Self-tuning Control Algorithm Research Articles

Application of a Neural Network on Steam Drum Pressure Control in a Fixed-Bed Reactor for Exothermic Reactions

The control of steam drums, used to remove heat from Fischer–Tropsch synthesis or diethyl oxalate hydrogenation, is confronted with a challenge on controlling quality. The traditional proportional–integral–differential (PID) controllers with fixed parameters are dissatisfying upon deployment. The backward-propagation neural network (BPNN) self-tuning PID control algorithm was thus developed and implemented via a Python and KINGVIEW software combination. Application experiments showed that, in both setpoint control and step change control of the steam drum pressure, static deviation and the maximum error were less with the BPNN self-tuning PID controller, in comparison to the conventional PID controller. Moreover, it seemed that certain adaptations occurred to the nonlinear change in the reaction system, revealing that it was superior to the traditional PID controller. It is shown that the backward-propagation neural network will improve the control quality in boiling water drum systems for exothermic reactions. It can be predicted that the backward-propagation neural network is qualified for process condition control in the chemical industry.

Pressure control for pneumatic pressing roller in automated fiber placement

In automated fiber placement (AFP), the contact pressure between the roller and the mold is an important process parameter. Too small or too large contact pressure will result in unacceptable laying defects. This paper focuses on the control method of contact pressure in AFP. Firstly, the dynamic model of the pressing foot mechanism is established, and a mathematical relationship of input air pressure and output force is developed. Then, a self-tuning PI control algorithm is used to implement servo control of the roller output force based on the Slowly Time-Varying Recursive Identification Algorithm. Also, using Abaqus/CAE, the mapping relationship between the contact area and measurements from displacement sensors is established to predict the contact area in real time. Finally, the contact pressure control method is established by combining the servo control method of contact force and the prediction method of contact area. Experimental results show that the constant pressure control method can effectively stabilize the contact pressure, with a pressure fluctuation of less than 4% under the experimental conditions set in this paper.

Fault-Tolerant Self-Tuning Control for Three-Phase Three-Level T-Type Rectifier

This paper proposes a sensorless self-tuning control algorithm for a three-phase T-type rectifier. The proposed sensorless algorithm consists of three layers including an observer, which estimates grid voltages or currents based on the Kalman filter algorithm (KFA), an online estimator, which estimates the state-space matrix parameters, and a linear quadratic tracker (LQT), which is the main controller in this study. On the other hand, the performance of the LQT depends on the gains that need to be adjusted according to the system parameters, which is a challenge. To overcome this problem, a self-tuning method based on recursive least square algorithm (RLSA) is also proposed to tune the gains of LQT online. An experimental prototype is established, and the results, including the steady-state and dynamic performances, are carried out and presented to validate the effectiveness of the proposed controller.

Self-tuning vibration reduction study of magnetic fluid rolling-ball damper based on PSO-FUZZY-PID

For towering structures under complex time-varying excitation, the tuned magnetic fluid rolling-ball dampers (TMFRBD) cannot achieve self-tuning damping. In this paper, considering the operating characteristics of the TMFRBD, a damper self-tuning control algorithm, namely particle swarm optimization (PSO)-FUZZY-PID, is proposed for real-time adjustment of the intrinsic frequency of TMFRBD, which can greatly improve the damping effect, namely self-tuning magnetic fluid rolling-ball dampers (STMFRBD). Firstly, the equivalent dynamics model of the damping system is established, deriving the structural dynamic amplitude response as a function of the excitation frequency to structural frequency ratio. Different frequency tracking schemes of the damper are compared and analyzed. Then magnetic field simulations and magnetic fluid magnetic property measurements are performed for the dampers, respectively. Secondly, the self-tuning PSO-FUZZY-PID control algorithm is designed to improve the damping performance of STMFRBD under time-varying excitation by optimizing factors of the FUZZY-PID controller with PSO. To confirm the effectiveness of the damper self-tuning control algorithm, the frequency tracking of dampers with different control algorithms under time-varying excitation is compared. The results show that the damper’s frequency can be tuned quickly and accurately tracking excitation frequency by PSO-FUZZY-PID. Finally, the simulation and experimental results of STMFRBD and passive mass damper (PMD) under different loads are analyzed, and the structural amplitude response is analyzed using the Hilbert-Huang transform (HHT). In addition, the damping of STMFBD and TMFBD under different loads is compared. The results show that STMFRBD has better vibration-damping performance than PMD and TMFRBD.

