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This paper presents a newly implemented self-tuning PID controller that uses a relay feedback identification using a recently designed relay shifting method to determine the mathematical model of the process and subsequently adjust the controller parameters. The controller is applicable to proportional and integrating systems and is even applicable to systems with transport delays if steady-state oscillation can be achieved in the relay control of the system. After briefly introducing the relay shifting method, the current paper describes the hardware (HW) and software (SW) of the proposed controller in detail. The relay feedback identification and control of a laboratory setup by an automatically tuned controller is demonstrated on a real laboratory device called "Hot air tunnel". The evaluation of the experiment and the characteristics of the controller are presented at the end of the paper. The advantage of the relay method is that it is not as computationally intensive as other identification methods. It can thus be implemented on more energy-efficient microcontrollers, which is very important nowadays. [ABSTRACT FROM AUTHOR]

This paper presents a newly implemented self-tuning PID controller that uses a relay feedback identification using a recently designed relay shifting method to determine the mathematical model of the process and subsequently adjust the controller parameters. The controller is applicable to proportional and integrating systems and is even applicable to systems with transport delays if steady-state oscillation can be achieved in the relay control of the system. After briefly introducing the relay shifting method, the current paper describes the hardware (HW) and software (SW) of the proposed controller in detail. The relay feedback identification and control of a laboratory setup by an automatically tuned controller is demonstrated on a real laboratory device called “Hot air tunnel”. The evaluation of the experiment and the characteristics of the controller are presented at the end of the paper. The advantage of the relay method is that it is not as computationally intensive as other identification methods. It can thus be implemented on more energy-efficient microcontrollers, which is very important nowadays.

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A de-loaded real power control strategy is proposed to decrease the real power output and increase the reactive power output of a grid-connected offshore wind farm in order to improve the voltage profile when the wind farm is subject to a grid fault. A simplified linear model of the wind farm is first derived and a fixed-gain proportional-integral (PI) real power controller is designed based on the pole-zero cancellation method. To improve the dynamic voltage response when the system is subject to a major disturbance such as a three-phase fault in the grid, a self-tuning controller based on particle swarm optimization (PSO) is proposed to adapt the PI controller gains based on the on-line measured system variables. Digital simulations using MATLAB/SIMULINK were performed on an offshore wind farm connected to the power grid in central Taiwan in order to validate the effectiveness of the proposed PSO controller. It is concluded from the simulation results that a better dynamic voltage response can be achieved by the proposed PSO self-tuning controller than the fixed-gain controller when the grid is subject to a three-phase fault. In addition, low voltage ride through (LVRT) requirements of the local utility can be met by the wind farm with the proposed power controller.

Left ventricular assist device (LVAD) recently has been used in advanced heart failure (HF), which supports a failing heart to meet blood circulation demand of the body. However, the pumping power of LVADs is typically set as a constant and cannot be freely adjusted to incorporate blood need from resting or mild exercise such as walking stairs. To promote the adoption of LVADs in clinical use as a long-term treatment option, a feedback controller is needed to regulate automatically the pumping power to support a time-varying blood demand, according to different physical activities. However, the tuning of pumping power induces suction, which will collapse the heart and cause sudden death. It is essential to consider suction when developing control strategy to adjust the pumping power. Further, hemodynamic of a failing heart exhibits variability, due to patient-to-patient heterogeneity and inherent stochastic nature of the heart. Such variability poses challenges for controller design. In this work, we develop a feedback controller to adjust the pumping power of an LVAD without inducing suction, while incorporating variability in hemodynamic. To efficiently quantify variability, the generalized polynomial chaos (gPC) theory is used to design a robust self-tuning controller. The efficiency of our control algorithm is illustrated with three case scenarios, each representing a specific change in physical activity of HF patients. [ABSTRACT FROM AUTHOR]

