Development of seam tracking system with ultrasonic sensor using self-tuning fuzzy control

One kind of the SAW seam tracking system with contactless ultrasonic sensor is presented in this paper. The new contactless ultrasonic sensor for seam tracking and the working principle of the seam tracking with the sensor are introduced. Based on the experiments, the optimal values of the fuzzy control parameters α and k 3 are defined by means of the off line adjusting method. Because the self tuning fuzzy control is adopted in the seam tracking system, the overshoot of the system is restrained, the steady state error is reduced, and the system's response speed is improved effectively. The results of the SAW seam tracking experiments show that this system's tracking accuracy is up to ±0.5 mm and the system can satisfy the requirements of the engineering application.

Self-tuning Information Fusion Kalman Predictor Weighted by Diagonal Matrices and Its Convergence Analysis

为有未知噪音统计的 multisensor 系统，使用现代时间系列分析方法，基于革新建模的动人的一般水准(麻省)的联机鉴定，并且基于为关联功能的矩阵方程的解决方案，噪音变化的评估者被获得，并且在线性最小的变化下面由斜矩阵加权的最佳的信息熔化标准，一个自我调节的信息熔化 Kalman 预言者被介绍，它认识到自我调节的 dec 基于动态错误系统，一个新集中分析方法为自我调节的 fuser 被介绍。在一条认识的集中的一个新概念被介绍，它是比有概率一的集中弱的。如果 MA 革新模型的参数评价是一致的，那么，自我调节的熔化 Kalman 预言者将在一条认识收敛到最佳的熔化 Kalman 预言者，这严格地被证明，或与概率一，以便它有 asymptotic optimality。它能减少计算负担，并且对实时应用合适。为追踪系统的一个目标的一个模拟例子显示出它的有效性。

Fuzzy self-tuning PID control of the operation temperatures in a two-staged membrane separation process

A two-staged membrane separation process for hydrogen recovery from refinery gases is introduced. The principle of the gas membrane separation process and the influence of the operation temperatures are analyzed. As the conventional PID controller is difficult to make the operation temperatures steady, a fuzzy self-tuning PID control algorithm is proposed. The application shows that the algorithm is effective, the operation temperatures of both stages can be controlled steadily, and the operation flexibility and adaptability of the hydrogen recovery unit are enhanced with safety. This study lays a foundation to optimize the control of the membrane separation process and thus ensure the membrane performance.

A Real Time Self-Tuning Motion Controller for Mobile Robot Systems

This paper proposes an intelligent controller for motion control of robotic systems to obtain high precision tracking without the need for a real-time trial and error method.In addition, a new self-tuning algorithm has been developed based on both the ant colony algorithm and a fuzzy system for real-time tuning of controller parameters. Simulations and experiments using a real robot have been addressed to demonstrate the success of the proposed controller and validate the theoretical analysis. Obtained results confirm that the proposed controller ensures robust performance in the presence of disturbances and parametric uncertainties without the need for adjustment of control law parameters by a trial and error method.

Position Control of a Flexible Manipulator Using a New Nonlinear Self-Tuning PID Controller

In this paper, a new nonlinear self-tuning PID controller(NSPIDC) is proposed to control the joint position and link deflection of a flexible-link manipulator(FLM) while it is subjected to carry different payloads. Since, payload is a critical parameter of the FLM whose variation greatly influences the controller performance. The proposed controller guarantees stability under change in payload by attenuating the non-modeled higher order dynamics using a new nonlinear autoregressive moving average with exogenous-input(NARMAX) model of the FLM. The parameters of the FLM are identified on-line using recursive least square(RLS) algorithm and using minimum variance control(MVC) laws the control parameters are updated in real-time. This proposed NSPID controller has been implemented in real-time on an experimental set-up. The joint tracking and link deflection performances of the proposed adaptive controller are compared with that of a popular direct adaptive controller(DAC). From the obtained results, it is confirmed that the proposed controller exhibits improved performance over the DAC both in terms of accurate position tracking and quick damping of link deflections when subjected to variable payloads.

Self-adaptive PID controller of microwave drying rotary device tuning on-line by genetic algorithms

The control design, based on self-adaptive PID with genetic algorithms(GA) tuning on-line was investigated, for the temperature control of industrial microwave drying rotary device with the multi-layer(IMDRDWM) and with multivariable nonlinear interaction of microwave and materials. The conventional PID control strategy incorporated with optimization GA was put forward to maintain the optimum drying temperature in order to keep the moisture content below 1%, whose adaptation ability included the cost function of optimization GA according to the output change. Simulations on five different industrial process models and practical temperature process control system for selenium-enriched slag drying intensively by using IMDRDWM were carried out systematically, indicating the reliability and effectiveness of control design. The parameters of proposed control design are all on-line implemented without iterative predictive calculations, and the closed-loop system stability is guaranteed, which makes the developed scheme simpler in its synthesis and application, providing the practical guidelines for the control implementation and the parameter design.

