System Identification and Parameter Self-Tuning Controller on Deep-Sea Mining Vehicle

System identification is a quintessential measure for real-time analysis on kinematic characteristics for deep-sea mining vehicle, and thus to enhance the control performance and testing efficiency. In this study, the system identification algorithm, recursive least square method with instrumental variables(IV-RLS), is tailored to model ‘Pioneer I’, a deep-sea mining vehicle which recently completed a 1305-meter-deep sea trial in the Xisha area of the South China Sea in August, 2021. The algorithm operates on the sensor data collected from the trial to obtain the vehicle’s kinematic model and accordingly design the parameter self-tuning controller. The performances demonstrate the accuracy of the model, and prove its generalization capability. With this model, the optimal controller has been designed, the control parameters have been self-tuned, and the response time and robustness of the system have been optimized,which validates the high efficiency on digital modelling for precision control of deep-sea mining vehicles.

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.

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.

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.

A Stable Fuzzy-Based Computational Model and Control for Inductions Motors

In this paper,a stable and adaptive sliding mode control(SMC)method for induction motors is introduced.Determining the parameters of this system has been one of the existing challenges.To solve this challenge,a new self-tuning type-2 fuzzy neural network calculates and updates the control system parameters with a fast mechanism.According to the dynamic changes of the system,in addition to the parameters of the SMC,the parameters of the type-2 fuzzy neural network are also updated online.The conditions for guaranteeing the convergence and stability of the control system are provided.In the simulation part,in order to test the proposed method,several uncertain models and load torque have been applied.Also,the results have been compared to the SMC based on the type-1 fuzzy system,the traditional SMC,and the PI controller.The average RMSE in different scenarios,for type-2 fuzzy SMC,is 0.0311,for type-1 fuzzy SMC is 0.0497,for traditional SMC is 0.0778,and finally for PI controller is 0.0997.

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.

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.

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.

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.

Multivariable PI Type Generalized Predictive Control

This paper presents a multivariable generalized predictive controller with proportion and integration structure by modifying the quadratic criterion of the usual MGPC. The control performance has been improved greatly. The effectiveness of the controller is demonstrated by the simulation result.

AUTOMATIC TUNING OF PID CONTROLLERS IN THE THERMAL POWER UNIT LOAD CONTROL SYSTEM

A method of measuring the interactions in a multivariable control sys-tem(MVCS)in time domain is defined in this paper.An intelligent decoupling com-pensator is designed in terms of the concept of fuzzy control,so that the auto-tuningof controllers’ parameters in a 2×2 MVCS can be turned into that of two independentsingle-loop control systems(SLCS).The method presented in the paper has success-fully been used in a simulation experiment on the automatic tuning of a coordinatedcontrol system(CCS)in the drum-boiler turbogenerating unit(DBTU)and the simu-lation results axe satisfactory.

Motion Control of Omnidirectional Mobile Platform based on Fuzzy PID

The omnidirectional mobile platform has three degrees of freedom that make it widely applicable to all areas of industry, while the Mecanum wheel has the disadvantages wheel omnidirectional mobile platform is always difficult in of large vibration, the trajectory precision of Mecanum the omnidirectional mobile platform. To control the trajectory of the omnidirectional mobile platform better, this paper proposes a fuzzy self-tuning PID control model, through establishing the motion model of omnidirectional mobile platform in Adams software, then combined with Simulink simulation, analysis of fuzzy PID controller to improve the accuracy of the speed control of omnidirectional mobile platform, improve the control method of a precise trajectory of the omnidirectional mobile platform motion.

TYPICAL ARCHITECTURES FOR FUZZY CONTROL

Some typical structural schemes of Fuzzy control have been surveyed. Besides general structure of fuzzy logic controller (FLC), the structural schemes include PID fuzzy controller, self-organizing fuzzy controller, selftuning fuzzy controller, self-learning fuzzy controller, and expect fuzzy controller, etc. This survey focuses on the control principle, and provides a basis for potential applications. Most of the structures have been used in various control fields, one of application areas is in the metallurgy industry, e. g., the temperature control of the electric furnace, the control of the aluminum smelting process, etc. According to the application requirements, one can choose a structural scheme for special use.

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.

Simulation Analysis of Control System in an Innovative Magnetically-Saturated Controllable Reactor

Controllable saturation reactors are widely used in reactive power compensation. The control system of controllable saturation reactor determines adaption speed, accuracy, and stability. First, an innovative type of controllable saturation reactor is introduced. After that the control system is designed, and a self-tuning algorithm in PID controller is proposed in the paper. The algorithm tunes PID parameters automatically with different error signals caused by varied loads in power system. Then the feasibility of the above algorithm is verified by Simulink module of Matlab software. The results of simulation indicate that the control system can efficiently reduce adaption time and overshoot.

RESEARCH ON THE ADAPTIVE CONTROL OF DRILLING PROCESS

A self-tuning adaptive controller is presented which is used for the adaptive control (AC) of drlling process. The parameters of the controller can be regulated in-processly by on line system identification, so as to fit the variation of the cutting conditions. This control method has been successfully applied to the adaptive control of drilling process. The experimental results indicate that the controller has the ability to adapt to the variation of the cutting process parameters and is good in performance.

Smith Prediction Monitor AGC System Based on Fuzzy Self-Tuning PID Control

In accordance with the feature of pure delay in monitor AGC system for cold rolling mill, a new fuzzy selftuning PID Smith prediction controller is developed. The position control model is deduced based on a single stand cold rolling mill, and the fuzzy controller for monitor AGC system is designed. The analysis of dynamic performance for traditional PID Smith prediction controller and fuzzy self-tuning PID Smith prediction controller is done by MAT- LAB toolbox. The simulation results show that fuzzy self-tuning PID Smith controller has stronger robustness, faster response and higher static accuracy than traditional PID Smith controller.

STABILITY AND STOCHASTIC CONVERGENCE OF SYNTHETIC GENERALIZED PREDICTIVE SELF-TUNING CONTROLLER

The synthetic generalized predictive adaptive algorithm has been greatly developed recently. This paper establishes some global stability and convergence results for the algorithm with a strict proof.

Robust Self-tuning Control Based on Discrete-time Sliding Mode for Auto-regressive Mathematical Model in the Presence of Unmodelled Dynamics

In this paper, we propose a new robust selfbtuning control, called the generalized minimum variance a/-equivalent self- tuning control （GMVSTC-a/） for the linear timevarying （LTV） systems, which can be described by the discrete-time auto-regressive exogenous （ARX） mathematical model in the presence of unmodelled dynamics. The estimation of the parameters contained in this mathematical model is made on the basis of the proposed modified recursive least squares （m-RLS） parametric estimation algorithm with dead zone and forgetting factor. The stability analysis of the proposed parametric estimation algorithm m-RLS is treated on the basis of a Lyapunov function. A numerical simulation example is used to prove the performances and the effectiveness of the explicit scheme of the proposed robust self-tuning control GMVSTC-a/.

基于RBF神经网络的改进多变量预测控制

针对一类多输入多输出非线性被控对象,提出一种基于单神经网络的预测控制算法,应用RBF神经网络对非线性系统进行辨识,并计算被控系统多步预测输出值。该方法通过对传统预测目标函数加以改进,给出一种带微分项的多步预测目标函数,通过迭代寻优实时给出优化控制量。该方法实时性好,简化了传统预测控制算法,加快了滚动寻优的速度,有效地抑制了系统惯性和输入时滞所带来的超调,减小了模型误差、干扰及不确定性对控制器的影响。仿真及应用结果表明了该方法的有效性。