Recently, detailed investigations of various 'run-to-run' (R2R) control schemes for semiconductor manufacturing have been conducted. However, a pure R2R control scheme cannot ensure sufficient control quality when the process suddenly undergoes a larger change (herein, a larger change is defined when the output exceeds 2σε). In many semiconductor processes, sudden changes in the situation are often generated through the operation of different devices during the same process or changes in the control environment (e.g. process ageing or the influence of chemical concentrations). We propose an integrated R2R control system (IRCS) to alleviate this problem; the system includes an on-line experiment, R2R double exponentially weighted moving average (dEWMA) control, R2R triple EWMA (triEWMA) control and R2R self-tuning control. The IRCS initially employs a warning threshold to evaluate the process changes. When the process is changed, the system then uses the 2k centre points design to amend the process model and selects a suitable R2R control scheme to execute the process control. We used polysilicon gate etching and chemical mechanical planarisation processes as case studies for verifying the proposed method. Based on the analysis results, the IRCS can reduce problems due to process changes. The IRCS is also an improvement in that R2R EWMA-like control does not deal with the problem of process changes in more complicated models. [ABSTRACT FROM AUTHOR]

Linearised model for frequency control of an islanding system comprising an equivalent synchronous generator and an equivalent doubly fed induction generator (DFIG) is derived. Small signal stability analysis is performed to determine the stable regions for DFIG supplementary droop controller under various wind velocities. In order to achieve better dynamic frequency response under different system parameters, the DFIG droop gain is adapted in real‐time using a self‐tuning controller designed based on particle swarm optimisation technique. In order to validate the results from small signal analysis and to demonstrate the effectiveness of the proposed self‐tuning frequency controller, digital simulations using MATLAB/SIMULINK are performed on a local power system in central Taiwan. It is concluded from the simulation results that the proposed self‐tuning frequency controller offer better dynamic frequency response than the fixed‐gain droop controller. [ABSTRACT FROM AUTHOR]

Copyright of FME Transactions is the property of University of Belgrade, Faculty of Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a partial update Kalman Filter (PUKF) is presented for the real-time parameter estimation of a dc–dc switch mode power converter. The proposed estimation algorithm is based on a novel combination between the classical Kalman filter (KF) and an M-Max partial adaptive filtering technique. The proposed PUKF offers a significant reduction in computational effort compared to the conventional implementation of the KF, with 50% less arithmetic operations. Furthermore, the PUKF retains comparable overall performance to the classical KF. To demonstrate an efficient and cost effective explicit self-tuning controller, the proposed estimation algorithm (PUKF) is embedded with a Bányász/Keviczky proportional, integral, derivative (PID) controller to generate a new computationally light self-tuning controller. Experimental and simulation results clearly show the superior dynamic performance of the explicit self-tuning control system compared to a conventional pole placement design based on a precalculated average model. [ABSTRACT FROM AUTHOR]

To have long operating life and to withstand high temperature and pressure of steam, now a days, the standard material used in power industry is the P91 Grade steel. It has high creep resistance. In a fabrication process when arc welding is used for joining this material the high energy density of arc heat makes strong influence on its microstructure. Moreover, the prospect of hydrogen cracking increases if pre-heating is not properly done. Therefore, for long term quality of P91 Grade steel, detailed heat treatment operation must be performed before and after the welding. For heat treatment operations, the induction heating method is ideally suitable. This article proposes to use air-cooled system to improve the efficiency of power delivery to the requisite area of pipe in high power applications. [ABSTRACT FROM AUTHOR]

The rapid population growth of cities in developing countries (DC) make difficult to distribute the available potable water (PW) with equality. The distribution problem arises from an insufficient amount of PW and because cities water distribution systems (WDS) are not efficient. The novelty of this paper is a self-tuning controller (STC) proposed to manage, along the day, the pressure of water through the nodes of a WDS. It means, pressure management (PM) is proposed to control water levels (WLs) in householders tanks (HTs). The objective is to satisfy with equality the PW demand at different zones of a city forcing the flow of water by managing the pressure. The proposed STC performance is tested on the digital simulator developed to characterize the hydraulic operation of a WDS. The dynamic behaviour of the WDS is determined by the variation of the WL in the tanks of the WDS when water is supplied or extracted from them. The WDS of Mexico City is analysed and the proposed STC is applied to a simplified WDS. The results allow to conclude that the proposed STC could become a supporting tool for the decision making of WDS operators. [ABSTRACT FROM AUTHOR]

In this study, we investigate the control performance of an adaptive controller using a multilayer quaternion neural network. The control system is a self-tuning controller, the control parameters of which are tuned online by the quaternion neural network to track plant output to follow the desired output generated by a reference model. A proportional-integral-derivative (PID) controller is used as a conventional controller, the parameters of which are tuned by the quaternion neural network. Computational experiments to control a single-input single-output (SISO) discrete-time nonlinear plant are conducted to evaluate the capability and characteristics of the quaternion-neural-networkbased self-tuning PID controller. Experimental results show the feasibility and effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

