1142 results about "Fuzzy control system" patented technology

The invention discloses a trajectory tracking control method used for an automatic driving robot of a vehicle, which relates to electronic control technologies of vehicles. The desired trajectory of a vehicle is described in a data point mode to obtain the current position information of the vehicle; humanoid driving is carried out according to the current driving direction at the current vehicle position to preview a distance ahead; the position obtained by previewing is compared with the desired trajectory to determine the lateral position deviation, the angle deviation and the vehicle speed deviation of the coordinate position obtained by previewing relative to the desired trajectory; then, the vehicle direction is jointly controlled by a trajectory tracking and speed controller according to position deviation and direction deviation; and according to vehicle speed deviation, the control quantities of an accelerator pedal and a brake pedal are determined with a fuzzy control method. The trajectory tracking control method is suitable for controlling the trajectory tracking of any trajectory, various vehicle types and various working conditions, and an automatic driving robot performs the control. The trajectory tracking control method has high precision and good repeatability and has an important meaning for guaranteeing the quality of vehicle reliability experiments and improving experiment safety.

Methods and apparatus for automatically generating a list of recommended links for a user are disclosed. For a particular user, one or more criteria for use in generating a list of recommended links are identified or selected by the user. A list of one or more recommended links is then generated and provided to the user.

The invention discloses an air conditioner control method, which comprises the following steps that the indoor temperature is detected; a difference value between the indoor temperature and the temperature set by a user is calculated according to the indoor temperature and the temperature set by the user; the initial operation frequency increment of an air conditioner compressor is calculated according to the difference value and a fuzzy control rule, and the initial operation frequency is calculated according to the initial operation frequency increment for controlling the compressor to operate at the initial operation frequency; the operation time of the compressor is detected; when the operation time is greater than the preset time, and if the absolute value of the difference value is smaller than or equal to a preset threshold value, the operation frequency modification value is calculated according to the difference value and a PID (proportion integration differentiation) control model, and in addition, the operation frequency modification value is overlapped onto the initial operation frequency for controlling the compressor to operate at the modified operation frequency. Therefore the operation frequency of the compressor can realize fast response to the load change, the fast refrigeration and heating can be realized, meanwhile, the temperature can be precisely controlled, and the energy sources can be effectively saved. The invention also discloses an air conditioner control device.

An artificial intelligence system is provided which makes use of a dual subroutine to adapt weights. Elastic Fuzzy Logic ("ELF") System is provided in which classical neural network learning techniques are combined with fuzzy logic techniques in order to accomplish artificial intelligence tasks such as pattern recognition, expert cloning and trajectory control. The system may be implemented in a computer provided with multiplier means and storage means for storing a vector of weights to be used as multiplier factors in an apparatus for fuzzy control.

The invention relates to a machinery automatic steering control method which comprises the following steps that: positional deviation and course deviation are determined; according to the variance in the practical front wheel rotating angle of an agricultural machine, online setting of PID parameter is completed by means of parameter self-setting PID control algorithm; moreover, the expected front wheel rotating angle of the next moment is calculated, thereby realizing automatic steering control of the agricultural machine. Based on conventional PID navigation control method, the invention makes full use of the fuzzy control method; according to the variance in the practical front wheel rotating angle of an agricultural machine, the invention meets the different requirements of the machine on PID control parameter under different errors and error change rate, thereby realizing online setting of PID parameter. The invention not only has the advantages of fuzzy control such as flexibility and adaptability, but also has the characteristics of higher precision of PID control; therefore, the invention can increase the stability and precision of agricultural machine automatic steering control and the robustness of a control system.

The invention discloses a central air conditioner intelligent control system based on a wireless sensor network and a method, and belongs to the technical field of central air conditioner control. The central air conditioner intelligent control system comprises an intelligent information management center, wireless communication devices, a central air conditioner information monitoring and control module, a room information monitoring and control module and the like. The invention adopts the method that the central air conditioner intelligent control system is used to monitor information, build a data base, calculate current load of the system, and realize advance, accurate and optimized control of a central air conditioner system by combining a short-term loading forecasting method based on similar dates and fuzzy control technology, as well as taking the temperature variation of supply/return chilled/cooling water of a central air conditioner as the manipulated variable. The central air conditioner intelligent control system has the characteristics of great communication capacity, guaranteed communication quality, powerful network functions, wide monitoring range and extensive control quantity of the central air conditioner system, optimized control, high control accuracy, good energy saving effect and the like, and can be widely used in energy-saving optimizing control of various buildings, and is particularly suitable for energy-saving optimizing control of central air conditioners of intelligent buildings.

