Self-tuning Control Research Articles

Stable optimal self-tuning interval type-2 fuzzy controller for servo position control system

An optimal self-tuning interval type-2 fuzzy controller for servo position control systems is reported here. To achieve precise positioning of the actuator, input scaling factors of an interval type-2 fuzzy proportional-integral controller are updated online depending on the latest operating conditions in terms of closed loop tracking error and change of error. To ensure desired performance, input scaling factors are obtained by adaptive cuckoo search-based optimisation algorithm. Efficacy of the proposed scheme is substantiated through performance comparison with recently reported peak observer based and online self-tuning based interval type-2 fuzzy PID, interval type-2 fuzzy PI, and also type-1 fuzzy PI controllers through simulation study along with real-time validation on a DC servo position control system. Lyapunov function-based stability analysis for the proposed controller is also provided.

Nonlinear Identification and Control of Laser Welding Based on RBF Neural Networks

A laser beam is a heat source with a high energy density; this technology has been rapidly developed and applied in the field of welding owing to its potential advantages, and supplements traditional welding techniques. An in-depth analysis of its operating process could establish a good foundation for its application in China. It is widely understood that the welding process is a highly nonlinear and multi-variable coupling process; it comprises a significant number of complex processes with random uncertain factors. Because of their nonlinear mapping and self-learning characteristics, artificial neural networks (ANNs) have certain advantages in comparison to traditional methods in the field of welding. Laser welding is a nonlinear dynamic process; these processes still pose a major challenge in the field of control. Therefore, establishing a stable model is a prerequisite for achieving accurate control. In this study, the identification and control of radial basis function neural networks in laser welding processes and self-tuning PID control methods are proposed to improve weld quality. Using a MATLAB simulation, it is shown that the proposed method can obtain a good description of the level of nonlinear dynamic control, and that the algorithm identification accuracy is high, practical, and effective. Using this method, the weld width quickly reaches the expected value and the system remains stable, with good robustness. Further, it ensures the stability and dynamic performance of the welding process and improves weld quality.

Modeling and Simulation of Two Axes Gimbal Using Fuzzy Control

The application of the guided missile seeker is to provide stability to the sensor's line of sight toward a target by isolating it from the missile motion and vibration. The main objective of this paper is not only to present the physical modeling of two axes gimbal system but also to improve its performance through using fuzzy logic controlling approach. The paper is started by deriving the mathematical model for gimbals motion using Newton's second law, followed by designing the mechanical parts of model using SOLIDWORKS and converted to xml file to connect dc motors and sensors using MATLAB/SimMechanics. Then, a Mamdani-type fuzzy and a Proportional-Integral-Derivative (PID) controllers were designed using MATLAB software. The performance of both controllers was evaluated and tested for different types of input shapes. The simulation results showed that self-tuning fuzzy controller provides better performance, since no overshoot, small steady-state error and small settling time compared to PID controller.

Self-Tuning Multiple Model Predictive Control for Signalized Traffic Junctions with Jump Dynamics

Self-Tuning Multiple Model Predictive Control for Signalized Traffic Junctions with Jump Dynamics

Self-tuning fuzzy PID speed controller for quarter electric vehicle driven by In-wheel BLDC motor and Pacejka's tire model

Vehicles using internal combustion engine (ICE) adversely affect the environment. Therefore, all vehicle makers have started their mutation toward electric vehicles. In this paper, a quarter vehicle model is developed considering brushless DC (BLDC) motor actuation. The longitudinal behavior of the drive wheel is modeled using the Pacejka model or magic formula. This model describes the interaction between the tire and the road surface, which reflects road condition effect. The purpose of the control is to provide smooth speed control in both acceleration and deceleration modes, regardless of changes in road conditions. Due to the high complexity of the control model, a hybrid fuzzy-PID controller is designed to serve as a speed controller for the system motor-tire-road-surface. It is checked by numerous simulations that the proposed controller performs well, ensuring comfortable steering operation despite road characteristic changes.

Self Tuning PID Control of Antilock Braking System using Electronic Wedge Brake

This paper describes the design of an antilock braking system (ABS) control for a passenger vehicle that employs an electronic wedge brake (EWB). The system is based on a two-degree-of-freedom (2-DOF) vehicle dynamic traction model, with the EWB acting as the brake actuator. The developed control structure, known as the Self-Tuning PID controller, is made up of a proportional-integral-derivative (PID) controller that serves as the main feedback loop control and a fuzzy supervisory system that serves as a tuner for the PID controller gains. This control structure is generated through two structures, namely FPID and SFPID, where the difference between these two structures is based on the fuzzy input used. An ABS-based PID controller and a fuzzy fractional PID controller developed in previous works were used as the benchmark, as well as the testing method, to evaluate the effectiveness of the controller structure. According to the results of the tests, the performance of the SFPID controller is better than that of other PID and FPID controllers, being 10% and 1% faster in terms of stopping time, 8% and 1% shorter in terms of stopping distance, 9% and 1% faster in terms of settling time, and 40% and 5% more efficient in reaching the target slip, respectively.