In this study, we propose the self-tuning interval type-2 fuzzy proportional derivative plus proportional integral (STIT2FPD+PI) controller, which consists of two fuzzy processors. The first is the IT2FPD+PI controller, which is a parallel combination of the IT2FPD controller and the IT2FPI controller and it is used as the main controller. The second is the tuning mechanism, which is a type-1 fuzzy logic system (T1FLS) that used as parameters tuning. The tuning mechanism generates the updating factors that update the output scaling factors (SFs), the output membership functions (MFs) and the degree of uncertainty for the input MFs. The proposed STIT2FPD+PI controller is implemented practically based on a microcontroller for controlling the overhead crane system. The test is established using the hardware-in-the-loop (HIL) simulation. The practical results show that the proposed STIT2FPD+PI controller improves significantly the performance over a wide range of system uncertainties. [ABSTRACT FROM AUTHOR]

In the traditional run-to-run (R2R) control scheme, the controller provides the feedback strategy, enabling each run of process output to remain as close to the target as possible. However, in practice this process encounters bottlenecks if all the runs are started with a control parameter adjustment, as this will increase process variation in the stable process. In this paper, an improved R2R control scheme is developed to add an adjustments limitation technique to both the EWMA controller and the self-tuning controller. From the simulation, analysis of the results shows that the proposed procedure can effectively reduce the frequency of controller adjustments, without affecting the capability of the original controller. Therefore, in practical applications, the proposed control framework can be used to prevent the problem of process over-control.

This paper presents a lateral driver model for vehicle–driver closed-loop simulation at the limits of handling. An appropriate driver model can be used to evaluate the performance of vehicle chassis control systems via computer simulations before vehicle tests which incurs expenses especially at the limits of handling. The driver model consists of two parts. The first part is an upper-level controller employing force-based approach to reduce the number of unknown vehicle parameters. The feedforward part of the upper controller has been designed by using the centre of percussion. The feedback part aims to minimise ‘tangential error’, defined as the sum of body slip angle and yaw error, to match vehicle direction and road heading angle. The part is designed to regenerate an appropriate skid motion similar to that of a professional driver at the limits. The second part is a lower-level controller which converts the desired front lateral force to steering wheel angle. The lower-level controller also consists of feedforward and feedback parts. A two-degree-of-freedom bicycle model-based feedforward part provides nominal steering wheel angle, and the feedback part aims to eliminate unmodelled error. The performance of the lateral driver model has been investigated via computer simulations. It has been shown that the steering behaviours of the proposed driver model are quite close to those of a professional driver at the limits. Compared with the previously developed lateral driver models, the proposed lateral driver model shows good tracking performance at the limits of handling. [ABSTRACT FROM PUBLISHER]

This paper investigates the performance of an adaptive controller using a multi-layer quantum neural network (QNN) comprising qubit-inspired neurons as information processing units. The control system is a self-tuning controller whose control parameters are tuned online by the QNN to track plant output relative to the desired plant output generated by a reference model. A proportional-integral-derivative (PID) controller is utilized as a conventional controller, with its parameters tuned by the QNN. Computational experiments to control a single-input single-output discrete-time non-linear plant are conducted to evaluate the capability and characteristics of the quantum neural self-tuning controller. The experiment results demonstrate the feasibility and effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Copyright of Automatika: Journal for Control, Measurement, Electronics, Computing & Communications is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The aim of the given paper is development of a minimum variance control (MVC) approach for a closed-loopdiscrete-time linear time-invariant (LTI) system when the parameters of a dynamic system as well as that of a controller are not known and ought to be estimated by processing observations in the case of additive Gaussian noise on the output with contaminating outliers uniformly spread in it. Afterwards, the current value of the control signal is found in each operation, and it is used to generate the output of the system. The results of numerical simulation by computer are presented and discussed here, too.

