Fuzzy Proportion Integration Differentiation Research Articles

Research on Automatic Train Driving Based on Fuzzy Proportion Integration Differentiation Iterative Control

Automatic train driving system is an important subsystem of train operation control system, which can provide passengers with punctual, accurate, efficient and fast transportation services. At the same time, the accurate stop, comfort and stability of the train is an important index to measure the control performance of the train automatic driving system, and the accurate stop plays a vital role in the efficient operation of the train. Based on the characteristics of high-speed train parking, an accurate parking algorithm based on fuzzy PID iterative control was proposed to solve the problem of low parking accuracy caused by frequent switching of control output. On the basis of solving the differential equation of the train braking model, the gradient of the system is obtained, and then the learning parameters of the convergence condition are obtained to overcome the repeated uncertainty in the stopping stage. The simulation results show that the fuzzy PID iterative control for asymptotic stability is an effective method to realize the precise parking of trains, and has strong robustness against the train parameter uncertainties and external disturbances.

Analysis and control for a new reconfigurable parallel mechanism

This article proposes a new reconfigurable parallel mechanism using a spatial overconstrained platform. This proposed mechanism can be used as a machine tool. The mobility is analyzed by Screw Theory. The inverse kinematic model is established by applying the closed-loop equation. Next, the dynamic model of the presented mechanism is established by Lagrange formulation. To control the presented mechanism, some controllers have been used. Based on this dynamic model, the fuzzy-proportion integration differentiation (PID) controller is designed to track the trajectory of the end effector. For each limb, a sliding mode controller is applied to track the position and velocity of the slider. Finally, some simulations using ADAMS and MATLAB are proposed to verify the effectiveness and stability of these controllers.

Mechanism and Position Tracking Control of a Robotic Manipulator Actuated by the Tendon-Sheath

To achieve a manipulator that is lighter and more anthropomorphic, a 7-DOF robotic manipulator driven by the tendon-sheath is designed. Its mechanical system and control system are both introduced in this paper. An encoder is installed on both the driver side and the joint side to realize double closed-loop control. Considering the transmission characteristics of the reducer and the tendon-sheath, a position transmission model of a single joint is constructed. Furthermore, the correctness of the transmission model is verified based on the prototype joint four. A proportional-integral-differential (PID) controller and fuzzy PID controller with time-delay estimation (TDE) are designed based on double encoders, and some experiments on the position tracking control are carried out for the robotic joints. The experimental results show that each joint can rotate normally and there is no interference between the different tendon-sheaths. When the drive motor moves to follow the position command of the sinusoidal signal, the TDE-based fuzzy PID controller can reduce the maximum tracking error compared with the traditional PID controller. According to the experimental results, it is feasible to apply the tendon-sheath transmission to the manipulator, which provides a reference for the development of other equipment based on the tendon-sheath.

Event-Triggered Sliding Mode Control of Fuzzy Systems via Artificial Time-Delay Estimation

In this article, we show that a fuzzy system of relative degree two can be stabilized by proportion-integration-differentiation (PID) sliding mode control depending on the outputs and their derivatives. The main focus of this article is to formulate a novel event-based fuzzy PID sliding surface under the imperfect premise matching. First, the measured output is adequately sampled for frequently checking a delay-dependent event-triggered condition, which reduces the number of data transmissions and controller updates. Then, an artificial time-delay estimator is used to approximate the output derivative. Based on linear matrix inequalities, a heuristic algorithm is investigated to design an event-triggered sliding mode controller, which reveals how to choose the maximum sampling interval that maintains the estimation accuracy and preserves the stability under fast event triggering. In simulation, the effectiveness of the proposed design method is verified in a cart and pendulum system subject to system uncertainties.