Stabilization and tracking control of an x-z type inverted pendulum system using Lightning Search Algorithm tuned nonlinear PID controller

AbstractInverted pendulum systems (IPSs) are mostly used to demonstrate the control rules for keeping the pendulum at a balanced upright position due to a slight force applied to the cart system. This paper presents an application for nonlinear control of an x-z type IPS by using a proportional-integral-derivative (PID) controller with newly established evolutionary tuning method Lightning Search Algorithm (LSA). A single, double and triple PID controller system is tested with the conventional and the self-tuning controllers to get a better understanding of the performance of the given system. The mathematical modelling of the x-z type IPS, the theoretical explanation of the LSA and the simulation analysis of the x-z type IPS is put forward entirely. The LSA algorithm solves the optimization problem of PID controller in an evolutionary way. The most effective way of making comparisons is evaluating the performance results with a well-known optimization technique or with the previous accepted results. We have compared the system’s performance with particle swarm optimization and with a classical control study in the literature. According to the simulation results, LSA-tuned PID controller has the ability to decrease the overshoot better than the conventional-tuned controllers. Finally, it can be concluded that the LSA-supported PID can better stabilize the pendulum angle and track the reference better for non-disturbed and the slightly disturbed systems.

Semi-active vibration control of building structure by Self Tuned Brain Emotional Learning Based Intelligent Controller

Control algorithms are the most crucial aspects in effective control of civil structures exposed to earthquake forces. Recently, adaptive intelligent control algorithms are evolving to be a viable substitute strategy for conventional model-based control algorithms. One of the most recent developments, known as the Brain Emotional Learning Based Intelligent Controller (BELBIC), has caught the attention of scientists as a model-free adaptive control system. It possesses appealing capabilities for dealing with nonlinearities and uncertainties in control frameworks. The modern semi-actively controlled civil structures have a highly uncertain and nonlinear nature following severe disturbances. As a result, these structures require real-time (online) robust control actions towards changing conditions, which the controllers with rigid settings cannot adapt. This study intends to overcome this issue in two ways: an online self-tuning brain emotional learning-based intelligent controller (ST-BELBIC) is formulated. Then its capabilities in improving the performance of cascaded controller in attenuating seismic vibrations of a three-story scaled building structure are validated. In this case, the central control unit BELBIC is based on sensory inputs (SI) and emotional cues (reward) signals. The main contribution of the proposed controller is a self-attuned version of the standard BELBIC that uses the benefits of a first-order Sugano fuzzy inference system (FIS) to adapt its parameters online. The proposed control methodology can be a promising model-free controller in terms of online tuning, simplicity of configuration, ease of applicability, less operational time, and neutralizing nonlinearities. The simulation affirms that the proposed controller compared with conventional LQR and intelligent Fuzzy tuned PID (FT-PID) controllers shows a superior performance regarding attenuating seismic responses of the building and can also improve the performance of cascaded FT-PID controller.

Self‐tuning predictive control applicable to ship magnetic levitation damping device

In the ship design, there are strict vibration-proof requirements for precision instruments. Therefore, a ship repulsive magnetic levitation damping device is designed to achieve vibration reduction. And one self-tuning predictive control method is proposed to achieve the stable levitation of this device. Firstly, a predictive control (MPC) method with state constraints and input constraints is adopted to realise the stable suspension of the floater. The MPC can solve the problem of position imbalance of the magnetic levitation system under the external complex disturbances. Secondly, a self-tuning MPC method based on recursive least square is proposed to solve the problem caused by the fixed parameters of the traditional predictive controller. At the beginning of each control cycle, the recursive least-squares (RLS) method is used to estimate the parameters of the system. Thus, the optimal control model could be obtained for the current situation. Then, this model is applied to the predictive controller to solve the problem of parameter fixation in the traditional predictive control. Finally, the simulation results show that it can improve the accuracy, dynamic response and anti-interference performance obviously.

