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.

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.

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.

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.

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.

MULTIPLE ELECTRONIC CONTROL UNITS CALIBRATION SYSTEM BASED ON EXPLICIT CALIBRATION PROTOCOL AND J1939 PROTOCOL

The rising number of electronic control units （ECUs） in vehicles and the decreasing time to market have led to the need for advanced methods of calibration. A multi-ECU calibration system was developed based on the explicit calibration protocol （XCP） and J1939 communication protocol to satisfy the need of calibrating multiple ECUs simultaneously. The messages in the controller area network （CAN） are defined in the J1939 protocol. Each CAN node can get its own calibration messages and information from other ECUs, and block other messages by qualifying the CAN messages with priority, source or destination address. The data field of the calibration message is designed with the XCP, with CAN acting as the transport layer. The calibration sessions are setup with the event-triggered XCP driver in the master node and the responding XCP driver in the slave nodes. Mirroring calibration variables from ROM to RAM enables the user to calibrate ECUs online. The application example shows that the multi-ECU calibration system can calibrate multiple ECUs simultaneously, and the main program can also accomplish its calculation and send commands to the actuators in time. By the multi-ECU calibration system, the calibration effort and time can be reduced and the variables in ECU can get a better match with the variables of other ECUs.

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.

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.

智能汽车轨迹跟踪多目标显式模型预测控制

针对现有智能汽车轨迹跟踪控制算法难以同时保证跟踪精确性、横向稳定性、舒适性以及控制实时性的问题,提出了一种基于多目标优化和显式模型预测控制理论的轨迹跟踪控制策略(MO-EMPC)。首先,建立考虑跟踪精确性、横向稳定性、舒适性的多目标函数及约束。然后,针对传统MPC控制实时性低的问题,设计基于EMPC的多目标优化轨迹跟踪控制器,通过引入多参数二次规划(MPQP)理论,将反复在线优化求解过程转化为等价的分段仿射系统(PPWA),离线计算得到最优显式控制律以供实时控制调用,减少在线运算时间。最后,基于CarSim/Simlink联合仿真方法,将所设计控制器的轨迹跟踪多目标优化效果与MPC控制效果进行对比验证。研究结果表明,所提出的轨迹跟踪策略在保证良好的跟踪精度前提下,横向稳定性、舒适性方面的表现更优于MPC控制器,且算法在线运行速度提高56.63%。

单步无条件稳定显式结构动力学算法

在结构动力学分析中,数值积分方法通常被用来高效和准确地求解复杂结构系统在动态负载下的响应。基于量纲分析的原理,提出一种求解结构动力学问题的单步无条件稳定显式算法。首先对所构造的关于位移和速度的函数关系式进行无量纲化处理,然后通过算法精度要求、无条件稳定条件和数值阻尼可控条件确定了其最终递推格式,并根据算法最终递推格式分析得出算法耗散与弥散特性、超调和自启动特性和多自由度系统计算步骤,最后通过数值算例验证了算法的收敛精度、可控数值阻尼和计算效率优势。研究结果表明:对于线性和非线性系统,该算法都兼具显式算法的非迭代特性和隐式算法的无条件稳定特性;该算法能实现真正意义上的自启动并且在速度和位移上均无超调;该算法的数值阻尼由单自由参数控制,数值耗散和弥散对系统低模态响应的影响可以忽略不计,与具有代表性的非迭代显式算法KR-α法相比,该算法在引入数值阻尼滤除虚假高频模态的同时更真实地保留了低阶振型的贡献,从而表现出更好的计算精度;该算法在求解非线性结构动力学问题时相较于常用隐式算法有着显著的计算效率优势,并略优于两求解器非迭代显式算法KR-α法。研究结果很有希望成为求解非线性结构动力学问题的最新且更加有效的显式算法。

质子交换膜燃料电池性能优化控制

质子交换膜燃料电池(proton exchange membrane fuel cell,PEMFC)的热管理,是影响PEMFC安全、稳定、高效运行的核心关键因素。该文在分析PEMFC热管理子系统的结构,以及关键组成部件(包括空气压缩机、冷却液循环泵和冷却风扇)的工作原理基础上,首先搭建耦合热管理子系统的PEMFC非线性仿真模型,进一步将其简化处理得到用于控制的线性模型;在此基础上,设计基于卡尔曼滤波的状态观测器,以实现对状态和不可测量扰动的估计,从而消除模型稳态误差;随后设计目标计算器,以净输出功率跟踪误差最小化和系统效率最大化为目标,对PEMFC系统工作状态进行优化;进而基于线性控制模型,设计无偏显式模型预测控制(offset-free explicit model predictive control,offset-free EMPC),对优化工作状态进行跟踪控制;最后,搭建一个软件在环(softwareintheloop,Si L)测试平台,通过与传统MPC控制策略的仿真结果对比可得,无偏EMPC控制策略可以显著将净输出功率的平均跟踪误差绝对值从344.29W减小到82.19W,系统效率从40.32%提升到46.56%,算法平均单步计算耗时从26ms降低到11.8ms,并且可以约束PEMFC系统运行在稳定和安全的工况。

应用显式模型预测控制的永磁同步风电机组频率电压协同调节

为解决高压直流输电线路发生闭锁故障引起送端近区功率冗余、造成频率和电压升高的问题,提出了一种面向频率电压协同调节的永磁同步风电机组的显式模型预测控制(EMPC)策略。该控制策略利用了直驱式永磁同步风力发电机组具有同时调节电网频率和电压的能力。根据频率摆动方程和电压灵敏度模型建立了换流站母线电压和电网频率的状态空间模型;将永磁同步风电机组的有功输出和无功输出相结合,利用多个EMPC控制器计算代价函数下的最优输出并进行组合,计算出故障后永磁同步风电机组的参考功率输出,以实现送端近区频率和电压的协同控制。在MATLAB/Simulink平台进行仿真验证表明:当闭锁故障发生时,EMPC控制策略不仅可以降低送端近区的频率和电压的峰值与稳态值,而且与其他控制方法相比,频率峰值降低了0.089 Hz,电压峰值降低了0.012(标幺值)。该研究可为电力系统的频率电压控制提供理论及工程参考。

基于EMPC的四足机器人控制方法研究

为了提高四足机器人的运动性能和弥补模型预测控制在线计算量大的不足,提出了基于显式模型预测控制(EMPC)理论的四足机器人控制方法。基于动力学和运动学理论建立四足机器人的状态空间模型,并确定目标函数和约束条件;利用EMPC理论,得到显式控制律实现四足机器人的控制。仿真结果表明,所提出的EMPC控制方法减少了在线计算时间,实现了四足机器人足端力控制。

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.

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.