fuzzy control

摘要： The driver-automation shared driving is a transition to fully-autonomous driving,in which human driver and vehicular controller cooperatively share the control authority.This paper investigates the shared steering control of semi-autonomous vehicles with uncertainty from imprecise parameter.By considering driver’s lane-keeping behavior on the vehicle system,a driver-automation shared driving model is introduced for control purpose.Based on the interval type-2(IT2)fuzzy theory,moreover,the driver-automation shared driving model with uncertainty from imprecise parameter is described using an IT2 fuzzy model.After that,the corresponding IT2 fuzzy controller is designed and a direct Lyapunov method is applied to analyze the system stability.In this work,sufficient design conditions in terms of linear matrix inequalities are derived,to guarantee the closed-loop stability of the driver-automation shared control system.In addition,an H∞performance is studied to ensure the robustness of control system.Finally,simulation-based results are provided to demonstrate the performance of proposed control method.Furthermore,an existing type-1 fuzzy controller is introduced as comparison to verify the superiority of the proposed IT2 fuzzy controller.

摘要： Edge Computing is a new technology in Internet of Things(IoT)paradigm that allows sensitive data to be sent to disperse devices quickly and without delay.Edge is identical to Fog,except its positioning in the end devices is much nearer to end-users,making it process and respond to clients in less time.Further,it aids sensor networks,real-time streaming apps,and the IoT,all of which require high-speed and dependable internet access.For such an IoT system,Resource Scheduling Process(RSP)seems to be one of the most important tasks.This paper presents a RSP for Edge Computing(EC).The resource characteristics are first standardized and normalized.Next,for task scheduling,a Fuzzy Control based Edge Resource Scheduling(FCERS)is suggested.The results demonstrate that this technique enhances resource scheduling efficiency in EC and Quality of Service(QoS).The experimental study revealed that the suggested FCERS method in this work converges quicker than the other methods.Our method reduces the total computing cost,execution time,and energy consumption on average compared to the baseline.The ES allocates higher processing resources to each user in case of limited availability of MDs;this results in improved task execution time and a reduced total task computation cost.Additionally,the proposed FCERS m 1m may more efficiently fetch user requests to suitable resource categories,increasing user requirements.

摘要： Double-column bridge piers are prone to local damage during earthquakes,leading to the destruction of bridges.To improve the earthquake resistance of double-column bridge piers,a novel swing column device(SCD),consisting of a magnetorheological(MR)damper,a current controller,and a swing column,was designed for the present work.To verify the seismic energy dissipation ability of the SCD,a lumped mass model for a double-column bridge pier with the SCD was established according to the low-order modeling method proposed by Steo.Furthermore,the motion equation of the double-column bridge pier with the SCD was established based on the D′Alembert principle and solved with the use of computational programming.It was found that the displacement response of the double-column bridge pier was effectively controlled by the SCD.However,due to rough current selection and a time delay,there is a significant overshoot of the bridge acceleration using SCD.Hence,to solve the overshoot phenomenon,a current controller was designed based on fuzzy logic theory.It was found that the SCD design based on fuzzy control provided an ideal shock absorption effect,while reducing the displacement and acceleration of the bridge pier by 36.43%‒40.63%and 30.06%‒33.6%,respectively.

摘要： The length of fexible manipulators with a telescopic arm alters during movement.The dynamic parameters of telescopic fexible manipulators exhibit signifcant time-varying characteristics owing to variations in length.With an increase in the manipulators’length,the nonlinear terms caused by fexibility in the manipulators’dynamic equations cannot be ignored.The time-varying characteristics and nonlinear terms of telescopic fexible manipulators cause fuctuations in rotation angles,which afect the operation accuracy of end-efectors.In this study,a control strategy based on a combination of fuzzy adjustment and an RBF neural network is utilized to improve the control accuracy of fexible telescopic manipulators.First,the dynamic equation of the manipulators is established using the assumed mode method and Lagrange’s principle,and the infuence of nonlinear terms is analyzed.Subsequently,a combined control strategy is proposed to suppress the fuctuation of the rotation angle in telescopic fexible manipulators.The variation ranges of the feedforward PD controller parameters are determined by the pole placement strategy and length of the manipulators.Fuzzy rules are utilized to adjust the controller parameters in real-time.The RBF neural network is utilized to identify and compensate the uncertain part of the dynamic model of the fexible manipulators.The uncertain part comprises time-varying parameters and nonlinear terms.Finally,numerical simulations and prototype experiments prove the efectiveness of the combined control strategy.The results prove that the proposed control strategy has a smaller standard deviation of errors.Therefore,the combined control strategy is more suitable for telescopic fexible manipulators,which can efectively improve the control accuracy of rotation angles.