The image sensing device or image processing apparatus has a synthesizing capability enabling the device or apparatus to perform blur control using a specified amount of blur and to prepare a blur-controlled image, as well as a blur-controlled image processing capability enabling the device or apparatus to carry out processes other than blur control on the prepared blur-controlled image and to change the degree of processing in accordance with the amount of blur.

The invention discloses a multi-behavior combining auto-navigation method for a mobile robot in an unknown environment. The method is characterized in that the method comprises the following steps that (1) current azimuth angle is obtained in real time according to the relative positions of an object and the mobile robot, and a plurality of distance parameters are obtained in real time according to the status of obstacles around the mobile robot; (2) a multi-output support vector machine fuzzy controller outputs a corner value Theta i and a velocity value vi according to the obtained azimuth angle and distance parameters, wherein i is equal to 1, 2 or 3; (3) a multi-output support vector machine environmental-identification controller inputs signals and outputs weight parameters wi of three subbehaviors according to input signals of the azimuth angle and the distance parameters, wherein i is equal to 1, 2 or 3; and (4) current corner value Theta i and velocity value vi of the mobile robot used for navigation are calculated according to the formula. The multi-behavior combining auto-navigation method adopts intelligent control strategy, and has the advantages of strong self adaptation, high navigation reliability and excellent effect.

The invention discloses a stepping motor control system and a control method thereof. The stepping motor control system comprises a controller, a stepping motor controller, a stepping motor, a transmission mechanism, a load and a photoelectric encoder, wherein the controller sends a plurality of pulses with a certain frequency and a certain quantity to the driver which receives the pulse information and a direction signal and acts on the stepping motor, the stepping motor drives the load through a driving mechanism, and the photoelectric encoder feeds back and transfers position information to the controller. The invention realizes high-accuracy positioning through a fuzzy control method, achievesprovides higher operation effect by adopting the combination of dual compensation feedback and open-loop control, has the desynchronizing detection function for compensatingrehending and feeding back failure information and also hasprovides higher failure suitability.

The invention provides a multi-robot path planning and coordination collision prevention method based on fuzzy logic. The method comprises the steps of confirming an input variable and an output variable of a fuzzy controller by setting up the fuzzy controller with robots as a system, using related languages to describe the input variable and the output variable of the fuzzy controller, confirming a qualitative reasoning principle according to the fuzzy control theory, setting up fuzzy control rules according to input signals and output signals of the fuzzy controller, selecting a subordinating degree function of each input language variable and a subordinating degree function of each output language variable, finally, conducting defuzzification, conducting voting on the basis of the maximum principle of the subordinating degree functions, conducting corresponding motions on the robots, and completing the task for planning a path of a single robot. On the basis of the method, the other robots are regarded as dynamic front barriers, and the aim of coordination collision prevention of the multiple robots can be achieved.

The invention provides an unknown environment route planning method of an underwater cleaning robot, which is a self-adaptive learning fuzzy control method for planning a track in an unknown environment. The method is characterized in that: by simulating the route planning thought, obstacle avoiding through and intelligent thoughts for making a decision for determining the priority of quick planning and obstacle avoiding, three different sub fuzzy controllers are put forward correspondingly for solving three kinds of problems. Meanwhile, according to the estimation of the movement results of a controlled robot, the rule base of the fuzzy controller is modified correspondingly to make the fuzzy controller can handle more complex external disturbance and effectively solve the problem of U-shaped obstacles and symmetric obstacles, so that the robot can complete a planned task and arrive at a destination in a more complex environment. The method eliminates the uncertainty influences of an underground complex environmental factor on the route planning of the robot, improves the movement safety of the robot and enables the robot to well adapt to the complex underground industrial environment.

