Self-tuning Of Parameters Research Articles

A Self-Tuning Power System Stabilizer Design

The traditional power system stabilizer (PSS) usually configures parameters in off-line circumstances and can’t adapt to the variations of power system operating modes well. Based on the prony algorithm, this paper presents a parameter self-tuning PSS. After identifying the system reduced model, it recognizes the real-time low frequency oscillation characteristics of system based on the online prony and uses the dominant mode frequency to complete self-tuning parameters of PSS. The simulation results in IEEE 4-11 system show that the self-tuning PSS based on prony algorithm can adapt to the variations of power system operating modes and restrain low frequency oscillation system better, then improve the stability of the system.

BP-PID Control for Electro-hydraulic Proportional Position Servo System

The electro-hydraulic proportional position servo system is a kind of highly non-linear and time-varying system as well as parameter uncertainties. It is often difficult to achieve satisfactory control results with the traditional control methods. Considering that the adaptive neural network PID control method has the characteristics of online learning and adjustment PID control parameters adaptively, this paper puts forward to adopt BP-PID control method for the position servo control of electro-hydraulic proportional system. An online adaptive BP-PID controller is designed. The simulation model of the system is established based on Simulink platform. The step response and Sinusoidal tracking characteristics are researched for electro-hydraulic proportional position servo system by using traditional PID and BP-PID control algorithms through simulations and experiments. The results show that BP-PID has a strong ability of online learning and self-tuning PID parameters. Compared with the traditional PID control method, the BP-PID control has strong self-adaptability and robustness, and can significantly improve the step response speed and tracking control precision.

The Research of Controlling Constant Temperature for Semiconductor Laser Diodes

This paper introduces two types of precise temperature controllers for high power semiconductor laser diodes. We apply a category of self-tuning parameter fuzzy-PID controller，and a kind of neuronal network optimized rules fuzzy-PID controller to realize stable temperature controlling. we simulate these two methods by Matlab. The result proves that the new method has more superiority than traditional means, such as PID controlling, etc. And further improvement on constant temperature control will inevitably promote the reliability of LD.

Dynamic Modeling and Fuzzy Self-Tuning Disturbance Decoupling Control for a 3-DOF Serial-Parallel Hybrid Humanoid Arm

A unique fuzzy self-tuning disturbance decoupling controller (FSDDC) is designed for a serial-parallel hybrid humanoid arm (HHA) to implement the throwing trajectory-tracking mission. Firstly, the dynamic model of the HHA is established and the input signal of the throwing process is obtained by studying the throwing process of human's arm. Secondly, the FSDDC, incorporating the disturbance decoupling controller (DDC) and the fuzzy logic controller (FLC), is designed to ensure trajectory tracking of the HHA in the presence of uncertainties and disturbances. With the FSDDC method, the HHA system can be decoupled by actively estimating and rejecting the effects of both the internal plant dynamics and external disturbances. The self-tuning parameters are adapted online to improve the performance of the FSDDC; thus, it does not require detailed system parameters of the presented FSDDC. Finally, the controller introduced is compared with a PD controller which is commonly used for the robot manipulators control in industry. The effectiveness of the designed FSDDC is illustrated by simulations.

New Algorithm for the Smoothing Speed Control of Induction Motor in Electric Car based on Self-Tuning Parameter PID-Fuzzy Logic

Driving system of electric car for low speed has a performance of controller that is not easily set up on large span so it does not give a comfort to passengers. The study has been tested in the bumpy road conditions, by providing disturbances in the motor load, it is to describe the condition of the road. To improve the system performance, the speed and torque controller was applied using Field Oriented Control (FOC) method. In this method, On-Line Proportional Integral Derivative Fuzzy Logic Controller (PID-FLC) is used to give dynamic response to the change of speed and maximum torque on the electric car and this results the smooth movement on every change of car performance both in fast and slow movement when breaking action is taken. Optimization of membership functions in Fuzzy PID controller is required to obtain a new PID parameter values which is done in autotuning in any changes of the input or disturbance. PID parameter tuning in this case using the Ziegler-Nichols method based on frequency response. The mechanism is done by adjusting the PID parameters and the strengthening of the system output. The test results show that the controller Fuzzy Self-Tuning PID appropriate for Electric cars because they have a good response about 0.85% overshoot at to changes in speed and braking of electric cars.