Self-tuning adaptive active disturbance rejection pitch control of a manta-ray-like underwater glider

In this paper, a self-tuning adaptive active disturbance rejection control (ADRC) algorithm for the pitch angle control of a new manta-ray-like underwater glider—”NPManta II” is proposed. First, a new dynamic model of NPManta II is established, which quantifies the influence of installation error, bladder volume change and position change of the moving internal mass. Second, a linear extended state observer with parameter self-tuning based on the least mean square algorithm is designed. Third, a control-switching strategy is applied during posture transition to avoid inaccurate compensation of the control input. Finally, simulations and sea trial of the proposed adaptive ADRC controller and proportional integral differential controller are presented. The results indicate that the parameter converges quickly. The adaptive ADRC controller can effectively compensate for the control input, and move the internal mass to the optimal equilibrium position quickly and accurately, thus avoiding unnecessary movements to save energy.

Recursive least squares based sliding mode approach for position control of DC motors with self-tuning rule

In this paper, a self-tuning rule-based position control algorithm is proposed for DC motors with system parameter estimation using the recursive least squares method. First, a mathematical model of the angular position control of a DC motor was derived. Next, the time-varying parameters including the rotational inertia in the model were estimated using the RLS method along with multiple forgetting factors without prior knowledge of the system. Based on the derived model and the parameter estimation, a sliding mode control algorithm was designed by applying a self-tuning rule that enables the magnitude of the voltage input to be adaptively adjusted for improvement of the energy efficiency. The performance of the designed control algorithm was then experimentally evaluated under several different load conditions. Finally, the evaluation results show that the designed controller achieves a satisfactory capability for a DC motor to deal with both tracking accuracy and energy efficiency without prior knowledge of the system.

Research on temperature control of heating furnace with intelligent proportional integral derivate control algorithm

In view of the problems of large overshoot and large oscillation frequency in cur?rent furnace temperature control, based on the development of intelligent control theory, expert control, fuzzy control, and neural network control in intelligent control theory are combined with proportional integral derivative (PID) control. The intelligent PID control algorithm is used to carry out numerical simulation and experimental research on these several control algorithms. The results show that the adjustment effect of the intelligent PID control algorithm is significantly better than the traditional PID control algorithm. Among them, the fuzzy self-tuning PID control algorithm and the fuzzy immune PID control algorithm are feasible in the application of furnace temperature control. The neural network PID control algorithm It also has good development and application potential.

Microgrid Dynamic Modeling and Islanding Control With Synchrophasor Data

This paper illustrates the application of synchrophasor data to formulate reduced order dynamic models and use these models to design control algorithms that allow automated islanding and grid reconnection of a microgrid. The dynamics of the phasors at the point of common coupling (PCC) excited by the controllable assets in the microgrid are captured in reduced order models estimated from phasor data. The controllable assets are taken to be a hybrid set of distributed energy resources (DER) with different operational constraints and dynamics. In turn, the reduced order models are used to design a self-tuning feedback control algorithm to control the phasors at the PCC to allow seamless islanding and reconnection of the microgrid. The approach is illustrated on a hardware-in-the-loop setup with energy storage (ES) and diesel generator (DG) to validate the effectiveness of the modeling and control algorithms.

Fuzzy self-tuning PID control algorithm for belt conveyor driven by multi-motor

Aiming at the problems of low efficiency when long distance and high-inclination belt conveyor driven by multi-motor is in light or idle run as well as electrical power unbalance. Control strategy for belt conveyor driven by four-motor via fuzzy self-tuning PID control algorithm was proposed. The Speed setting algorithm of belt conveyor and fuzzy self-tuning PID algorithm for speed control and power balance control are introduced. The application shows that, according to the coal flow changes of the belt conveyor in real-time, the speed can be automatically adjusted through this algorithm, making the power of the four motors convergence both speed control error and the power control error of which are lower than 2.6%.

