Dual Hydraulic Cylinders Synchronized Control Strategy

Relative to the passive suspension, automotive active suspension car driving more ride comfort and stability, has a vital role to further improve the performance of the vehicle. For such a typically complex active suspension system research, the key issue is the selection of control strategies. The problems in the currently active suspension control strategy, the principle of a simple, effective, this paper, a single neuron PID control strategy used in the automotive active suspension system. The results show that compared with other control strategies, single neuron PID control strategy is reliable, has more advantages.

The drive system of shearer traction unit is affected by complex load interference and system structure, which results in the problem of unbalanced power of dual-motor of shearer traction unit. A new type of dual-motor frequency conversion control system for shearer traction unit with “common rectification and dual inverters” is designed. And a dual-motor power balance control method based on particle swarm optimization torque cross-coupled control is proposed. The new frequency conversion control system adopts a parallel structure of two inverter circuits with a common DC bus. Particle swarm optimization is used to optimize the PID control parameters of torque cross-coupling control, which solves the problem that it is difficult to gain optimal PID parameters. The control model is established using Matlab/Simulink simulation software. The proposed control method and the master-slave control method are compared by simulation. The results show that the control effect of proposed control method is better synchromization and power balance effect than the master-slave control method. The proposed control method is applied to dual-motor control of shearer traction unit for experimental verification. The results indicate that the proposed power balance control method realizes that the output torque ratio of dual-motor is between 0.9 and 1.1.

A master-slave control method based on the identifications of load force and impedance for Tele-operated hydraulic construction robot system is presented in this paper. The robot system is composed of a hydraulic excavator and two joysticks. The hydraulic excavator is served as the construction robot. The joysticks are used for operating the robot in a remote region. The master actuator and the slave actuator in this system are the joystick-gear of a joystick and the fork-glove actuator of the excavator respectively. The load force and impedance identifiers are used for identifying the load force and impedance of the slave actuator respectively. According to the load force, the master controller controls the master actuator to generate the reaction force for operator. On the basis of the load impedance and the master slave error, the slave controller controls the slave actuator to perform a working task. The experimental researches confirmed that the operator not only obtains the force sense of the object accurately, but also obtains the deformation and stiffness senses of the object obviously.

The purpose of this research was to develop a control method that would provide an operator with a noticeable and reasonable sense of reaction force during slow grasping of a soft object. We examined a master-slave control system for a teleoperation construction robot comprised of two joysticks as the master that are used to manipulate the object from a remote location, and an excavator with four degrees of freedom consisting of a fork glove, swing, boom, and arm as the slave. In remote control systems, the operator must feel a reasonable sense of force from the fork glove feedback. We previously proposed a variable-gain velocity control system but found that the reaction force was insufficient and often undetectable to operators' sensory receptors in the initial stage of the grasping task, and the reaction force did not appear as a stepwise relation to the increased driving force when grasping a foam block at a slow speed. Based on these earlier problems, we proposed an improved method that provides a noticeable torque and variable gain that changes with the hardness of the task object. Its effectiveness was verified by a concrete block, tire, and sponge foam block grasping experiment.

In this paper, a combination ANN/Fuzzy technique is used to design a Novel Fuzzy Single Neuron PID (NFSNPID) controller to achieve high performance brushless DC motor. The design steps include two parts. The first part uses the genetic algorithm (GA) to find the optimum parameters of Single Neuron PID (SNPID) controller, while the former deals with the design of fuzzy logic control to update the weights of SNPID control online. To demonstrate the designed controller effectiveness, a comparative study is made with between the NFSNPID, Conventional Fuzzy Single Neuron PID CFSNPID and SNPID. All controllers were used to drive, separately, the brushless DC motor against the sudden change of load and operating speed. The performed simulations show better results that motivate for further investigations.

This study mainly focuses on the synchronization problems of whole-body CT scanner. A master-slave synchronous control method is used to achieve synchronism between the gantry rotation motor and the bed moving motor in helical scanning mode, while the cascade control method is used to enhance the stability of each motor. In order to keep synchronous control, a control law is designed by the eigenstructure assignment method, which insures the accuracy of the signal tracking of the periodic load disturbance during the dual-motor synchronous control, and eliminates the adverse effects brought by periodic load. The proposed algorithm is simulated on MATLAB, and compared with the traditional PID algorithm. In addition, the algorithm is implemented on ARM9 and evaluated on a realistic synchronization control experimental platform designed by us. Both the simulation and realistic experimental results indicate that the algorithm is feasible for helical scanning.

In order to achieve simple and effective control for the heating boiler, a single neuron PID controller is used in the burden control system of the heating boiler to deal with the feedback information and the quantity of fuel entering boiler. The controller can realize online real-time adjustment of the controller parameters according to the target function. The simulation results verify that, using the single neuron PID controller, the burden control system of the heating boiler not only gains the better control performance, but also acquires stronger adaptability to math model.

To improve the dynamic performance of voice coil motor, a single position loop control method based on active disturbance rejection control is proposed in this paper. The feedback law is sufficiently analyzed and well designed considering both sinusoidal input and step reference input. Moreover, stability analysis is conducted on the basis of a state-space model and Routh-Hurwitz criterion, which is proved to be stable and convergent under proper parameter arrangement. Further, simulation and experimental validation indicate that the proposed control strategy has faster step response and better tracking accuracy compared with traditional three-loop PI controller with only one eighth of settling time and less than half of the tracking error compared to PI controller.