摘要： Mechanical ventilation is an effective medical means in the treatment of patients with critically ill,COVID-19 and other pulmonary diseases.During the mechanical ventilation and the weaning process,the conduct of pulmonary rehabilitation is essential for the patients to improve the spontaneous breathing ability and to avoid the weakness of respiratory muscles and other pulmonary functional trauma.However,inappropriate mechanical ventilation strategies for pulmonary rehabilitation often result in weaning difficulties and other ventilator complications.In this article,the mechanical ventilation strategies for pulmonary rehabilitation are studied based on the analysis of patient-ventilator interaction.A pneumatic model of the mechanical ventilation system is established to determine the mathematical relationship among the pressure,the volumetric flow,and the tidal volume.Each ventilation cycle is divided into four phases according to the different respiratory characteristics of patients,namely,the triggering phase,the inhalation phase,the switching phase,and the exhalation phase.The control parameters of the ventilator are adjusted by analyzing the interaction between the patient and the ventilator at different phases.A novel fuzzy control method of the ventilator support pressure is proposed in the pressure support ventilation mode.According to the fuzzy rules in this research,the plateau pressure can be obtained by the trigger sensitivity and the patient’s inspiratory effort.An experiment prototype of the ventilator is established to verify the accuracy of the pneumatic model and the validity of the mechanical ventilation strategies proposed in this article.In addition,through the discussion of the patient-ventilator asynchrony,the strategies for mechanical ventilation can be adjusted accordingly.The results of this research are meaningful for the clinical operation of mechanical ventilation.Besides,these results provide a theoretical basis for the future research on the intelligent control of ventilator and the automation of weaning process.

摘要： To solve the problem of self-balancing two-wheeled vehicle, this article presents double cascade PID control algorithm. This method reduces the coupling of balance control, speed control and direction control, because of the special system structure. This article successfully solved the sensor fusion of gyroscope and accelerometer by using Kalman filtering algorithm, and adding in fuzzy PID algorithm to improve the flexibility of the steering system, thus greatly improving the accuracy and response rate of the system.

摘要： The inverted pendulum is a classic problem in dynamics and control theory and is widely used as a benchmark for testing control algorithms. This paper studies the use of fuzzy control method to study the stability control problem of a triple inverted pendulum system. By the linear model of the system, the feedback weight matrix of the LQR optimal control and the feedback parameters of the linear optimal control are designed to determine the parameters of the fuzzy controller. The simulation results show that the proposed method can achieve the stability control of the three stage inverted pendulum, and has good dynamic performance with simple parameter selection.

摘要： The control strategy is presented using passive and active hybrid magnetically suspended flywheels(P&A MSFWs),which can help meet the requirements of high precision and high stability for earth-observation satellites.Compared with the conventional flywheel,P&A MSFW has more rotation degrees of freedom(DOFs)since the rotor is suspended by magnetic bearings,and thus requires more efficient controllers.A modified sliding mode control law(SMC)to our novel nonlinear and coupled system is presented,which is interrupted by inertia matrix uncertainties and external disturbances.SMC law via Lyapunov method is improved,and a fuzzy control scheme is used to attenuate the chatting and control attitude accuracy and maintain the robustness of SMC.Simulation results are provided to illustrate the efficiency of our model by using our control law.

摘要： A directly adaptive fuzzy algorithm is applied in vehicle adaptive cruise control system. The basic principle of the adaptive fuzzy algorithm is analyzed. The initial value of the fuzzy based vector is given by the traditional fuzzy membership. Adaptive law of the adjustable parameters θ is also determined. The directly adaptive fuzzy ACC controller is designed based on Matlab fuzzy toolbox. Matlab-Simulink is adopted to test the function of the adaptive fuzzy ACC controller. The control system is established using a 7 DOF vehicle dynamics model. Simulation results indicate that the principle of the method is correct and it performs well both in cruise and distance keeping.

摘要： The basic idea of fuzzy mathematics and theoretical control methods. In the traditional areas ofcontrol, control system dynamics model is accurate or not affect the merits of the most important key to con-trolling the system dynamics more detailed information, you can achieve more precise control. However, forcomplex systems, because too many variables, often difficult to correctly describe the dynamics of the system,so engineers will use a variety of ways to simplify the system dynamics, in order to achieve the purpose ofcontrol, but less than ideal. In other words, the traditional control theory, a clear system for strong control, buttoo complex or difficult to accurately describe the system, it appears helpless. So they try to deal problemswith fuzzy control.

摘要： The basic idea of fuzzy mathematics and theoretical control methods. In the traditional areas ofcontrol, control system dynamics model is accurate or not affect the merits of the most important key to con-trolling the system dynamics more detailed information, you can achieve more precise control. However, forcomplex systems, because too many variables, often difficult to correctly describe the dynamics of the system,so engineers will use a variety of ways to simplify the system dynamics, in order to achieve the purpose ofcontrol, but less than ideal. In other words, the traditional control theory, a clear system for strong control, buttoo complex or difficult to accurately describe the system, it appears helpless. So they try to deal problemswith fuzzy control.

摘要： The basic idea of fuzzy mathematics and theoretical control methods. In the traditional areas ofcontrol, control system dynamics model is accurate or not affect the merits of the most important key to con-trolling the system dynamics more detailed information, you can achieve more precise control. However, forcomplex systems, because too many variables, often difficult to correctly describe the dynamics of the system,so engineers will use a variety of ways to simplify the system dynamics, in order to achieve the purpose ofcontrol, but less than ideal. In other words, the traditional control theory, a clear system for strong control, buttoo complex or difficult to accurately describe the system, it appears helpless. So they try to deal problemswith fuzzy control.