Effective PID Parameters Self-Tuning Rule Based on Internal Model Control

The methods of self-tuning PID parameters are widely used in industry process control. This paper elaborated the principle and design of internal model control, and it is used for PID parameters self-tuning. By simulation, for a time-delay inertial system, the step response curses are obtained with different methods of self-tuning PID parameters. Compared and analyzed the curses, it shows that the present method of self-tuning IMC-PID parameters has a good performance in the time-delay inertial system. It has great potential for practical applications.

Application of PID Control Based on RBF Neural Network in Electro-Hydraulic Servo System for Steel Strip Deviation

The working principle of the electrical-hydraulic servo system for steel strip deviation is introduced. The mathematical model of the system is established, and the performance indexes in time domain are analyzed with MATLAB. Aiming at the steel strip deviation, an improved PID control method based on RBF neural network is proposed. According to Jacobian information identification of RBF neural network combined with incremental PID algorithm, the self-tuning of parameters is implemented so that the performance of the system can achieve the designed requirements. Compared with traditional PID control, the simulation results show RBF- PID control has better dynamic performance and stabilization.

Study on VAV Temperature Fuzzy Controllers with Self-Tuning Parameters and Variable Universe

VAV air-conditioning system has the characters of nonlinear and non-stationary under the influence of many factors, so it is hard to tune the parameters of the fuzzy control system. According to the structure and operation principle of VAV air-conditioning system, the monitoring system has been developed. The conventional fuzzy control algorithm, temperature self-tuning parameters fuzzy control algorithm and variable universe adaptive fuzzy control algorithm were researched in the system. The relationship between the scale factors and several main performance indexes of the system's respond curve like overshoot, steady-state error and transient time has been analyzed. The experiments results indicate that the variable universe fuzzy control algorithm can improve the performance of the fuzzy controlle, the combination property index decreases over 17%. The algorithm is feasible to use it on line since it is simple and need few system resources.

Start Control of Ultrasonic Motor Based on Neural Network

It is very hard for the traveling wave ultrasonic motor to start directly with high speed, because of their special running mechanism and the unique features. To solve this problem and realize the uitrasonic motor’s high-speed start control, speed controller with on-line self-tuning parameters is designed. Neural network is used to realize the online adjustment of controller’s parameters, to agree with the different starting request of different speed references, and make best use of motor’s ability. The experiments indicated that the motor start fast and accurately, the control algorithm is effective and reliable.

Self-Tuning for Maximized Lifetime Energy-Efficiency in the Presence of Circuit Aging

This paper presents an integrated framework, together with control policies, for optimizing dynamic control of self-tuning parameters of a digital system over its lifetime in the presence of circuit aging. A variety of self-tuning parameters such as supply voltage, operating clock frequency, and dynamic cooling are considered, and jointly optimized using efficient algorithms described in this paper. Our optimized self-tuning approach satisfies performance constraints at all times, and maximizes a lifetime computational power efficiency (LCPE) metric, which is defined as the total number of clock cycles achieved over lifetime divided by the total energy consumed over lifetime. We present three control policies: 1) progressive-worst-case-aging (PWCA), which assumes worst-case aging at all times; 2) progressive-on-state-aging (POSA), which estimates aging by tracking active/sleep modes, and then assumes worst-case aging in active mode and long recovery effects in sleep mode; and 3) progressive-real-time-aging-assisted (PRTA), which acquires real-time information and initiates optimized control actions. Various flavors of these control policies for systems with dynamic voltage and frequency scaling (DVFS) are also analyzed. Simulation results on benchmark circuits, using aging models validated by 45 nm measurements, demonstrate the effectiveness and practicality of our approach in significantly improving LCPE and/or lifetime compared to traditional one-time worst-case guardbanding. We also derive system design guidelines to maximize self-tuning benefits.

MIMO adaptive fuzzy terminal sliding-mode controller for robotic manipulators

A new design approach of a multiple-input–multiple-output (MIMO) adaptive fuzzy terminal sliding-mode controller (AFTSMC) for robotic manipulators is described in this article. A terminal sliding-mode controller (TSMC) can drive system tracking error to converge to zero in finite time. The AFTSMC, incorporating the fuzzy logic controller (FLC), the TSMC, and an adaptive scheme, is designed to retain the advantages of the TSMC while reducing the chattering. The adaptive law is designed on the basis of the Lyapunov stability criterion. The self-tuning parameters are adapted online to improve the performance of the fuzzy terminal sliding-mode controller (FTSMC). Thus, it does not require detailed system parameters for the presented AFTSMC. The simulation results demonstrate that the MIMO AFTSMC can provide a reasonable tracking performance.