SELF-TUNING WEIGHTED MEASUREMENT FUSION WHITE NOISE DECONVOLUTION ESTIMATOR

For the multi-sensor linear discrete time-invariant stochastic systems with correlated measurement noises and unknown noise statistics,an on-line noise statistics estimator is obtained using the correlation method.Substituting it into the optimal weighted fusion steady-state white noise deconvolution estimator based on the Kalman filtering,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the Dynamic Error System Analysis(DESA) method,it proved that the self-tuning fusion white noise deconvolution estimator converges to the steady-state optimal fusion white noise deconvolution estimator in a realization.Therefore,it has the asymptotically global optimality.A simulation example for the tracking system with 3 sensors and the Bernoulli-Gaussian input white noise shows its effectiveness.

A Design of a PID Self-Tuning Controller Using LabVIEW

In this paper a trial has been made to design a simple self-tuning LabVIEW-based PID controller. The controller uses an open-loop relay test, calculates the tuned parameters in an open loop mode of operation before it updates controller parameters and runs the process as a closed-loop system. The controller reacts on a persistent offset error value as a result of load disturbance or a set point change. Practical results show that such a controller may be recommended to control a variety of industrial processes. A GUI was developed to facilitate control-mode selection, the setting of controller parameters, and the display of control system variables. GUI makes it possible to put the controller in manual or self-tuning mode.

Self-tuning weighted measurement fusion Kalman filter and its convergence

For multisensor systems,when the model parameters and the noise variances are unknown,the consistent fused estimators of the model parameters and noise variances are obtained,based on the system identification algorithm,correlation method and least squares fusion criterion.Substituting these consistent estimators into the optimal weighted measurement fusion Kalman filter,a self-tuning weighted measurement fusion Kalman filter is presented.Using the dynamic error system analysis （DESA） method,the convergence of the self-tuning weighted measurement fusion Kalman filter is proved,i.e.,the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a realization.Therefore,the self-tuning weighted measurement fusion Kalman filter has asymptotic global optimality.One simulation example for a 4-sensor target tracking system verifies its effectiveness.

Design and simulation about a self-Tuning fuzzy-PID controller

Fuzzy logic has attracted the attention of structural control engineers during the last few years,because fuzzy logic can handle nonlinearities,uncertainties,and heuristic knowledge effectively and easily.In this paper,a self-Tuning fuzzy-PID control method which used the technology of the fuzzy control and PID control unified is presented.These techniques can visualize the results and processes for structure stress.These techniques will also provide convenience for engineers and users,and have high practical values.The MATLAB simulation result shows that the system precision and the efficiency are very high and the static error is small,and robustness was also validated.

Stabilization of CSTR w ith Self-tuning Sliding Mode Controller Using T-S Fuzzy Linearization

A self-tuning reaching law based sliding mode control(SMC)theory is proposed to stabilize the nonlinear continuous stirred tank reactor(CSTR).T-S fuzzy logic is used to build a global fuzzy state-space linear model.Combing the traits of SMC and CSTR,three fuzzy rules can meet the requirements of controlled system.The self-tuning switch control law which can drive the state variables to the sliding surface as soon as possible is designed to ensure the robustness of uncertain fuzzy system.Lyapunov equation is applied to proving the stability of the sliding surface.The simulations show that the proposed approach can achieve desired performance with less chattering problem.

Neural Model-Based Self-Tuning PID Strategy Applied to PEMFC

This paper illustrates the benefits of a self-tuning PID strategy applied to a proton exchange membrane fuel cell system. Controller parameters are updated on-line, at each sampling time, based on an instantaneous linearization of an artificial neural network model of the process and a General Minimum Variance control law. The self-tuning PID scheme allows managing nonlinear behaviors of the system while avoiding heavy computations. The applicability, efficiency and robustness of the proposed control strategy are experimentally confirmed using varying control scenarios. In this aim, the original built-in controller is overridden and the self-tuning PID controller is implemented externally and executed on-line. Experimental results show good performance in setpoint tracking accuracy and robustness against plant/model mismatch. The proposed strategy appears to be a promising alternative to heavy computation nonlinear control strategies and not optimal linear control strategies.

SELF-TUNING MEASUREMENT FUSION KALMAN FILTER WITH CORRELATED MEASUREMENT NOISES

For the multisensor system with correlated measurement noises and unknown noise statistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances is obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented, based on the Riccati equation. By the Dynamic Error System Analysis (DESA) method, it rigorously proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion steady-state Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows that the presented self-tuning measurement fusion Kalman fuser converges to the optimal steady-state measurement fusion Kalman fuser.