Static pressure set-point reset smoothing scheme based-on improved iterative learning control and variable trajectory for air-conditioning system

Variable static pressure is the main strategy for air volume adjustment in variable air volume air-conditioning system and can offer huge energy savings compared with constant air volume systems. When parameter set-points reset with load, dynamic processes are excited. Some processes take a long time to reach a new stable state with large overshoots. Improved iterative learning control (ILC) algorithm is studied to ameliorate transition processes of the static pressure reset control loop. A mathematical model of the static pressure control loop is established, and a new desired trajectory is given to improve the system tracking performance. Simulation and experimental studies are performed on traditional Proportion Integration Differentiation (PID) control, Proportion Differentiation (PD) type ILC, fuzzy gain PD type ILC and variable speed-integral PID type ILC. Results show that the system has the smallest overshoot and the shortest adjustment time with the fuzzy gain PD type ILC. Meanwhile the system can be betterly stabilized near the set-point with smallest evaluation index and the desired trajectory can be tracked with better dynamic performance. Studies show that the dynamic process is smoothed by improved ILC and variable trajectory. It is of great significance to enhance the whole system stability.

Adaptive fuzzy Proportion Integration Differentiation control in hydraulic offshore wind turbine for optimal power extraction based on the estimated wind speed

AbstractIn this paper, a novel fuzzy Proportion Integration Differentiation (PID) controller is proposed to extract the optimal energy from wind for a hydraulic offshore wind turbine. Firstly, in order to calculate the theoretical power, an adaptive neuro‐fuzzy inference system (ANFIS) is designed for wind speed evaluation, in which the input parameters include rotor speed, rotor torque, and pitch angle, and the output parameter is the estimated wind speed. Then, the optimal power coefficient is achieved by controlling the hydraulic pump torque to tracking the optimal rotor speed, and a fuzzy PID is designed to adjust the pump displacement and to obtain the optimal hydraulic pump torque. Furthermore, aiming to meet the various output power demand, a hydraulic accumulator is utilized for storing the excess energy, and a PID controller is designed for distributing the hydraulic energy between the accumulator and the Pelton turbine. Finally, the performance of proposed controls system are confirmed by two case simulations on a 5 MW hydraulic offshore wind turbine. The simulation results show that, compared with the conventional control methods (fuzzy logical and PID controller) used in electro‐hydraulic servo system, the fuzzy PID controller can provide the better tracking effect with a faster speed and a minimal steady‐state oscillation. Moreover, with the use of a hydraulic accumulator, all the power fluctuations from the wind speed disturbances are damped out and the output power become controllable.

Fuzzy controller design of micro-unmanned helicopter relying on improved genetic optimization algorithm

In this paper, the dynamic model of unmanned helicopter is modeled by frequency domain identification method. An adaptive fuzzy Proportion Integration Differentiation (PID) controller is established, with its design carried out from the aspects of the input and output fuzzy subset, the membership function selection, the fuzzy rule generation, as well as the fuzzy reasoning. An improved genetic algorithm is employed to optimize the initial expert empirical fuzzy rules, which avoids the traditional method from falling into the local optimal solution in the process of optimization. Specifically, adaptive crossover and mutation probability are adopted to accelerate the convergence speed of the genetic algorithm. We then apply the proposed algorithm to optimize the membership function and fuzzy control rules of the fuzzy controller. Simulation results indicate that our adaptive fuzzy PID has better control effect and anti-interference ability than the traditional PID control law. Additionally, our adaptive fuzzy PID controller is verification by flight test thought AF25B unmanned helicopter platform.

Investigation on sticking for drill rod based on finite element analysis and fuzzy proportion–integration–differentiation

In order to investigate the essence of sticking for drill rod served in underground coal mine, a comprehensive solution combing finite element analysis with fuzzy proportion–integration–differentiation on the drilling rig is proposed. First, the structure and working mechanism of the drilling rig are studied; then, the static and modal analyses on the drill rod with finite element analysis are performed according to the actual working conditions of the drilling rig. Furthermore, a load-sensing hydraulic power system of the drilling rig is built, and the co-simulation with AMESim and Simulink is performed on the response, overshoot, and vibration, when the hydraulic power is controlled with the conventional control method, proportion–integration–differentiation control method, and fuzzy proportion–integration–differentiation method, respectively. The simulation results show that the strength, stiffness, and vibration frequency of the drilling rig model meet the working requirements, and no resonance occurs; furthermore, the load-sensing hydraulic power system with fuzzy proportion–integration–differentiation method is preferable than the other two control methods.

