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 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.展开更多
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...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.展开更多
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 n...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.展开更多
Fuzzy logic has attracted the attention of structural control engineers during the last few years,because fuzzy logic can handle nonlinearities,uncertainties,and heuristic knowledge effectively and easily.In this pape...Fuzzy logic has attracted the attention of structural control engineers during the last few years,because fuzzy logic can handle nonlinearities,uncertainties,and heuristic knowledge effectively and easily.In this paper,a self-Tuning fuzzy-PID control method which used the technology of the fuzzy control and PID control unified is presented.These techniques can visualize the results and processes for structure stress.These techniques will also provide convenience for engineers and users,and have high practical values.The MATLAB simulation result shows that the system precision and the efficiency are very high and the static error is small,and robustness was also validated.展开更多
Interacting The highest storage capacity of a circular tank makes it pop-ular in process industries.Because of the varying surface area of the cross-sec-tions of the tank,this two-tank level system has nonlinear chara...Interacting The highest storage capacity of a circular tank makes it pop-ular in process industries.Because of the varying surface area of the cross-sec-tions of the tank,this two-tank level system has nonlinear characteristics.Controlling theflow rate of liquid is one of the most difficult challenges in the production process.This proposed effort is critical in preventing time delays and errors by managing thefluid level.Several scholars have explored and explored ways to reduce the problem of nonlinearity,but their techniques have not yielded better results.Different types of controllers with various techniques are implemented by the proposed system.Sliding Mode Controller(SMC)with Fractional Order PID Controller based on Intelligent Adaptive Neuro-Fuzzy Infer-ence System(ANFIS)is a novel technique for liquid level regulation in an inter-connected spherical tank system to avoid interferences and achieve better performance in comparison of rise time,settling time,and overshoot decrease.Evaluating the simulated results acquired by the controller yields the efficiency of the proposed system.The simulated results were produced using MATLAB 2018 and the FOMCON toolbox.Finally,the performance of the conventional controller(FOPID,PID-SMC)and proposed ANFIS based SMC-FOPID control-lers are compared and analyzed the performance indices.展开更多
Two novel improved variants of reptile search algorithm(RSA),RSA with opposition-based learning(ORSA)and hybrid ORSA with pattern search(ORSAPS),are proposed to determine the proportional,integral,and derivative(PID)c...Two novel improved variants of reptile search algorithm(RSA),RSA with opposition-based learning(ORSA)and hybrid ORSA with pattern search(ORSAPS),are proposed to determine the proportional,integral,and derivative(PID)controller parameters of an automatic voltage regulator(AVR)system using a novel objective function with augmented flexibility.In the proposed algorithms,the opposition-based learning technique improves the global search abilities of the original RSA algorithm,while the hybridization with the pattern search(PS)algorithm improves the local search abilities.Both algorithms are compared with the original RSA algorithm and have shown to be highly effective algorithms for tuning the PID controller parameters of an AVR system by getting superior results.Several analyses such as transient,stability,robustness,disturbance rejection,and trajectory tracking are conducted to test the performance of the proposed algorithms,which have validated the good promise of the proposed methods for controller designs.The performances of the proposed design approaches are also compared with the previously reported PID controller parameter tuning approaches to assess their success.It is shown that both proposed approaches obtain excellent and robust results among all compared ones.That is,with the adjustment of the weight factorα,which is introduced by the proposed objective function,for a system with high bandwitdh(α=1),the proposed ORSAPS-PID system has 2.08%more bandwidth than the proposed ORSA-PID system and 5.1%faster than the fastest algorithm from the literature.On the other hand,for a system where high phase and gain margins are desired(α=10),the proposed ORSA-PID system has 0.53%more phase margin and 2.18%more gain margin than the proposed ORSAPS-PID system and has 0.71%more phase margin and 2.25%more gain margin than the best performing algorithm from the literature.展开更多
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 ha...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.展开更多
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.Co...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.展开更多
基金financially supported by the Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City(Grant No.2021JJLH0078)the Science and Technology Commission of Shanghai Municipality (Grant No.19DZ1207300)the Major Projects of Strategic Emerging Industries in Shanghai。
文摘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.
文摘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.