An integrated guidance and control system has been developed to enable underactuated marine surface vessels to operate autonomously and yield robust tracking performance in spite of significant external disturbances and modeling imprecision. A nonlinear ship model, accounting for all six degrees-of-freedom of the ship, has been used as a test bed to assess the performance of the proposed scheme. The controller combines the advantages of the variable structure systems (VSS) theory with the self-tuning fuzzy logic scheme. It does not require an accurate dynamic model of the ship or the construction of a rule-based expert system. Its asymptotic stability is ensured by knowing the upper bounds on modeling imprecision and external disturbances and by forcing the tuning parameters to satisfy the sliding conditions. The guidance system is based on the concepts of the variable radius line-of-sight (LOS) and the acceptance circle around the waypoints. The current system varies the LOS radius exponentially with the cross track error in order to achieve a fast convergence rate of the ship to its desired trajectory. The simulation results demonstrate the robust tracking characteristic of the integrated guidance and control system in spite of significant modeling uncertainties and environmental disturbances. [ABSTRACT FROM AUTHOR]

In the previous papers (Pupeikis, 2000; Genov et al., 2006; Atanasov and Pupeikis, 2009), a direct approach for estimating the parameters of a discrete-time linear time-invariant (LTI) dynamic system, acting in a closed-loop in the case of additive noise with contaminating outliers uniformly spread in it, is presented. It is assumed there that the parameters of the LQG (Linear Quadratic Gaussian) controller are unknown, as well as known beforehand, too. The aim of the given paper is development of a minimum variance control (MVC) approach for a closed-loop discrete-time linear dynamic system when slowly or suddenly time-varying coefficients of the transfer function of such a system as well as that of a minimum variance (MV) controller are not known and ought to be estimated. The recursive parametric identification of an open-loop system and determination of the coefficients of the MV controller are performed in each current operation by processing observations in the case of additive noise at the output with contaminating outliers uniformly spread in it. The robust recursive technique, based on the S-algorithm, with a version of Shweppe's GM-estimator and with discounting previous data, used in the estimation, by introducing a constant as well as time-varying forgetting factors in the abovementioned estimator, is applied here in the calculation of estimates of the parameters of a dynamic system. Then, the recursive parameter estimates are used in each current iteration to determine unknown parameters of the MV controller. Afterwards, the current value of the MV control signal is found in each operation, and it is used to generate the output of the system, too. The results of numerical simulation by computer are presented and discussed. [ABSTRACT FROM AUTHOR]

A de-loaded real power control strategy is proposed to decrease the real power output and increase the reactive power output of a grid-connected offshore wind farm in order to improve the voltage profile when the wind farm is subject to a grid fault. A simplified linear model of the wind farm is first derived and a fixed-gain proportional-integral (PI) real power controller is designed based on the pole-zero cancellation method. To improve the dynamic voltage response when the system is subject to a major disturbance such as a three-phase fault in the grid, a self-tuning controller based on particle swarm optimization (PSO) is proposed to adapt the PI controller gains based on the on-line measured system variables. Digital simulations using MATLAB/SIMULINK were performed on an offshore wind farm connected to the power grid in central Taiwan in order to validate the effectiveness of the proposed PSO controller. It is concluded from the simulation results that a better dynamic voltage response can be achieved by the proposed PSO self-tuning controller than the fixed-gain controller when the grid is subject to a three-phase fault. In addition, low voltage ride through (LVRT) requirements of the local utility can be met by the wind farm with the proposed power controller. [ABSTRACT FROM AUTHOR]

In this paper a predictive control strategy is applied to a periodic-review dynamic inventory system with deteriorating items. Given the current inventory level, we determine the optimal production rates to be implemented at the beginning of each of the following periods over the control horizon. The effectiveness of this approach is the use of future information of the inventory target level and the desired production rate, which are available, along the fixed horizon. The deterioration coefficient may be known or unknown and both cases are considered. In the case where it is unknown, the self-tuning predictive control is applied. The proposed control algorithms are illustrated by simulations. [ABSTRACT FROM AUTHOR]

This paper deals with multivariable self-tuning temperature control of a thermo-analyser. The decoupling method is used and the parameters of two independent controllers are derived using the deadbeat approach with reference signal tracking. The recursive least-squares method with LD decomposition and directional forgetting is used to estimate the parameters of the process model. This multivariable controller has been designed specially for technological processes where process output variables without overshoot are desired and reference signals are changed with steps and linear increasing. [ABSTRACT FROM AUTHOR]