Fuzzy PID speed control of BLDC motor based on model design

Brushless direct current (BLDC) motor has been widely used in industrial production. Based on the method of model design, BLDC motor speed control is studied. In Matlab/Simulink, a fuzzy PID speed control model of BLDC motor is established, and C Code is generated by Real-Time Workshop (RTW) technology. After that, Code Warrior is compiled into machine Code and written into ECU. The results show that the fuzzy Proportion Integration Differentiation (PID) controller can improve the speed performance of the BLDC.

Control Strategy of Low-Grade Waste Heat Recovery Unit Based on Fuzzy Proportion Integration Differentiation

Control Strategy of Low-Grade Waste Heat Recovery Unit Based on Fuzzy Proportion Integration Differentiation

Active-Passive Combined Control System in Crane Type for Heave Compensation

For the purpose of researching the displacement control system of heave compensation for offshore drilling platform, a set of crane type active and passive combined heave compensation device are designed on the basis of the similarity principle. As it known that platform heaving will be caused by the wind, sea wave and ocean current when conducting the drilling operations on the offshore drilling platform, which then will disturb the drilling operation. Therefore, the compensation device must be adopted to keep the vertical relative motion between the drill string system and the drilling platform to zero during the operation. Meanwhile, to improve the real-time performance of the heave compensation, the control system is optimized by establishing the simulation model of the active-passive combined crane, and LS-SVM (Least Squares Support Vector Machine) is improved by the artificial immune algorithm to predict the motion trend of offshore platforms. Eventually, in order to acquire the best control scheme, the Proportion Integration Differentiation (PID), fuzzy PID, BP neural network PID control method are utilized to carry out the simulation analysis, and the BP neural network PID control is found to be the optimum. Experiments showed that after using the BP neural network PID control algorithm, the displacement compensation rate of hook for active-passive combined crane device is more than 90%, the performance of the heave compensation is better, and the control is in time.

Design and experimental validation of control algorithm for vehicle hydraulic active stabilizer bar system

This paper presents a novel active roll control algorithm for vehicle hydraulic active stabilizer bar system. The mechanical structure and control scheme of hydraulic active stabilizer bar system is detailed. The anti-roll torque controller is designed with “Proportional-Integral-Differential (PID) + feedforward” algorithm to calculate the total anti-roll torque. A lateral acceleration gain and roll rate damping are added into “PID + feedforward” controller, which can improve vehicle roll dynamic response. The torque distributor is introduced based on fuzzy–PID algorithm to distribute the anti-roll torque of front and rear stabilizer bar dynamically, which can improve vehicle yaw dynamics response. The actuator controller is used for realizing the closed-loop control of the actuators displacement and generating the accurate anti-roll torque. The hardware-in-the-loop simulation platform is established based on AutoBox and active stabilizer bar actuators. The hardware-in-the-loop experiment is carried out under typical maneuvers. Experimental results show that the proposed control algorithm improves the vehicle roll and yaw dynamics response, which can enhance the vehicle roll stability, yaw stability, and ride comfort.

Design and testing of a control system associated with the automatic feeding boat for farming Chinese river crabs

In order to address the issues of nonuniform feeding and high labor cost plaguing the process of farming Chinese river crabs, the present study proposes a multifunctional automatic river crabs feeding boat based on Advanced RISC Machine (ARM) and Global Positioning System / Inertial Navigation System (GPS/INS) integrated navigation. This paper proposes a new calculation method based on real time point insertion. This method calculates the current target position of the boat in the real time according to the position of the boat and the turning points of the current route. A new turning and route-switching strategy is also presented in this paper to improve the ship's operational efficiency and prevent the ship from veering off the target route due to its high speed. Considering the boat's unique movement characteristics including non-linearity, large delay and underdamped nature, a route-speed dual-loop control algorithm is designed based on fuzzy Proportion Integration Differentiation (PID) method. Through analyzing the bait distribution associated with the feeding machine, the present study proposes an inner-spiral-based full coverage traversal method and a travel distance optimization model so as to improve the uniformity of the automatic feeding. Results show that the speed overshoot is no more than 5% and the steady-state error can be kept within 3%. Compared with the finite point method, the real time point insertion method decreases the peak route deviation errors by 82.82% and 84.14% while turning and going straight.

