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.

Cavitation Diagnostics Based on Self-Tuning VMD for Fluid Machinery with Low-SNR Conditions

Variational mode decomposition(VMD)is a suitable tool for processing cavitation-induced vibration signals and is greatly affected by two parameters:the decomposed number K and penalty factorαunder strong noise interference.To solve this issue,this study proposed self-tuning VMD(SVMD)for cavitation diagnostics in fluid machinery,with a special focus on low signal-to-noise ratio conditions.A two-stage progressive refinement of the coarsely located target penalty factor for SVMD was conducted to narrow down the search space for accelerated decomposition.A hybrid optimized sparrow search algorithm(HOSSA)was developed for optimalαfine-tuning in a refined space based on fault-type-guided objective functions.Based on the submodes obtained using exclusive penalty factors in each iteration,the cavitation-related characteristic frequencies(CCFs)were extracted for diagnostics.The power spectrum correlation coefficient between the SVMD reconstruction and original signals was employed as a stop criterion to determine whether to stop further decomposition.The proposed SVMD overcomes the blindness of setting the mode number K in advance and the drawback of sharing penalty factors for all submodes in fixed-parameter and parameter-optimized VMDs.Comparisons with other existing methods in simulation signal decomposition and in-lab experimental data demonstrated the advantages of the proposed method in accurately extracting CCFs with lower computational cost.SVMD especially enhances the denoising capability of the VMD-based method.

Experimental investigation of a passive self-tuning resonator based on a beam-slider structure

This work investigates a self-tuning resonator composed of a slender clamped-clamped steel beam and a freely movable slider.The clamped-clamped beam exhibits hardening nonlinearity when it vibrates in large amplitude,providing a broad bandwidth of dynamic response.The moving slider changes the mass distribution of the whole structure,and provides a passive self-tuning approach for capturing the high-energy orbit of the structure.In the case without inclination,adequate inertial force that mainly depends on the vibration amplitude of the beam and the position of the slider can drive the slider to move from the side toward the centre of the beam.This movement amplifies the beam response when the excitation frequency is below 37 Hz in our prototyped device.In the multi-orbit frequency range(28-37 Hz),the self-tuning and magnification of beam response can be achieved when the slider is initially placed in an appropriate position on the beam.Once the beam is disturbed,however,the desired response in the high-energy orbit can be lost easily and cannot be reacquired without external assistance.In an improved design with a small inclination,the introduced small gravitational component enables the slider to move from the higher side toward the lower side when the beam amplitude is small.This property sacrifices the less efficient self-tuning region below 25 Hz,but can enable the beam to acquire and maintain the high-energy orbit response in the multi-orbit frequency range(28-39 Hz),which is resistant to disturbance.The proposed resonator in this paper not only broadens the frequency bandwidth of dynamic response,but also enables capture and maintenance of the high-energy orbit in a completely passive way.Such a passive self-tuning structure presents an advantage in the design of broadband vibration energy-harvesting systems.

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.

SELF-TUNING WEIGHTED MEASUREMENT FUSION WHITE NOISE DECONVOLUTION ESTIMATOR

For the multi-sensor linear discrete time-invariant stochastic systems with correlated measurement noises and unknown noise statistics,an on-line noise statistics estimator is obtained using the correlation method.Substituting it into the optimal weighted fusion steady-state white noise deconvolution estimator based on the Kalman filtering,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the Dynamic Error System Analysis(DESA) method,it proved that the self-tuning fusion white noise deconvolution estimator converges to the steady-state optimal fusion white noise deconvolution estimator in a realization.Therefore,it has the asymptotically global optimality.A simulation example for the tracking system with 3 sensors and the Bernoulli-Gaussian input white noise shows its effectiveness.

Self-tuning weighted measurement fusion Kalman filter and its convergence

For multisensor systems,when the model parameters and the noise variances are unknown,the consistent fused estimators of the model parameters and noise variances are obtained,based on the system identification algorithm,correlation method and least squares fusion criterion.Substituting these consistent estimators into the optimal weighted measurement fusion Kalman filter,a self-tuning weighted measurement fusion Kalman filter is presented.Using the dynamic error system analysis （DESA） method,the convergence of the self-tuning weighted measurement fusion Kalman filter is proved,i.e.,the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a realization.Therefore,the self-tuning weighted measurement fusion Kalman filter has asymptotic global optimality.One simulation example for a 4-sensor target tracking system verifies its effectiveness.

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.

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.

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.

Self-tuning measurement fusion white noise deconvolution estimator with correlated noises

For the multisensor linear discrete time-invariant stochastic systems with correlated noises and unknown noise statistics,an on-line noise statistics estimator is presented by using the correlation method.Substituting it into the steady-state Riccati equation,the self-tuning Riccati equation is obtained.Using the Kalman filtering method,based on the self-tuning Riccati equation,a self-tuning weighted measurement fusion white noise deconvolution estimator is presented.By the dynamic error system analysis（DESA） method,it is proved that the self-tuning fusion white noise deconvolution estimator converges to the optimal fusion steadystate white noise deconvolution estimator in a realization,so that it has the asymptotic global optimality.A simulation example for Bernoulli-Gaussian input white noise shows its effectiveness.