The invention discloses an intelligent automobile path tracking hybrid control method, and belongs to the technical field of intelligent automobile lateral control. The intelligent automobile path tracking hybrid control method comprises the following steps that step 1, an automobile lateral control preview kinematics model at a low speed is established; step 2, an automobile lateral control dynamic model at a high speed is established; and step 3, a path tracking hybrid controller is designed, the path tracking hybrid controller comprises a lateral controller, a supervisor and a switching stability fuzzy controller, wherein the lateral controller is designed based on PID control and model predictive control, the supervisor determines a tracking mode based on longitudinal vehicle speed, and the switching stability fuzzy controller is designed based on a fuzzy control theory. The intelligent automobile path tracking hybrid control method effectively solves the problem of lateral controlperformance requirements under high-speed and low-speed working conditions of an intelligent automobile, and the easy realization, accuracy and stability of path tracking of the intelligent automobile are improved.

A control system using a genetic analyzer based on discrete constraints is described. In one embodiment, a genetic algorithm with step-coded chromosomes is used to develop a teaching signal that provides good control qualities for a controller with discrete constraints, such as, for example, a step-constrained controller. In one embodiment, the control system uses a fitness (performance) function that is based on the physical laws of minimum entropy. In one embodiment, the genetic analyzer is used in an off-line mode to develop a teaching signal for a fuzzy logic classifier system that develops a knowledge base. The teaching signal can be approximated online by a fuzzy controller that operates using knowledge from the knowledge base. The control system can be used to control complex plants described by nonlinear, unstable, dissipative models. In one embodiment, the step-constrained control system is configured to control stepping motors.

The invention provides a microgrid economic operation optimization method based on a fuzzy particle swarm algorithm and an energy saving system. The method comprises the following steps of: (1) determining an economic dispatch strategy of a micro source in the microgrid; (2) building a microgrid grid-connected economic operation mathematical model combined with economic benefit and environment benefit; (3) putting forward a fuzzy control strategy for charging/discharging control of the energy saving system; and (4) optimizing based on the particle swarm algorithm, and determining the output of all micro sources and the generation cost per day. Compared with a method in which an optimization dispatch scheme is not adopted by the energy saving system, the method has the advantages that the generation cost per day of microgrid running and pollution emission are reduced; and compared with a method in which the fuzzy control charging/discharging strategy is not adopted by the energy saving system, the method has the advantages that optimal solution quality is improved, operational convergence rate is increased, and the reliability of microgrid running is further enhanced.

The invention discloses a fuzzy control system and a method of a throttle mechanism. According to the invention, a refrigeration cycle system comprises a compressor, a condenser and an evaporator. The control method comprises the following steps: monitoring the refrigerant vaporization temperature and water supply fluid temperature of the evaporator, and defining the small temperature difference of the evaporator as the temperature difference of subtracting the refrigerant vaporization temperature from the water supply fluid temperature; monitoring the temperature and the saturation temperature of the refrigerant discharged by the condenser, and defining the condenser depression of the refrigerant discharged by the condenser as the difference of subtracting the refrigerant temperature from the refrigerant saturation temperature; and calculating the opening of the throttling orifice of an electronic expansion valve by utilizing the small temperature difference of the evaporator and the condenser depression of the refrigerant discharged by the condenser. The method and the device of the invention can solve the problem of failure to realize stable and precise regulation on the opening of the electronic expansion valve due to the unstable liquid level signals of the evaporator in the prior art.

The invention relates to a yaw control method for a large-sized wind powered generator unit. A current yaw angle reference value alpha reference is obtained according to a current wind direction, a current yaw angle practical value alpha practice is obtained by a encoder or a tachometer, a yaw angular error delta alpha is obtained by subtracting the yaw angle practical value alpha practice by the current yaw angle reference value alpha reference, and a corresponding yaw mechanism control rule is calculated by integrating the yaw angular error delta alpha and the variation tendency thereof by a fuzzy controller. Furthermore, an obtained electrical signal of the yaw mechanism control rule is sent to a yaw actuating mechanism to control a propeller pitch angle, and the yaw angle of the wind powered generator unit traces the yaw angle reference value alpha reference to enable the yaw angle practical value alpha practice and the yaw angle reference value alpha reference of the wind powered generator unit to be kept consistent. The invention obviously enhances the total precision, is favorable to increasing the year total generated energy of the wind powered generator unit, has high adaptability for complex and extreme cases, greatly enhances the operating effectiveness, furthest prolongs the mechanical life of the yaw actuating mechanism, and also has low remodel difficulty and low cost.

