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.

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.

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.

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.

DYNAMIC HUNTING EXTREMUM CONTROL METHOD BASED ON MEASURE AND ESTIMATION

Objective To propose a dynamic hunting extremum control method based on comparison of the estimated value and measured value. Methods The output linear group of the system is approximately expressed as an nth order system with large time constant or with time delay. Moreover, the relation between input drive reversal of the logic circuit and dynamic output of the system is analyzed in detail. The calculating formulae for the drive reversal are given for different extremum control systems. Based on principles above, a controller using a micrprocessor and a testing laboratory plant were designed and implemented. Results With this new method, the controller achieves fast optimum point hunting process, good performance in extremum control systems for high order processes, and robust result under sudden change or drift of the extremum characteristics. Conclusion The new control owns the merits of rapid optimizing dynamics, robusticity, and wide applicability.

OUTPUT MAXIMIZATION CONTROL FOR VSCF WIND ENERGY CONVERSION SYSTEM USING EXTREMUM CONTROL STRATEGY

The energy conversion optimization control strategy is presented for a family of horizontal-axis variablespeed fixed-pitch wind energy conversion systems,working in the partial load region.The system uses a variablespeed wind turbine(VSWT)driving a squirrel-cage induction generator(SCIG)connected to a grid.A new maximum power point tracking(MPPT)approach is proposed based on the extremum seeking control principles under the assumption that the wind turbine model and its parameters are poorly known.The aim is to drive the average position of the operation point close to optimality.Here the wind turbulence is used as search disturbance instead of inducing new sinusoidal search signals.The discrete Fourier transform(DFT)process of some available measures estimates the distance of operation point to optimality.The effectiveness of the proposed MPPT approach is validated under different operation conditions by numerical simulations in MATLAB/SIMULINK.The simulation results prove that the new approach can effectively suppress the vibration of system and enhance the dynamic performance of system.

Power Maximization and Control of Variable-Speed Wind Turbine System Using Extremum Seeking

Maximizing the power capture is an important issue to the turbines that are installed in low wind speed area. In this paper, we focused on the modeling and control of variable speed wind turbine that is composed of two-mass drive train, a Squirrel Cage Induction Generator (SCIG), and voltage source converter control by Space Vector Pulse Width Modulation (SPVWM). To achieve Maximum Power Point Tracking (MPPT), the reference speed to the generator is searched via Extremum Seeking Control (ESC). ESC was designed for wind turbine region II operation based on dither-modulation scheme. ESC is a model-free method that has the ability to increase the captured power in real time under turbulent wind without any requirement for wind measurements. The controller is designed in two loops. In the outer loop, ESC is used to set a desired reference speed to PI controller to regulate the speed of the generator and extract the maximum electrical power. The inner control loop is based on Indirect Field Orientation Control (IFOC) to decouple the currents. Finally, Particle Swarm Optimization (PSO) is used to obtain the optimal PI parameters. Simulation and control of the system have been accomplished using MATLAB/Simulink 2014.

Extremum seeking control for UAV close formation flight via improved pigeon-inspired optimization

This paper proposes a comprehensive design scheme for the extremum seeking control(ESC)of the unmanned aerial vehicle(UAV)close formation flight.The proposed design scheme combines a Newton-Raphson method with an extended Kalman filter(EKF)to dynamically estimate the optimal position of the following UAV relative to the leading UAV.To reflect the wake vortex effects reliably,the drag coefficient induced by the wake vortex is considered as a performance function.Then,the performance function is parameterized by the first-order and second-order terms of its Taylor series expansion.Given the excellent performance of nonlinear estimation,the EKF is used to estimate the gradient and the Hessian matrix of the parameterized performance function.The output feedback of the proposed scheme is determined by iterative calculation of the Newton-Raphson method.Compared with the traditional ESC and the classic ESC,the proposed design scheme avoids the slow continuous time integration of the gradient.This allows a faster convergence of relative position extremum.Furthermore,the proposed method can provide a smoother command during the seeking process as the second-order term of the performance function is taken into account.The convergence analysis of the proposed design scheme is accomplished by showing that the output feedback is a supermartingale sequence.To improve estimation performance of the EKF,a improved pigeon-inspired optimization(IPIO)is proposed to automatically tune the noise covariance matrix.Monte Carlo simulations for a three-UAV close formation show that the proposed design scheme is robust to the initial position of the following UAV.