Fuzzy Self-tuning PID Control Algorithm for Belt Conveyor Driven by Multi-motor

Fuzzy Self-tuning PID Control Algorithm for Belt Conveyor Driven by Multi-motor

Self-tuning control algorithm design for vehicle adaptive cruise control system through real-time estimation of vehicle parameters and road grade

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.

Điều khiển tự thích nghi hệ động lực tuyến tính có cấu trúc và thông số không thay đổi

A self-tuning adaptive control algorithm based on the modified variational method and the choosing an appropriate regulator is proposed for system with unknown order and coeeficient.

Vibration mitigation of nonlinear crane system against earthquake excitations with the self-tuning fuzzy logic PID controller

This study aims to reduce the earthquake-induced vibrations of the container crane structures using an active vibration control. Vibration control using intelligent controllers such as fuzzy logic has attracted the attention of structural control engineers during the last decade. Fuzzy logic can handle uncertainties and heuristic knowledge and even nonlinearities effectively and easily. The improved seismic control performance can be performed by converting a simply designed static gain into a real time variable dynamic gain through a self-tuning mechanism. Because of the several advantages, the self-tuning fuzzy logic control algorithm is used in this study. Furthermore, with the aim of the increasing the performance of the designed controller, derivative and integral controller which are independent from the fuzzy logic controller are added to the architectural structure of the controller. This structure is called as self-tuning fuzzy logic PID controller (STFLPIDC). In this study, a six-degrees-of-freedom nonlinear mathematical model of the container crane structure is defined. Then, the controller is developed. Performance of designed controller is evaluated by numerical simulations. In the simulations, the ground motion of the Kobe earthquake \((M_w=7.2)\) in Japan on January 17, 1995, is used as earthquake excitation. The time history of displacements and accelerations and frequency responses of the both uncontrolled and controlled cases of the crane structure and control force are obtained. Performance of the designed controller is compared with the classical fuzzy logic controller (FLC). Comparison of the STFLPIDC and FLC in frequency and time domain is presented to evaluate the effect of performance of the controllers. Simulations with the STFLPIDC show better seismic performance than classical FLC. Besides, the robustness of the STFLPIDC was checked through the change in rigidity parameters. It is revealed that using designed controller and active vibration control system causes suppression of the earthquake-induced vibration.

Real-Time Self-Tuning Speed Controller: Performance Comparison between Engine Fuelled with Palm Methyl Esters and Petroleum Diesel

This paper investigates the performance of a real-time self-tuning speed controller designed to track and regulate at various engine speeds. The controller was tested with an automotive engine fuelled with petroleum diesel and and palm oil biodiesel (Palm Methyl Esters) within speed range of 1800 rpm to 2400 rpm. A self-tuning control algorithm based on pole assignment method together with on-line model parameters estimation strategy based on the recursive least squares method are adopted. The ability of the controller to track, regulate at various engine speed and also to reject disturbances applied for both type of fuel are compared and presented. The results confirmed that the controller performed very satisfactorily.

Experimental system for the optimization of the parallel manipulator control

This paper presents the results of an experimental system design for the optimization of parallel manipulator control based on an optimal configuration. The experimental system is an open design for various different configurations and controllers to support parallel manipulator research and applications. Traditional PID control and self-tuning fuzzy PID control algorithm are applied for motion control of parallel manipulator. The quality system standard will be analyzed and compared with simulation results. The system can be a useful tool for research and training.

Fuzzy PID Parameters Self-Tuning in the Application of the Furnace Temperature Control System

According to the set temperature, the parameter self-tuning fuzzy PID control algorithm is used furnace temperature automatic control and fuzzy PID control process to make use of the MATLAB simulation. Simulation results show that compared with conventional PID control, the parameter self-tuning fuzzy PID control algorithm improves the system response, shorten the adjusting time, enhances the disruptive, demonstrate the superiority of the fuzzy PID.