Fuzzy-PID在反重力铸造液面加压控制系统中的应用

由于反重力铸造过程的时滞性、不确定性、复杂性,难以建立精确的数学模型。因此采用不依赖于数学模型的PID控制算法,而传统PID存在动态性能差、稳定恢复时间长、容易出现超调的缺点。为了实现对复杂系统的精确控制,引入Fuzzy控制,将二者结合起来,设计了Fuzzy-PID混合控制算法。该控制算法继承了PID控制无静差、静态性能好的特点,同时又兼有Fuzzy控制适应能力强,动态性能好的优势。实践证明,该控制算法对参数变化适应性好,抗干扰能力强,适合应用在反重力铸造液面加压控制系统中,并取得了良好的控制效果。

基于Fuzzy-PID的PZT微纳扫描控制算法

针对压电陶瓷的特性,提出了基于Fuzzy-PID的复合控制算法。该算法在整个过程中的不同阶段采用不同的控制方式,即根据误差信号e的大小有选择性的使用模糊控制或PID控制,因此不需要建立被控对象的精确数学模型,而且还继承了常规PID控制静差小、静态稳定性好的特点,同时又兼有模糊控制适应能力强、动态性能好的优势。实验中采用此算法获得闭环控制电压最大误差为18mV,置位重复精度为6nm,系统相对误差0.15%;在位移量为10μm的时间-位移阶跃响应曲线中阶越响应时间约为20ms,且在电压为80V的蠕动曲线中,3min内的蠕变为3.6%,3min以后变化开始缓慢。由此可知该控制方法能够有效地降低压电陶瓷非线性所引起的位移误差,提高定位精度,而且还能改善压电陶瓷的蠕动特性。

基于LabVIEW的Fuzzy-PID温度控制系统的设计

针对电子束加热是在真空室中进行,能量密度高、工件温度变化快、控制难度大、数学模型难以建立等特点,提出了系统解决方案及控制机理。利用LabVIEW软件中的模糊逻辑模块(Fuzzy Logic)及仿真模块(Simulation Module)建立系统仿真程序,并通过仿真曲线获得优化参数。实验表明,该控制系统具有动态响应快、调整时间短、稳态误差小、超调量小等特点,从而证明了在以高能密度的电子束为加工热源过程中,采用Fuzzy-PID控制技术对被加工件温度进行控制,可以获得理想的控制效果。

基于Fuzzy-PID的移动机器人运动控制

移动机器人涉及到许多研究方向,运动控制是其中的基础。通过对移动机器人运动学模型进行分析,以足球机器人系统为实验平台,论证了Fuzzy PID技术应用于移动机器人运动控制的可行性。将传统的PID控制与模糊控制相结合,通过PID控制实现控制的准确性,利用模糊控制提高控制的快速性。针对移动机器人运动控制中的实际问题,着重提出了基于误差分区的PID控制器和模糊控制器的设计方法。实验证明该方法不仅增强了控制器的调节能力,还在一定程度上简化了控制器的设计。

预估Fuzzy-PID在中央空调控制系统中的应用

电加热常作为中央空调的末端设备置于被调节房间风道入口处 ,微调加热以提高温度控制精度 ,为此提出一种电加热温度控制方法 ,采用预估Fuzzy PID复合控制算法 ,通过对加热功率的调节进行闭环控制 ,实现控制点温度的稳定控制 .新的控制算法结合了smith预估控制、Fuzzy和PID控制器的优点 .在MATLAB环境中仿真预估Fuzzy PID控制中央空调末端电加热 ,结果表明该控制方法能提高温度稳定性并降低能耗 ,控制精度达到± 0 .1℃ .

Fuzzy-PID切换控制在磁流变半主动悬置系统中的应用

建立了一种新型动力总成磁流变悬置的动力学模型,并对其进行动态特性试验。通过数值拟合方法对试验数据进行拟合,得到了电流与可控阻尼力的关系式,为磁流变悬置的控制应用提供了理论模型。考虑车辆悬架系统的等效刚度和阻尼,建立了磁流变半主动悬置系统模型。设计了磁流变半主动悬置Fuzzy-PID切换控制系统,对不同工况下磁流变半主动悬置系统的隔振特性进行了仿真研究,结果表明应用Fuzzy-PID切换控制的磁流变半主动悬置系统具有良好的隔振效果。

基于Fuzzy-PID控制的柴油发电机调频研究

频率稳定性是柴油发电机组运行的重要指标。传统柴油机电子调速仅依靠调速器控制频率,很难保证频率达到最优调节。本文采用Fuzzy-PID控制器,参数自整定为基础的复合控制,仿真结果表明,该控制器能够大大提高系统稳定性和鲁棒性。