach has been tested in real-time conditions for control of two-input/two-output (TITO) laboratory models. The STCLS is available free of charge at Internet site -http://www.utb.cz/stctool/.

Self-tuning control applied to turbogenerator AVRs

Results from a research and development programme devoted to turbogenerator control are described. Digital self-tuning controllers are covered, although fixed parameter controllers have also been considered. Simulations and tests on laboratory scale machines have been accomplished successfully, with a prototype self-tuning controller being manufactured. A set of typical results is given, covering responses to normal set point changes and fault dynamics. These new controllers are shown to have good potential for practical application.

Robust Self-Tuning PID Controller

New algorithm of digital self-tuning controller (STC PID) has been derived in this paper. The parameters of this controller are designed on the basis of modified Ziegler-Nichols criterion for digital control loops. The process is modelled as a controlled regression of the third order and its parameters estimated using the recursive least squares method (RLSM) with the LD decomposition and with directional forgetting is applied. The algorithm is suitable for automatic setting of digital PID controllers for control the deterministic processes or for adaptive control of stochastic processes.

A digital self-tuning tracking controller for permanent-magnet synchronous motors

A new digital self-tuning tracking controller for the permanent-magnet synchronous motor is presented, providing for either velocity or position trajectory tracking when both the electrical and mechanical parameters of the motor are unknown. A time-scale simplification leads to a discrete-time design model that is reduced order and that evolves in the slower mechanical time scale. A new piecewise-linear parameterization of the motor torque-angle characteristic functions and cogging allows for arbitrarily accurate identification of higher-order harmonics, without requiring more computation than traditional single-term sinusoidal descriptions. Experimental results demonstrate that good performance is achieved even with very poor initial parameter knowledge. >

Parallel Adaptive Control for Turbogenerator Systems

Abstract This paper describes the design and implementation of digital self-tuning controllers for turbogenerator AVR and governor systems. Practical aspects such as controller robustness, lack of. sufficient processing power, and algorithm mapping issues are discussed. A parallel architecture using Inmos transputers combined with an advanced VME bus-based measurement system provides a solutIOn to these problems and offers the control systems designer the opportunity to implement complex parallel algorithms. Simulation results, involving both SISO and multi-loop self-tuning regulators on a network of T800 transputers, are presented together with implementation results on a laboratory scale microalternator and comparisons with a fixed-gain AVR.

Development of a digital adaptive control system for PO 2 regulation in a membrane oxygenator

Regulation of gas exchange in artificial lungs (oxygenators) during cardiopulmonary bypass is normally achieved by manual control of the gas composition and flow in response to intermittent sampling of the arterial partial pressures of oxygen ( P a O 2) and carbon dioxide ( P a CO 2). Manual control often results in abnormal blood gases which have been implicated in patient morbidity as well as influencing perfusion safety. Fine control of P a O 2 and P a CO 2 may be achieved by a combination of an in-line blood gas monitoring system and a membrane type oxygenator which is automatically regulated. The overall dynamics of the oxygenation process and control system components are complex and have nonlinear, multivariable and time-varying characteristics. Consequently, an adaptive control system approach is necessary. The implementation of a digital self-tuning control regime for P a O 2 is described here. The controller is based on an explicit Linear Quadratic Gaussian (LQG) self-tuning control design which is presented using a polynomial equation approach. The controller performance was investigated in in vitro experiments. The self-tuner performed satisfactority with various sensor/oxygenator combinations for blood flow and temperature load disturbances. In contrast, a nonadaptive (proportional-integral, PI) type of control system was found to be unsuitable.

Comparison of Adaptive Control Algorithms of Multivariable Processes with Varying Delays

The report, deals with generalization, estimation and compari son of new algorithms of the digital self-tuning controllers CSTCD design for multivariable production processes with unknown and varying time delays in control channels, the value of which is taken into account in the law of control. The algorithms of STC design are developed for control systems design in chemical and petrochemical industries. They make it possible to design digital STC of optimal structure, to compensate for process’s unknown and varying time delays represented in implicit form, to estimate process delays included in explicit form in STC law of control, to realize multirate sampling of input and output coordinates in separate control loops of multidimensional system, to conduct adaptive tuning of digital STC parameters on the basis of identification in real time varying dynamic parameters of a process with closed control system. The developed algorithms of STC design can be used for linear and nonlinear processes, taking place in determinate and stochastic medium.

Predictor-based self-tuning control

Linear finite-memory output predictors updated in real-time appear to be a suitable internal representation of the system in a digital self-tuning controller. A new time-domain method of quadratic-optimum control synthesis for systems described by such predictors is presented. The synthesis covers both the servo problem and the regulation problem, including the program control (with preprogrammed command signal) and the feedforward from measurable external disturbances. Unlike the standard Riccati equation, the method leads to algorithms (or explicit formulae in low-order cases) which are numerically robust and therefore suitable for real-time computation using microprocessors with reduced wordlength.

Predictor-Based Self-Tuning Control

Linear finite-memory output predictors updated in real time appear to be a suitable internal representation of the system in a digital self-tuning controller. A new time-domain method of quadratic-optimum control synthesis for systems described by such predictors is presented. The synthesis covers both the servo problem and the regulation problem, including the program control (with preprogrammed command signal) and the feedforward from measurable external disturbances. Unlike the standard Riccati equation, the method leads to algorithms (or explicit formulae in low-order cases) which are numerically robust and therefore suitable for real-time computation using microprocessors with reduced word length.