Design methodology and self-turning velocity control for high-speed slim sensorless brushless direct current motors with self-lubricated bearings

This study presents the motor design methodology and adaptive fuzzy self-tuning proportional–integral–derivative (PID) velocity closed-loop control of slim sensorless brushless direct current (BLDC) motors with self-lubricated bearings that operate at a high speed of 14,000rpm. Moreover, the novel sensorless commutation method includes a mask-based phase shift detector to detect precise optimal commutation points. A prototype of the slim sensorless radial-flux BLDC motor with a self-lubricated bearing for use in the blowers of vacuum cleaners was manufactured and the motor design methodology was verified. The damping interaction caused by the self-lubricating hydrodynamic bearings, shafts, and blades of blowers was also considered, together with a multi-physics analysis. The velocity control experiment used a adaptive fuzzy self-tuning PID control algorithm that has been proven to improve the dynamic performance of the slim sensorless BLDC motors in the blowers of vacuum cleaners. The settling time converged within 1s, that is, the vacuum cleaners rapidly switched to different speeds, and the control approach exhibited robustness and precision in a steady state.

An Add-On Self-Tuning Control System for a UPFC Application

This paper presents an add-on self-tuning (ST) control scheme for a Unified Power Flow Controller (UPFC) to assist its conventional PI control system in damping power oscillations. The ST control algorithm is based on a Pole Shift (PS) technique that has previously been successfully applied in Adaptive Power System Stabilizers (APSSs). For a wide range of operating conditions, the conventional PI-UPFC unless returned suffers from insufficient/nonoptimal damping performance. To overcome this problem, the authors propose supplementing the PI controllers with an ST feedback loop comprised of an identifier and a PS control algorithm. With a CRLS identifier tracking the system conditions online, this scheme eliminates the need for PI parameter retuning. Further, to correctly identify system parameters during large disturbances, a constrained recursive least squares (CRLS) identification procedure is adopted here. An improved damping performance with the proposed add-on scheme assisting the nonoptimal PI-UPFC is demonstrated for a two-area power system.

Self-Tuning Closed-Loop Fuzzy Logic Control Algorithm for Adaptive Prestressed Structures

Constantly increasing traffic loads, unpredictable influences on and unplanned conditions of bridges represent new challenges in the field of engineering. Innovative strategies and construction techniques are required to meet these and future challenges. Adaptive prestressing offers great potential to increase a structure’s load-carrying capacity and to optimize its load-induced stresses. In this manner, cost efficiency and sustainability of constructions can be enhanced. Therefore, a control algorithm with high reliability and redundancy is required to meet the new challenges. In this paper, an adaptive fuzzy logic-based closed-loop control system for realization of adaptive prestressed structures is presented. Based on the new concept, two different specialized control algorithms are developed. In order to decrease deflections, the prestressing force of an aluminium truss is controlled. The algorithm includes a fuzzy system with model-based knowledge base. The learning process is established on direct synchronization of the calculated deflections of the model with the measured reactions of the structure. Another fuzzy system for adaptive prestressing of a reinforced concrete T-beam uses a model-free knowledge base. Optimization of load-induced stresses is accomplished utilizing expert knowledge. An incremental learning process is performed in accordance with the algorithm’s basic rules. Experiments on the two prototypes are conducted to investigate the functionality of the developed algorithms. In addition to efficiency, high reliability and redundancy are shown by the control system. In this article, the potential of fuzzy-controlled adaptive prestressing is illustrated.

The Pneumatic Position Control System Based on Fuzzy-PID

Put forward a kind of control system to achieve precise positioning of the pneumatic cylinder, coring to PC upper monitor and using Visual C++ as the development platform. Establish an experimental platform of pneumatic cylinder positioning control system: selecting the parameter self-tuning Fuzzy-PID control algorithm, using the proportional directional control valve, standard cylinder, displacement sensordata acquisition card and other components. the use of MATLAB to simulate and optimize, carry out the actual experiment on the basis of simulation. The experimental results proved the effectiveness and correctness of Fuzzy PID in pneumatic control system.