Aiming at strongly nonlinear process such as pH process, single neuron PID learning algorithm based on quadratic optimization is proposed. This algorithm does not need to conside the problem of subsection compartmentalization for common PID. The controller based on the algorithm mentioned above solves the problem of bad static state performance for common single neuron PID controller. Simulation control results of a strong nonlinear pH process show that this method has better adaptation and anti-disturbance ability than the general algorithm.

A hybrid aquatic-aerial vehicle is a special equipment with the ability to perform amphibious operations in water and air, and its control accuracy and stability during the water outflow process are crucial for ensuring the safety and operational efficiency of the vehicle. This article first constructs the kinematic and dynamic models of the hybrid aquatic-aerial vehicle and designs the basic control strategy based on a PID controller. Given the limitations of a single PID control strategy in specific contexts, this study proposes a PID controller based on genetic algorithm optimization and introduces a simulated annealing algorithm in the optimization process of the genetic algorithm to make the adjustment of controller parameters more precise and efficient. Through simulation verification, it was found that the optimized PID controller improved the performance of the aircraft during the outflow stage, indicating that this optimization control strategy has a positive impact on improving the operational performance of the aircraft.

In this article, two power factor corrector (PFC) control strategies for a bidirectional inductive power transfer (IPT) system are proposed. These control strategies are presented for a novel power circuit without input and output interfaces for a wireless electric vehicle battery charger application. This compact topology comprises: unfolding rectifier, primary resonant bridge, and secondary-side active rectification. Two PFC controls are described in detail: a primary-side PFC control, performed in the primary resonant bridge; and a secondary-side PFC control, implemented in the secondary-side active rectification stage. Both strategies are based on a duty-cycle control operating close to the resonance frequency, integrating different functionalities, i.e., PFC, current shaping (CS), and power control on a single control strategy. We analyze each control strategy, evaluating them for different operating points of the charging process. The performance of the wireless charger applying both control strategies is evaluated in terms of power losses, power factor, harmonic distortion, and bifurcation. Additionally, the theoretical results are validated using a GaN-based experimental prototype. The presented analysis and experimental results clearly identify the advantages and limitations of each control strategy leaving no doubt about their usefulness for the future IPT systems.

The control strategies of Switched Reluctance Motor (SRM) can be divided into current chopper control (CCC) angle position control (APC) and pulse width modulation (PWM). Because these methods have their advantages and disadvantages, a single control strategy is difficult to meet the needs. The paper applies current chopper control in low-speed, and adopts angle position control in high-speed. Using a three-phase 6/4 switched reluctance motor as the research object, to verify the control strategy is feasible and superior by MATLAB/simulink.

Starting in the rotating condition is an important technology for induction motor (IM) speed sensorless control. The key of this technology is that the initial speed of the IM in the rotating condition should be estimated quickly and accurately. In this article, an integral sliding mode control method based on the nonlinear model of the rotor back electromotive force is designed to start a speed sensorless controlled IM in the rotating condition. The stability is guaranteed by Lyapunov stability analysis, and the robustness to rotor time constant and disturbance are analyzed. Meanwhile, in comparison with the proposed control method, the existing input–output feedback linearization (IOFL) control method is introduced and the stability and robustness are also analyzed. Compared with the existing IOFL control method, the proposed control method can estimate the initial speed in the rotating condition without overshoot and has better dynamic performance and robustness. In addition, the proposed control method can be applied not only to the restart operation but also to the normal operation. Only a single control strategy is required from the restart operation to the normal operation. The proposed method is verified by simulation and experiments using a 7.5 kW IM.

Rotavirus is a leading cause of severe diarrhea in children in both developed and developing countries and is estimated to cause over half a million deaths worldwide with much of this mortality burden concentrated in developing countries. Rotavirus spreads easily especially among infants. In this paper, we consider the SVI-B rotavirus model with saturated incidence rate function. Here, S, V, I and B refer to the susceptible individuals, vaccinated individuals, infected individuals, and the pathogen population at time t, respectively. Optimal control is conducted by using three control variables i.e. health education campaigns, effort to restrict direct contact S-I and pathogens eradication during the outbreak of an epidemic. Some results concerning the existence and the characterization of the optimal control will be given. The optimality is taken to be to minimize the total number of infected by the end of the epidemic outbreak. The technical tool is used to determine the optimal strategy by using the Pontryagin Minimum Principle. Numerical simulations are also presented using fourth order Runge-Kutta scheme. Results show that multiple control strategies are more effective than a single control strategy.

At present, China’s traffic signal control machine has a low level of intelligence and a single control strategy. It cannot make corresponding control according to the actual traffic situation, and its ability to direct traffic flow in a reasonable and orderly manner is low. In order to understand the urban rail transit signal and control system, we analyzed the requirements of the train dispatching subsystem, designed the overall architecture of the system from the perspective of function realization and architecture, and constructed the wireless sensor network of the system, which is the best for other experts. In this paper, combined with the research of related technologies of the Internet of Things (IoT), an intelligent traffic signal control machine is designed, and the traffic signal control effects under different algorithms are compared, and the relevant rail transit conditions are statistically studied. Studies have proved that sensors based on IoT technology can effectively improve the intensity and control effect of urban rail transit signals. Compared with other algorithm technologies, the overall score of the sensor algorithm is higher than other algorithms, and the ratio is about 30% higher. This article realizes the maintenance function of various data on the system simulation operation terminal, and builds the overall framework of the system; realize the main functions of the train dispatching operation terminal, including the realization of train dispatching functions such as station map, manual route arrangement, automatic route triggering, station deduction and station jump settings, and log report generation. This shows that the sensor algorithm under the IoT has a great promotion effect on the urban rail transit signal and control system.