The invention relates to a navigation control system based on vision and ultrasonic waves, which comprises a caterpillar robot body, an ultrasonic ranging subsystem, a vision subsystem and a motion control subsystem, wherein the ultrasonic ranging subsystem is arranged in front of the robot, the vision subsystem is arranged over the robot, and the motion control subsystem is positioned above the robot. The ultrasonic ranging subsystem comprises ultrasonic transmitters, ultrasonic receivers and a signal processing circuit, and a plurality of transmitters and a plurality of receivers are distributed in an equally spaced mode. The vision subsystem comprises a CCD (Charge Coupled Device) camera, an analog-to-digital conversion circuit and a DSP (Digital Signal Processor) image processor, and the motion control subsystem comprises a DSP motion controller and corresponding peripheral circuits. Information processed by the vision subsystem and the ultrasonic ranging subsystem is transmitted to a fuzzy controller inlaid in the motion control subsystem, and the fuzzy controller outputs control information to control the motion of the robot. The system has the advantages that the advantages of visual navigation and ultrasonic navigation are combined, so the navigation accuracy is increased; as the DSP high-speed processor is adopted, the instantaneity and the expandability are increased; and the anti-jamming capability is enhanced through applying a fuzzy control method.

An automatic regulation system for the combustion of circulating fluidized-bed boiler (CFBB) features that it has several regulation loops for the discharged cinder quantity, primary blowing rate, supplied coal quantity, secondary blowing rate and draught air volume, which are based on the coordination between fluidizing, combustion and heat exchange, the expert system, fuzzy control and composite cascaded PI regulation are combined, and the protecting tactics such as override protection, output restriction and lock distribution choosing and tracking are used.

The invention discloses an automatic intelligent double cross limiting range combustion control method for a heating furnace, and belongs to the technical field of heating furnace combustion control. By combining temperature self-learning, fuzzy control and PI parameter self-learning, the intelligent double cross limiting range control in the combustion process of the heating furnace is realized,the problem of control accuracy of the combustion of the heating furnace is effectively solved, and a hot rolling production requirement is well met. The method has the advantages that: gas and air flow rates are reasonably, accurately and quickly regulated and controlled, the regulating function of a regulating valve is effectively realized, and the temperature of a hearth of the heating furnaceis reasonably and accurately controlled.

The invention discloses a moment distribution method for a self-adaptive cruise control system of a four-wheeled independent drive electric automobile. An ideal acceleration command is calculated through an upper layer control method and input in a lower layer control method, the lower layer control method is used for calculating the ideal drive moment according to the ideal acceleration command calculated through the upper layer control method and distributing the moment to four wheels, and the problem that a traditional self-adaptive cruise control system cannot be directly applied to the four-wheeled independent drive electric automobile is solved. Smoothly-restrained model prediction control is adopted in the upper layer control method, the practicality of the self-adaptive cruise control system is improved, and the requirements for safety, comfort and economical efficiency of a driver are met. Fuzzy control is adopted in the lower layer control method, so that the ideal longitudinal moment is obtained, the moment is distributed to the four wheels according to the vertical load proportions, and the robustness and practicality of the self-adaptive cruise control system of the four-wheeled independent drive electric automobile are improved while the dynamic quality of the four-wheeled independent drive electric automobile is guaranteed.

Disclosed is a Docker self-adaptive scheduling system with a task sensing function. A Docker self-adaptive scheduling method comprises the steps that a data collector collects the load of each container and the resource usage rate of a CPU and a memory system; based on a queuing theory and with the resource usage rate as a benchmark, a performance modeler constructs an application performance model and characterizes an association relationship of the load and response time; a response time predictor estimates parameters of the performance model during running through a Kalman filter, wherein estimation is conducted under a convergence condition of meeting a predicted and measured response error expectation; a feed-forward controller analyzes a residual mean and variance through a fuzzy controller to obtain a feed-forward adjustment value of a control parameter of the Kalman filter; a container scheduler judges whether or not a predicted value of the response time violates application service quality and conducts scheduling according to a scheduling algorithm; the container scheduler conducts container expansion or shrinkage or migration.