文摘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.
基金supported by Chongqing Bureau of Foreign Experts Affairs ( Project number:20075000028)
文摘Fuzzy logic has attracted the attention of structural control engineers during the last few years,because fuzzy logic can handle nonlinearities,uncertainties,and heuristic knowledge effectively and easily.In this paper,a self-Tuning fuzzy-PID control method which used the technology of the fuzzy control and PID control unified is presented.These techniques can visualize the results and processes for structure stress.These techniques will also provide convenience for engineers and users,and have high practical values.The MATLAB simulation result shows that the system precision and the efficiency are very high and the static error is small,and robustness was also validated.
文摘Interacting The highest storage capacity of a circular tank makes it pop-ular in process industries.Because of the varying surface area of the cross-sec-tions of the tank,this two-tank level system has nonlinear characteristics.Controlling theflow rate of liquid is one of the most difficult challenges in the production process.This proposed effort is critical in preventing time delays and errors by managing thefluid level.Several scholars have explored and explored ways to reduce the problem of nonlinearity,but their techniques have not yielded better results.Different types of controllers with various techniques are implemented by the proposed system.Sliding Mode Controller(SMC)with Fractional Order PID Controller based on Intelligent Adaptive Neuro-Fuzzy Infer-ence System(ANFIS)is a novel technique for liquid level regulation in an inter-connected spherical tank system to avoid interferences and achieve better performance in comparison of rise time,settling time,and overshoot decrease.Evaluating the simulated results acquired by the controller yields the efficiency of the proposed system.The simulated results were produced using MATLAB 2018 and the FOMCON toolbox.Finally,the performance of the conventional controller(FOPID,PID-SMC)and proposed ANFIS based SMC-FOPID control-lers are compared and analyzed the performance indices.
文摘Two novel improved variants of reptile search algorithm(RSA),RSA with opposition-based learning(ORSA)and hybrid ORSA with pattern search(ORSAPS),are proposed to determine the proportional,integral,and derivative(PID)controller parameters of an automatic voltage regulator(AVR)system using a novel objective function with augmented flexibility.In the proposed algorithms,the opposition-based learning technique improves the global search abilities of the original RSA algorithm,while the hybridization with the pattern search(PS)algorithm improves the local search abilities.Both algorithms are compared with the original RSA algorithm and have shown to be highly effective algorithms for tuning the PID controller parameters of an AVR system by getting superior results.Several analyses such as transient,stability,robustness,disturbance rejection,and trajectory tracking are conducted to test the performance of the proposed algorithms,which have validated the good promise of the proposed methods for controller designs.The performances of the proposed design approaches are also compared with the previously reported PID controller parameter tuning approaches to assess their success.It is shown that both proposed approaches obtain excellent and robust results among all compared ones.That is,with the adjustment of the weight factorα,which is introduced by the proposed objective function,for a system with high bandwitdh(α=1),the proposed ORSAPS-PID system has 2.08%more bandwidth than the proposed ORSA-PID system and 5.1%faster than the fastest algorithm from the literature.On the other hand,for a system where high phase and gain margins are desired(α=10),the proposed ORSA-PID system has 0.53%more phase margin and 2.18%more gain margin than the proposed ORSAPS-PID system and has 0.71%more phase margin and 2.25%more gain margin than the best performing algorithm from the literature.
文摘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.
文摘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.
文摘针对观察型水下机器人在水下运动时易受暗流、波浪影响,造成操控困难、系统稳定性差等问题,建立遥控水下机器人(Remotely Operated Vehicle,ROV)不同运动的控制模型,考虑电机和导管螺旋桨推进器的传递函数对ROV控制系统的影响,确定定艏向和定深控制系统的闭环传递函数,结合模糊控制和比例积分微分(Proportional Integral Differential,PID)控制法,得到模糊PID控制器,基于MATLAB/Simulink环境进行ROV定深度运动仿真和ROV水平面艏向定偏角运动仿真。结果表明,与传统PID控制相比,模糊PID控制具有更优的ROV定艏向和定深度控制效果,不会发生超调现象,在抗干扰能力和响应速度方面具有明显的优势,可有效地实现ROV定艏向和定深度运动控制。