Sliding Mode Self-Tuned Single Neuron PID Controller for Power System Stabilizer

In this paper, a modified technique based on the combination of the Single Neuron PID (SNPID), as the main controller and Sliding Mode Control (SMC), as an adaptation technique, to design an optimized self-tuned for SNPID controller that may overcome difficulties faced when a change in system operating points occurs. The proposed approach has been implemented as a power system stabilizer (PSS) for a synchronous generator connected to an infinite bus. The Flower Pollination (FP) optimization is based on an appropriate objective function. To demonstrate the effectiveness of the combination obtained controllers, PSS, is tested under different operating conditions. The combination controllers are shown through uncertainties system parameters changes under different disturbances. The results show the ability of the suggested controllers to enhance well the system performances

Iterative Self-Tuning Minimum Variance Control of a Nonlinear Autonomous Underwater Vehicle Maneuvering Model

This paper addresses the problem of control design for a nonlinear maneuvering model of an autonomous underwater vehicle. The control algorithm is based on an iteration technique that approximates the original nonlinear model by a sequence of linear time-varying equations equivalent to the original nonlinear problem and a self-tuning control method so that the controller is designed at each time point on the interval for trajectory tracking and heading angle control. This work makes use of self-tuning minimum variance principles. The benefit of this approach is that the nonlinearities and couplings of the system are preserved, unlike in the cases of control design based on linearized systems, reducing in this manner the uncertainty in the model and increasing the robustness of the controller. The simulations here presented use a torpedo-shaped underwater vehicle model and show the good performance of the controller and accurate tracking for certain maneuvering cases.

Control System and Algorithm of Sewage Treatment Plant Based on Artificial Intelligence

With the improvement of people’s awareness of environmental protection in recent years, the related problems of water pollution treatment have gradually come into people’s view. As the source of life, water accounts for a huge proportion in our lives, but at the same time, water pollution is quietly spreading in places we don’t know. The continuous discharge of heavy industrial wastewater, agricultural wastewater and domestic sewage leads to increasingly serious water pollution. Sewage treatment(ST) is imperative, and its social benefits are huge, but the corresponding cost is high, and the return on investment is low. Traditional ST methods can not load large-scale ST. How to carry out ST based on artificial intelligence(AI), build ST plant control system, and make ST enter the era of automation is the problem to be solved. The purpose of this paper is to put forward the reform of control system for ST plant based on AI, apply AI into ST, and realize the automation and precision of ST plant. This paper mainly uses the fuzzy self-tuning PID control system algorithm, through the analysis of ST control object, analysis of fuzzy self-tuning PID controller design to complete the ST control system settings. In this paper, the literature review method and data analysis method are used. By collecting relevant data, the control system of ST plant is constructed to simulate ST, and the real-time data of ST is analyzed. The traditional PID control and fuzzy self-tuning PID control are compared. The experimental results show that the wastewater treatment plant system based on AI input, in the aspect of wastewater treatment, the concentration of COD and BOD in the treated wastewater are reduced by a certain proportion, the dissolved oxygen content in the wastewater reaches about 2.0mg/l, which meets the national discharge standard, and its rising time is reduced to 25 seconds, and the adjustment time is saved by 50 seconds.

Adaptive Self-Tuned Controller IC for Resonant-Based Wireless Power Transfer Transmitters

This article introduces an adaptive self-tuned controller IC for resonant wireless power transfer (RWPT) transmitters. The controller IC comprises an on-the-fly very-high-frequency tracking hardware with high resolution and an independent high-resolution digital pulsewidth modulator (PWM)-based (HR-DPWM) current programmed control. These facilitate precise frequency generation as well as adaptive tuning of the reactive components in the matching network, which translate into tight current/power regulation capabilities while retaining optimized power transfer conditions on the transmitting side. The controller IC enables to effectively disengage the power-delivery capabilities from the variations of the resonators, electrical circuits, and wireless medium. The controller core is based on a fully synthesizable digital architecture that has been realized through HDL tools, and several key building blocks have been developed and described in detail: a delay-line-based phase detector, high-resolution digital frequency synthesizer, and HR-DPWM. To fully exploit the benefits of digital electronics, reduce power consumption, and save area, the digital core of the controller has been designed completely through asynchronous architecture, eliminating the need of high-speed clock and its related hardware. The mixed-signal controller IC has been designed, implemented, and fabricated in 0.18-μm 5-V CMOS process, resulting in effective silicon area of 0.6 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . To demonstrate the mixed-signal controller IC in closed-loop operation of a wireless power system, an experimental 20-W resonant capacitive-based WTP system has been designed and validated. The effectiveness of the controller is well demonstrated and evaluated at the MHz range up to 200 mm misalignment, meeting the strict requirements of resonant-based WPT systems.