The invention discloses an asymmetric four-motor UAV and a control method thereof. According to the asymmetric four-motor UAV, the connection line between two large motor propellers is perpendicular to that between two small motor propellers, and the connection line between the two small motor propellers is shorter than that of the two large motor propellers; the problem of control system divergence is solved through a Smith prediction algorithm; fuzzy control rules are designed and fuzzy inferences are utilized to achieve the optimized adjustment of PID (proportion-integral-derivative) parameters under different flight working conditions. The asymmetric four-motor UAV has the advantages of large take-off weight and good control robustness. The control method of the asymmetric four-motor UAV solves the problems of inaccurate control model and large system interference and reduces the control coupling degree of the four-rotor UAV, thereby reducing the control difficulty and achieving the smooth control of the four-rotor UAV.

The invention relates to an energy storage optimization and coordination control method for a direct-current micro grid group. When multiple sub micro grids are operated in parallel, an energy storage system in each sub micro grid automatically adjusts power distribution according to the maximum charging and discharging power and the state of charge (SOC) of the corresponding energy storage system, and therefore the safety of the energy storage systems is improved while distributed generated power fluctuation can be smoothed and the bus voltage quality can be improved; when the energy storage systems of all the sub micro grids cannot effectively buffer the system power supply and demand, an energy storage power station needs to be connected to control stabilization of the bus voltage. According to the energy storage optimization and coordination control method for the direct-current micro grid group, the virtual impedance of droop controllers of all energy storage units in the energy storage power station is adjusted by adopting fuzzy control to achieve automatic distribution of the power among the different energy storage units and SOC balance, fuzzy input is segmented, the SOC balancing speed of the fuzzy input in different ranges is increased by adopting fuzzy control, bus voltage drop caused by droop control is compensated by adopting a bus voltage feedback control method, and the bus voltage quality is improved.

The invention provides a sliding-mode control method for parameter-free driving-insufficient UUV (Unmanned Underwater Vehicle) vertical plane route tracking. The sliding-mode control method comprises the following steps: I, performing initialization; II, acquiring a current state of a UUV; III, establishing an error equation of the horizontal plane of the driving-free UUV so as to obtain position deviation values xe and ze and course deviation value theta e; IV, according to a sliding-mode control method, respectively designing traveling speed sliding-mode self-adaptive control rules, position sliding-mode control rules and trimming angle sliding-mode self-adaptive control rules, controlling propelling force Xprop, an excepted traveling speed U and a torque Mprop, wherein eu is 0, xe is 0 and theta e is 0; V, designing fuzzy control rules for a boundary layer, setting k to be equal to k+1, turning to step II, and updating control rules and self-adaptive rules of a next time. By adopting the sliding-mode control method, a controller for stabilizing a system can be designed only according to a vertical surface kinetic model, self-adaptive rules can be designed for water kinetic parameters with uncertainties, furthermore a control system can be relieved from dependency on parameters, the system has robustness, and the influence of the uncertainties on the sliding-mode control approaching process can be reduced.

The invention discloses a wind farm energy storage capacity optimization method in consideration of optimal desired output and charge state. The method comprises the steps as follows: firstly, a computing method of an optimal desired output is proposed and utilizes a function with minimum square deviation of offset of a grid-connected power target value as an objective function, setting subjectivity of a stabilizing fluctuation output power target value is eliminated, and the computing method serves as the premise of energy storage capacity optimization; secondly, an energy storage SOC (state of charge) parameter is introduced, charge-discharge power is timely adjusted on the basis of the fuzzy control theory, SOC and charge-discharge states, an energy storage charge-discharge strategy is built, and excessive charge and discharge are effectively restrained; and finally, an optimal model is built with minimum sum of the penalty cost associated with the SOC and the operation cost, so that the energy storage capacity optimization integrating a schedule decision-making requirements, energy storage operating service life and economic efficiency is achieved, and related optimizing problems are solved with improved PSO (particle swarm optimization). The solution algorithm provided by the method is fast, efficient, and strong in convergence capacity.