Structure Rotation and Pull-in Voltage Control of MEMS Lateral Comb Resonators Under Fault Conditions

Lateral comb resonators (LCRs) theoretically possess a symmetric structure; therefore, the electrostatic force is effective only along the axis of displacement and is neutralized elsewhere. This force is not a function of the distance between the stationary and the moving (shuttle) plates in the direction of the movement. However, manufacturing imperfections and fault conditions make the device operate asymmetrically and cause rotation of the shuttle structure in a lateral gap-closing direction, putting the device in an unstable region of operation (pull-in voltage limit). In this region, the rotated structure experiences an extra force which pulls the device into the region of instability beyond the pull-in voltage limits. This paper illustrates the application of variable structure controllers to correct such behavior and to compensate for the uncertainties that exist in the microsystem parameters. The controller is equipped with a self-tuning parameter estimation block and is designed to control the LCRs under both normal and fault conditions. The controller utilizes through-wafer optical monitoring to provide the position feedback signal. The controller is implemented in a real-time control board and is experimentally verified to demonstrate the effectiveness of the control method under different operating conditions.

Simple Fuzzy Self-tuning PI Controller for Multi-terminal HVDC Transmission Systems

This paper describes fuzzy logic-based self-tuning controller parameters for the rectifier side current regulators and inverter side gamma regulators in a multi-terminal high-voltage direct current (MTDC) transmission system. A four-terminal MTDC system is considered, with the detailed modeling of converters, transformers, source impedances, transmission lines, and filters. Simulations are conducted using MATLAB/SIMULINK® to generate transient responses. The simulation results using a fuzzy self-tuning proportional-integral (PI) controller and a fixed-parameter PI controller are compared. The transient simulation studies presented reveal the superior performance of the fuzzy self-tuning PI controller over the fixed-parameter PI controller in the MTDC transmission systems.

Human Simulated Intelligent Controller with Fuzzy Online Self-Tuning of Parameters and its Application to a Cart-Double Pendulum

Using the basic concepts and design methods of Human Simulated Intelligent Control (HSIC), we have designed the master control level of an HSIC controller for the swinging-up and handstand control of a cart-double pendulum. Then, the self-tuning structure of the self-tuning level of the HSIC Controller is implemented using fuzzy logic rules. This structure has fuzzy self-tuning abilities in the swing-up control of a cart-double pendulum system and achieves online self-tuning of the control parameters of the HSIC controller efficiently with a hierarchical and multimode control structure. The computer simulation and realtime experiments of the swing-up control of a cart-double pendulum system show that the fuzzy online self-tuning of the control parameters markedly enhances the robustness and adaptability of the HSIC.

Self-tuning dynamic models of HVAC system components

A great majority of modern buildings are equipped with Energy Management and Control Systems (EMCS) which monitor and collect operating data from different components of heating ventilating and air conditioning (HVAC) systems. Models derived and tuned by using the collected data can be incorporated into the EMCS for online prediction of the system performance. To that end, HVAC component models with self-tuning parameters were developed and validated in this paper. The model parameters were tuned online by using a genetic algorithm which minimizes the error between measured and estimated performance data. The developed models included: a zone temperature model, return air enthalpy/humidity and CO 2 concentration models, a cooling and heating coil model, and a fan model. The study also includes tools for estimating the thermal and ventilation loads. The models were validated against real data gathered from an existing HVAC system. The validation results show that the component models augmented with an online parameter tuner, significantly improved the accuracy of predicted outputs. The use of such models offers several advantages such as designing better real-time control, optimization of overall system performance, and online fault detection.

A Self-Tuning Control Strategy for Gas-Fired Glass Forehearths

This paper describes how a self-tuning feedforward-feedback control strategy could be implemented on a three-zone forehearth as used in the manufacture of glass containers. Experimental results, based upon real operating data, are presented in order to evaluate the control algorithm. It is shown how an existing model of a forehearth zone can be modified by the introduction of a self-tuning parameter, which enables the model to predict the glass surface temperature from the exit of any forehearth zone. The proposed control strategy for one zone of the forehearth is then modified to incorporate the self-tuning parameter, which makes it suitable for controlling each zone of the forehearth. Simulation results, using real operating data records, clearly show the effectiveness of the self-tuning parameter for the model as well as the control stategy.