PID Tuning Method Using Chaotic Safe Experimentation Dynamics Algorithm for Elastic Joint Manipulator

This paper proposed the chaotic safe experimentation dynamics algorithm (CSEDA) to regulate angular tracking and vibration of the self-tuning PID controller for elastic joint manipulators. CSEDA was a modified version of the safe experimentation dynamics algorithm (SEDA) that used a chaos function in the updated equation. The chaos function increased the exploration capability, thus improving the convergence accuracy. In this study, two self-tuning PID controllers were used to regulate the rotating angle tracking and vibration for elastic joint manipulators in this control challenge. The suggested self-tuning PID controller's performance was evaluated in angular motion trajectory tracking, vibration suppression, and the pre-determined control fitness function. A self-tuned PID controller based on CSEDA could achieve superior control accuracy than a traditional SEDA and its variants.

Self-tuning fuzzy logic control of quarter car and bridge interaction model

In this study, active suspension control of the interaction between the bridge can be modeled according to the Euler-Bernoulli beam theory, and the quarter car model with three degrees of freedom is studied. The active suspension system consists of a spring, damper, and linear actuator. The active suspension control is designed using classical PID and self-tuning fuzzy PID (STFPID) to reduce the vehicle body's disruptive effects. To determine the performance of the designed controllers, two different road profiles with the bridge oscillations caused by the bridge flexibility were considered as the disruptive effect of the vehicle. When the simulation results were examined in terms of passenger seat displacement and acceleration, the proposed STFPID method significantly increased road holding and ride comfort.

Fuzzy Logic-Based Self-Tuning PID Controllers Using Parameters Adaptive Method for Stabilization of a Two-Axis Seeker Gimbal

In guided missiles, the major function of a seeker is to track a target continuously. In target tracking process most seekers employ a gimbal system, usually a two-axis, on which a tracking device and a rate gyroscope are mounted. For such guided missiles, the stabilization of gimbal is essential for maintaining the tracking device directing towards the target. Various control methods have been developed for the gimbal stabilization to make the gimbal to respond faster for a rate command input so that the gimbal follows the rate input in the presence of disturbances. Here, a fuzzy self-tuning proportional, integral, derivative (PID) type controller based on parameters adaptive method is proposed for two-axis gimbal stabilization which can be applicable for most guided missiles. The controller gains in the proposed control technique are made of input and output scaling factors used in fuzzification and defuzzification processes. The scaling factors are tuned online based on the missile rotation rate and the gimbal rate response. The response of the gimbal in the stabilization loop with the proposed controller has been analyzed in MATLAB for various missile rotation rates and the simulation results are compared with a traditional PID controller. The results prove the efficiency of the proposed control method which gives a better and faster response with zero steady-state error than the traditional control method.

An intelligent self-tuning fuzzy-PID controller to coupled tank liquid level system

An intelligent self-tuning fuzzy-PID controller to coupled tank liquid level system

PSO Self-Tuning Power Controllers for Low Voltage Improvements of an Offshore Wind Farm in Taiwan

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.

Self-Tuning PID-Genetic Algorithm Controller for Steam Drum Boiler Water Level Control System

A control system with uncertainty or unpredictable disturbance needs more effort to be controlled. A conventional PID Controller is the most popular method used in industries. It was tuned and adjusted by the designer, and it has fixed parameters during operation. However, the disturbance effect causes the desired system performance unreachable. By using a self-tuning controller, the problem should be tackled. In this paper, the PID-Genetic Algorithm (PID-GA) controller was proposed and tested with the steam drum water level control system of a steam power plant. Variation in power load causes noisy water level characteristics and should be maintained at + 0.4 meters from the setpoint to prevent the power plant trip. From the simulation, PID-GA can reduce disturbance of the minimum, nominal, and maximum load with perturbation peaks 0.18 m, 0.22 m, and 0.26 m respectively.Keywords: genetic algorithm, NWL, PID-GA, steam drum, steam power plant.

Harmonic Analysis and Self-Tuning Control Combining Wavelet Analysis and Identification for High-Tc RF SQUID

High-T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</sub> radio-frequency (rf) superconducting quantum interference devices are well understood and widely used in many areas of low-field measurement because of their high sensitivity and high resolution. Harmonic analysis has proven to be an efficient way for finding the optimum working point. However, there is a precondition for using harmonic analysis, which is not well understood so far. In this article, it is shown that the precondition can be fulfilled in the typical range of parameters and under our experimental conditions by using a direct mathematical derivation. It is demonstrated that the improvement of wavelet analysis in mean square error (MSE) is more than 50% for harmonic analysis. In addition, identification by forgetting factor recursive least square is a helpful way for self-tuning controller who gives fast tracking, small MSE and high coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) for harmonic analysis of different experimentally measured signals.