The invention provides a multi-agent robot cooperative control method based on an artificial physics method. The method is implemented in a distributed manner based on multi-agent robots. Each agent robot is provided with one or more sensors, is used for acquiring and fusing distributed data and is driven by the information processing results. The method comprises the following steps: (1) setting up vector force fields interacting among the agent robots based on the artificial physics method; (2) determining the movement velocity and directions of the agent robots by solving the resultant force of the agent robots and suppressing the transient state of mode conversion by a four-point smoothing method; and (3) realizing obstacle avoidance of the agent robots based on a fuzzy controlled wall-following tracking method.

The invention discloses an inversing global SMFC (sliding mode fuzzy control) method for a micro-gyroscope based on a neural network. An inversing global SMFC system based on the neural network consists of an inversing global sliding mode fuzzy controller, a neural network dynamic characteristic estimator, and a fuzzy uncertainty estimator. Global sliding-mode control can iron out a defect that an arrival mode in the conventional sliding-mode control does not have robustness, speeds up system response, and enables a system to have robustness in the whole process of response. The method enables a lyapunov function of the system and the design process of a controller to be systematic and structuralized through inverse design during the inversing control. Fuzzy control is used for approximating a switching function term, and a switching term in sliding-mode control is converted into continuous fuzzy control output, thereby weakening vibration in sliding-mode control, and achieving a stronger capability of adaptive tracking. Therefore, the method improves the transient performance and robustness of a sliding-mode control system, estimates the unknown dynamic characteristics of the micro-gyroscope, and reduces the vibration in the control of a sliding-mode control structure.

The invention discloses an air conditioner control method, an air conditioner control device and an air conditioner. The control method includes the steps that the indoor environment temperature and the outdoor environment temperature are detected; the difference value delta T between the indoor temperature and the temperature set by a user is computed; the operating time of the air conditioner is detected; when the operating time is smaller than a preset time, the fuzzy control coefficient of a compressor is computed according to the difference value delta T, the outdoor environment temperature and a preset fuzzy control rule, and the operating frequency of the compressor is computed according to the fuzzy control coefficient; and when the operating time is larger than or equal to the preset time, the operating frequency correction value of the compressor is computed according to the difference value delta T and a preset PI control model, and the operating frequency correction value is added to the operating frequency to control the compressor to operate according to the corrected operating frequency. According to the control method, fuzzy control and PI control are combined to control the compressor so that the compressor can achieve a better control effect; and the precision of steady control is guaranteed and the steady state error is eliminated while the higher response speed is achieved.

The invention discloses a method for estimating lithium battery SOC based on self-adaptability fuzzy Kalman filter. For different lithium batteries, after Kalman filter discrete state model is determined, the SOC is estimated by the utilization of the method of self-adaptability fuzzy Kalman filter, wherein the residual variance matching degree and residual error mean of terminal voltage in a lithium battery equivalent model serve as input of a fuzzy control system, so that adjustment factors of a system noise variance and a measurement noise variance are obtained, and then the two variances are adjusted; the adjusted system noise variance and measurement noise variance are substituted into a Kalman filter algorithm to estimate SOC values at all moments. With the method, the power lithium battery SOC can be accurately estimated, the problems that according to an existing estimation method, the requirement for on-line estimation can not be satisfied, has large accumulated errors, diverges and is prone to being affected by noise can be solved, and the estimation accuracy is high.

The method for generating an integrated guidance law for aerodynamic missiles uses a strength Pareto evolutionary algorithm (SPEA)-based approach for generating an integrated fuzzy guidance law, which includes three separate fuzzy controllers. Each of these fuzzy controllers is activated in a unique region of missile interception. The distribution of membership functions and the associated rules are obtained by solving a nonlinear constrained multi-objective optimization problem in which final time, energy consumption, and miss distance are treated as competing objectives. A Tabu search is utilized to build a library of initial feasible solutions for the multi-objective optimization algorithm. Additionally, a hierarchical clustering technique is utilized to provide the decision maker with a representative and manageable Pareto-optimal set without destroying the characteristics of the trade-off front. A fuzzy-based system is employed to extract the best compromise solution over the trade-off curve.