Self-tuning measurement fusion white noise deconvolution estimator with correlated noises

For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation method.Substituting it into the steady-state Riccati equation,the self-tuning Riccati equation is obtained.Using the Kalman filtering method,based on the self-tuning Riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the dynamic error system analysis（DESA） method,it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization,so that it has the asymptotic global optimality.A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.

Self-Tuning Control for MIMO Network Systems

The advances in MIMO systems and networking technologies introduced a revolution in recent times, especially in wireless and wired multi-cast (multi-point-to-multi-point) transmission field. In this work, the distributed versions of self-tuning proportional integral plus derivative (SPID) controller and self-tuning proportional plus integral (SPI) controller are described. An explicit rate feedback mechanism is used to design a controller for regulating the source rates in wireless and wired multi-cast networks. The control parameters of the SPID and SPI controllers are determined to ensure the stability of the control loop. Simulations are carried out with wireless and wired multi-cast models, to evaluate the performance of the SPID and SPI controllers and the ensuing results show that SPID scheme yields better performance than SPI scheme;however, it requires more computing time and central processing unit (CPU) resources.

对称NARMA-U模型及其神经网络自校正控制器

带预测误差补偿的改进NARMA-L2模型是由NARMA模型在自适应滤波动态工作点处一阶泰勒展开逼近得出的,在自适应滤波动态工作点处二阶泰勒展开逼近可得到对称NARMA-U模型,采用BP神经网络辨识对称NARMA-U模型参数,提出一广义目标函数,基于对称NARMA-U模型的非线性系统的神经网络自校正控制器,应用直接极小化指标函数自适应优化算法对BP神经网络连接权重值进行在线学习。仿真研究表明算法的响应优良。

Self-Tuning Control Techniques for Wind Turbine and Hydroelectric Plant Systems

The interest on the use of renewable energy resources is increasing, especially towards wind and hydro powers, which should be efficiently converted into electric energy via suitable technology tools. To this aim, self-tuning control techniques represent viable strategies that can be employed for this purpose, due to the features of these nonlinear dynamic processes working over a wide range of operating conditions, driven by stochastic inputs, excitations and disturbances. Some of the considered methods were already verified on wind turbine systems, and important advantages may thus derive from the appropriate implementation of the same control schemes for hydroelectric plants. This represents the key point of the work, which provides some guidelines on the design and the application of these control strategies to these energy conversion systems. In fact, it seems that investigations related with both wind and hydraulic energies present a reduced number of common aspects, thus leading to little exchange and share of possible common points. This consideration is particularly valid with reference to the more established wind area when compared to hydroelectric systems. In this way, this work recalls the models of wind turbine and hydroelectric system, and investigates the application of different control solutions. Another important point of this investigation regards the analysis of the exploited benchmark models, their control objectives, and the development of the control solutions. The working conditions of these energy conversion systems will also be taken into account in order to highlight the reliability and robustness characteristics of the developed control strategies, especially interesting for remote and relatively inaccessible location of many installations.

Development of an Enhanced Self-Tuning RBF-PID Controller for Achieving Higher Energy-Efficient Process Control

Proportional, integral and derivative (PID) control strategy has been widely applied in heating systems in decades. To improve the accuracy and the robustness of PID control, self-tuning radial-basis-function neural network PID (RBF-PID) is developed and used. Even though being popular, during the control process both of PID and RBF-PID control strategy are inadequate in achieving simultaneous high energy-efficiency and good control accuracy. To address this problem, in this paper we develop and report an enhanced self-tuning radial-basis-function neural network PID (e-RBF-PID) controller. To identify the superiority of e-RBF-PID, following works are conducted and reported in this paper. Firstly, four controllers, i.e., on-off, PID, RBF-PID and e-RBF-PID are designed. Secondly, in order to test the performance of the e-RBF-PID controller, an experimental water heating system is constructed for being controlled. Finally, the energy consumption for the four controllers under the three control scenarios is investigated through experiments. The experimental results indicate that in the three scenarios, the developed e-RBF-PID controller outperforms on-off controller as having higher accuracy. Compared to the PID controller, the e-RBF-PID controller has higher speed in control, and the experimental results show that settling time savings is between 12.6% - 49.0%. Most importantly, less control energy consumption is obtained if using the e-RBF-PID controller. It is found that up to 28.5% energy consumption can be saved. Therefore, it is concluded that the proposed e-RBF-PID is capable of enhancing energy efficiency during control process.

APPLICATION OF FUZZY CONTROL METHOD WITH SELF-TUNING FACTOR IN JIGGER'S DISCHARGING

Adopting the strategy of fuzzy control with self tuning factor within whole universe of discourse, a kind of fuzzy control method for jigger discharging is put forward. This method has many advantages over the conventional PID controller in terms of response speed, stability and robustness. It is effective to restrain the jig bed from over thick or empty, and the stability of the bed is markedly improved. The good results are obtained in factory tests.