Design and Analysis of a Floor Radiant Heating System Based on Energy Substitution Technology

With the development of renewable energy, energy substitution technology has been applied to many fields. This research suggests that solar energy, as renewable energy, takes the place of conventional energy: a floor radiant heating system driven by solar energy is combined with a photovoltaic floor radiant heating system, and the photothermal floor radiant heating system has been proposed and investigated in this study. This research also designs a fuzzy PID (Proportion, Integration, Differentiation) control system to control the indoor temperature within the set range precisely. In this paper, the proposed floor radiant heating system has been tested and analyzed. The experimental results show that the inhomogeneity of the indoor floor surface temperature distribution is larger than that of other places, and the standard deviation of the indoor floor surface temperature can reach 1.87 °C. The standard deviation was approximately 0.36 °C at 0.6 m, 1.2 m and 1.8 m, which indicates this is suitable for habitation. Three kinds of floor radiant heating systems were compared and analyzed to demonstrate the advantage of the proposed floor radiant heating system. The calculation method of the heating system was proposed and applied to the actual heating system in this paper. The proposed floor radiant heating system is a highly efficient and environmental protection system that can be used for heating extensive areas to realize the objective of energy saving and emission reduction.

Experimental Analysis of an Active Vibration Frequency Control in Gearbox

Aiming at the vibrations of the multistage gear transmission system aroused by the gear meshing excitation, a novel active vibration control structure with built‐in piezoelectric actuators (PZT) was established. The active control forces generated by the PZT were transmitted to the shafts through the additional supporting bearings. In addition, an adaptive fuzzy proportion integration differentiation (AFPID) control algorithm was proposed as the primary control algorithm to reduce the transverse vibrations of the gear shaft. According to the control law of PID parameters, a fuzzy inference module was designed to adaptively adjust the PID parameters to obtain the optimal control effect. An experimental platform was set up to verify the control effect of the algorithm. The experiments performed show 10 dB reduction in housing vibrations at certain targeted mesh harmonics over a range of operating speeds.

Magnetic Levitation Haptic Augmentation for Virtual Tissue Stiffness Perception.

Haptic-based tissue stiffness perception is essential for palpation training system, which can provide the surgeon haptic cues for improving the diagnostic abilities. However, current haptic devices, such as Geomagic Touch, fail to provide immersive and natural haptic interaction in virtual surgery due to the inherent mechanical friction, inertia, limited workspace and flawed haptic feedback. To tackle this issue, we design a novel magnetic levitation haptic device based on electromagnetic principles to augment the tissue stiffness perception in virtual environment. Users can naturally interact with the virtual tissue by tracking the motion of magnetic stylus using stereoscopic vision so that they can accurately sense the stiffness by the magnetic stylus, which moves in the magnetic field generated by our device. We propose the idea that the effective magnetic field (EMF) is closely related to the coil attitude for the first time. To fully harness the magnetic field and flexibly generate the specific magnetic field for obtaining required haptic perception, we adopt probability clouds to describe the requirement of interactive applications and put forward an algorithm to calculate the best coil attitude. Moreover, we design a control interface circuit and present a self-adaptive fuzzy proportion integration differentiation (PID) algorithm to precisely control the coil current. We evaluate our haptic device via a series of quantitative experiments which show the high consistency of the experimental and simulated magnetic flux density, the high accuracy (0.28 mm) of real-time 3D positioning and tracking of the magnetic stylus, the low power consumption of the adjustable coil configuration, and the tissue stiffness perception accuracy improvement by 2.38 percent with the self-adaptive fuzzy PID algorithm. We conduct a user study with 22 participants, and the results suggest most of the users can clearly and immersively perceive different tissue stiffness and easily detect the tissue abnormality. Experimental results demonstrate that our magnetic levitation haptic device can provide accurate tissue stiffness perception augmentation with natural and immersive haptic interaction.