重载机车无模型黏着预测控制研究

重载机车牵引动力的有效发挥依赖于轮对和钢轨接触时形成的黏着力,然而,由于重载机车黏着过程动力学非线性强、变量耦合多、最优蠕滑速度检测难,导致传统控制方法难以对黏着进行高效控制。针对该问题,提出一种无模型黏着集成预测控制方法。首先,分析传统无模型方法存在的问题,建立重载机车蠕滑速度新型扩张超局部模型。其次,设计扩展滑模观测器估计超局部模型中的不确定部分,不同于传统观测方法,该观测器能够“一步式”估算机车当前的黏着系数和蠕滑速度;同时引入一种改进的后调制无稳态振荡极值搜索方法并进行收敛性分析,在有效黏着系数观测的基础上实时寻优期望蠕滑速度。再次,基于最优蠕滑速度极值搜索设计一种离散蠕滑速度预测控制器,避免传统的控制律设计中引入蠕滑速度微分导致的噪声放大问题,并证明闭环控制下的系统稳定性。最后,与传统滑模黏着控制策略进行仿真对比,印证本文所提控制框架能有效降低黏着控制策略设计的复杂度,提升控制转矩的平滑度。在轨面条件突变的情况下,改进无稳态极值搜索方法能够准确搜索适合的最优蠕滑速度,在3种预设工况下都能够准确锁定最优值;离散黏着控制律能够更为精确地控制驱动转矩,与传统的带有蠕滑速度微分的黏着控制律相比极大地降低了转矩抖振。由此可知,该重载机车无模型黏着预测控制方法能够充分发挥黏着系统效能,实现多工况瞬变下的高效黏着控制。

基于极值搜索的柴油机空气系统模型预测控制

针对配备可变截面涡轮增压器(VGT)、高压废气再循环(EGR)和进气节流阀(TVA)的柴油机进气歧管压力、涡前压力和EGR率多目标跟踪控制问题,提出一种权重系数自学习的扩张状态模型预测控制(MPC)算法.首先,基于柴油机的简化运行机理,提出了三入三出线性变参数预测模型;其次,为补偿因模型与发动机之间偏差造成MPC性能劣化问题,将其等效为3个控制通道的总扰动,并采用扩张状态观测器(ESO)进行快速在线观测和补偿;最后,引入极值搜索算法,对MPC权重系数进行缓慢优化整定,仿真结果表明:在1800 r/min油门开度以最大开度10%的步长向上和向下连续阶跃工况下,对比涡前压力控制,瞬态上升时间相同时本算法超调量比较参数全局优化的比例-积分-微分控制(PID)算法降低30.7%.台架试验中,世界统一瞬态循环(WHTC)动态驾驶循环中进气歧管压力和EGR率跟踪误差IAE(绝对误差积分)值比较传统PID算法分别降低28.3%、17.5%.

基于扰动补偿的高速列车自适应滑模黏着控制

列车安全和平稳运行依赖于牵引力的有效发挥,而牵引力的有效发挥受限于轮轨间的黏着状态。为了解决高速列车行驶在低黏着轨面时牵引力不足的问题,考虑到车重、阻力、坡度等参数不易精确获取,设计一种基于扰动补偿的自适应滑模黏着控制算法。根据黏着计算模型和简化列车模型,建立高速列车轮轨动力学模型。通过设计动态观测精度更高的鲁棒黏着观测器,以精确获取当前轨面提供的黏着力。在此基础上,针对列车工作在最优黏着点附近时能够获得较大牵引力这一特性,采用梯度观测极值搜索算法获取该点附近的蠕滑速度。同时,设计基于扰动补偿的自适应滑模控制器控制电机输出转矩,使高速列车能够在部分系统参数未知的情况下跟踪极值搜索算法输出的蠕滑速度,从而提高列车牵引力。基于扰动补偿的自适应滑模控制,通过观测补偿系统不确定部分和自适应调节开关增益,减少滑模控制律抖振对黏着控制性能的影响。研究结果表明,基于扰动补偿的自适应滑模黏着控制算法在系统中存在未知参数时,能够控制列车在10 s内到达冰雪轨面最优黏着工作点附近,在1 s内到达潮湿轨面最优黏着工作点附近。此外,相比于传统滑模黏着控制和自适应滑模黏着控制,所提算法控制律抖振更低,并且能够更精确地搜寻和跟踪最优黏着工作点。所提算法可以为实际列车黏着控制算法设计提供参考。

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.