A Fuzzy PID Controller-Based Two-Axis Compensation Device for Airborne Laser Scanning

In this paper, a two-axis compensation device was designed to eliminate the impact of the attitude deviations of the moving platform on the laser point cloud. In order to improve the dynamic performance of the compensation device, the fuzzy proportional-integral differential (PID) controller was employed for the compensation device. Then, a semi-physical simulation experiment was carried out to quantitatively evaluate the dynamic performance of the compensation device using a three-axis turntable and a high-accuracy inertial measurement unit (IMU), which was used to measure the attitude deviations of the three-axis turntable in real time. As a result, the roll and pitch deviations of turntable measured by IMU can be compensated using the two orthogonal frameworks of the compensation device controlled by fuzzy PID controller. The experimental results showed that the fuzzy PID controller compensated the attitude deviations more accurately than the traditional PID controller and the laser pointing direction could be remained within 0.2° using the compensation device.

Researches on Temperature Control Strategy of SMHS-Type 3D Printing Based on Variable Universe Fuzzy Control

Selective micro heat sintering (SMHS)-type 3D printing technology is a widely applied method in rapid prototyping, which uses an electric heating component to sinter non-metallic powder. It requires precise control of the heating component’s energy and its sintering time. Temperature is one of the key factors that affect the forming quality of fused-type 3D printing technology. Aiming at the nonlinear and time-delay characteristics of temperature control in fused-type 3D printing, a fuzzy control method based on variable universe fuzzy control was studied. This fuzzy control method adopts a set of nonlinear expansion-contraction factors to make the variable universes change with the adaptive error, which can help acquire adaptive temperature adjustment in the rapid prototyping process control. The results of the simulation and experiment showed that the controlled temperature response was faster, the overshoot was smaller, and the stability was better compared to the conventional fuzzy proportion integration differentiation (PID) algorithm after the temperature reached the target temperature. The printed results indicated that the universe fuzzy PID control can effectively improve the accuracy of the workpiece shapes and that the density distribution of the workpiece is increased, which can help improve the forming quality.

Adaptive optics correction technique based onfuzzy control

In an adaptive optics system, proportion-integration-differentiation (PID) controller is widely used for correcting wave front, but the controller is strictly dependent on the response model of deformable mirror. In this paper, a novel wave front correction method is proposed. The method, combining fuzzy control and PID control, does not depend on the response model of the deformable mirror. Based on rapid wave front reconstruction, the wave front evaluation indexes, extracted from the reconstructed wave front, are employed for the input of fuzzy controller and PID controller. Thus, the model response matrix of deformable mirror is not required. Each actuator of deformable mirror corresponds to an independent fuzzy PID controller. By designing the fuzzy controller, including fuzzy rule base selection and fuzzy reasoning, the three parameters of PID controller, the proportional kp, the integral ki and the differential kd, are adjusted automatically. A high rapid DSP hardware platform is constructed to verify the method. Test results show that the method can be used to correct the diffraction limit multiplication factor of the light spot from 10-12 to 3-4, which is basically the same as the traditional PID control, but its stability is better. Because the model does not need to calibrate the deformable mirror, the installation of the deformable mirror is easier.

Implementation of remotely operated vehicle for direct inspection of reactor pressure vessel and other water-filled infrastructure

To reduce the irradiation exposure time of inspection personnel inspecting the reactor pressure vessel and other water-filled infrastructure during overhaul period, a remotely operated vehicle (ROV) is developed to help monitor the underwater environment and salvage small parts like bolts and nuts in the reactor pool and other water-filled infrastructure of the nuclear power plants. It is designed to be compact, light and radiation resistant. The depth control strategy based on fuzzy proportion integration differentiation is proposed for the ROV to suspend at any depth of the reactor pool. And the integrated navigation algorithm based on the Kalman filter fuses data from sensors, including sonar, depth gauge, three-axis accelerometer, three-axis gyroscope and three-axis magnetometer. Radiation testing has been conducted to verify ROV's ability to work in the reactor pool and other water-filled infrastructure under the environment of high irradiation. Eventually, field experiment was conducted in the reactor simulation pool of the Daya Bay Nuclear Power Plant, and the result verifies the effectiveness of the depth control algorithm, the integrated navigation algorithm and also the leak tightness of the ROV.