244 results about "Fuzzy adaptive" patented technology

The invention provides a method and device for estimating a state of charge of a battery. The method comprises the following steps: establishing a multi-parameter fusion mathematical model of a lithium iron phosphate battery; using a fuzzy Kalman filtering algorithm based on an index input membership function to optimize and estimate the state of the charge of the battery, wherein the step of establishing the multi-parameter fusion mathematical model comprises establishing a charge-discharge multiplying power-state of charge sub module, a temperature-state of charge sub module, and a cycle index-state of charge sub module. The method for estimating the state of charge of the battery uses the fuzzy self-adaptive Kalman filtering algorithm based the index input membership function, so that the theoretical value of measurement noise in the Kalman filter is adjusted smoother in a self-adpative manner, thereby improving the matching degree of the system measurement noise, and estimating the state of the charge of the battery more accurately.

The invention discloses a constant pressure automatic grinding method based on fuzzy adaptive force control. The grinding method comprises the following steps: detecting the contact force of a grinding head and a workpiece in a real-time manner to feed back force signals; changing an analog voltage value output by a controller according to a preset control algorithm, thereby controlling the output torque of an x-axis servomotor to control the contact force. The invention further provides a grinding device for realizing the grinding method. The grinding device comprises an industrial control main engine, a workbench, motion mechanisms, one-dimensional sensing equipment, a position sensor for acquiring the position coordinate of the workpiece, grinding equipment and workpiece clamping equipment. The grinding device can be used for detecting the normal grinding force in a grinding process based on an intelligent force control technology, and feeding back detection results; through the processing of the controller, driving signals are generated for continuous adjustment of the output torque of a driver, so that the normal grinding force can be controlled in a real-time manner and the constant pressure grinding can be realized.

This invention discloses a dual-feed AC-DC-AC converter control structure for wind power generation, and the rotor exciting magnetic converter using the fuzzy PI adaptive control strategy of current inner loop. When the generator no-load networking, the current inner loop uses the conventional PI regulator, and after networking, as to three modes, sub-synchronization, synchronization, super-synchronous, it uses fuzzy adaptive PI control. The network-side converter uses SPWM control strategy which fixed switching frequency and combining electric network electromotive front-feed. In the invention, both converting processes use four-quadrant operation PWM converters based on full-controlled devices, so it can not only improve the system dynamic response, reduce wastage and impact, achieve the two-way electric transmission, but also it can achieve wind power generation network side unit power factor sinusoidal current networking power generation operating.

The invention discloses a motor non-speed sensor control method for smoothly switching composite rotating speed identification. In the method, the model reference fuzzy self-adaptive rotating speed identification of the magnetizing current reactive power is adopted for identifying the rotating speed of the motor in high speed; the slip angular speed ring opening rotating speed identification is adopted for identifying the rotating speed of the motor in low speed; the method for smoothly switching factors is adopted for smoothly transiting the two motor rotating speed identification methods, thus realizing the composite identification of the high and low rotating speed of the motor in the starting process of the motor. The invention has the beneficial effects that the method solves the contradiction that the indexes of the dynamic property and the steady-state performance in the traditional single rotating speed identification speed can not be simultaneously optimized so that the motor has the good rapidity and strong dynamic track during the starting; in the steady-state process, the overshoot is small and the speed control precision is high; the method has the complete robustness for the state resistance, can implement the vector control of the non-speed sensor in a wider speed range, thus realizing the composite identification of the high and low rotating speed of the motor in the true sense.

The invention provides a bivariate nonlocal average filtering de-noising method for an X-ray image. The method is characterized by comprising the following steps: 1) a selecting method of a fuzzy de-noising window; and 2) a bivariate fuzzy adaptive nonlocal average filtering algorithm. The method has the beneficial effects that in order to preferably remove the influence caused by the unknown quantum noise existing in an industrial X-ray scan image, the invention provides the bivariate nonlocal fuzzy adaptive non-linear average filtering de-noising method for the X-ray image, in the method, a quantum noise model which is hard to process is converted into a common white gaussian noise model, the size of a window of a filter is selected by virtue of fuzzy computation, and a relevant weight matrix enabling an error function to be minimum is searched. A particle swarm optimization filtering parameter is introduced in the method, so that the weight matrix can be locally rebuilt, the influence of the local relevancy on the sample data can be reduced, the algorithm convergence rate can be improved, and the de-noising speed and precision for the industrial X-ray scan image can be improved, so that the method is suitable for processing the X-ray scan image with an uncertain noise model.

The invention provides a fuzzy adaptive variational Bayesian unscented Kalman filter method. The method comprises the steps of estimating a one-step prediction target state as shown in the description and a covariance matrix thereof as shown in the description, iteratively estimating the variance as shown in the description of the measured noise, calculating the true value as shown in the description, the estimate value as shown in the description, the matching degree index as shown in the description and the adjustment quantity as shown in the description of a residual variance matrix at the current moment, and the adjusted measured noise variance as shown in the description, and calculating the estimated value as shown in the description of the target state and the error covariance matrix thereof. The method is capable of estimating the statistic variance capacity of the measured noise on line, and therefore, the filter error caused by unknown time variant of the noise statistical property is reduced and nonlinear filter estimation accuracy is improved. Meanwhile, the fuzzy logic method based on the innovated covariance matching technique is used for adjusting the measured noise variance estimated by the variational Bayesian method in real time, inhibiting the divergence of the filter and enhancing the robustness of the filter method.

The invention discloses an unmanned aerial vehicle attitude fuzzy adaptive predication control method based on nonlinear model and a system thereof. A Cubic-RBF-ARX nonlinear model of the system is established by means of an offline data identification method. Then a fuzzy adaptive prediction controller is designed based on the established nonlinear model. The prediction controller performs online adjustment to the weight coefficient of a target function in the prediction controller according to the real-time state in controlling the attitude of the unmanned aerial vehicle. The fuzzy adaptive prediction controller can ensure a fact that the selected target function accords with a dynamic and stable rule and trend for attitude adjustment in the attitude control process of the unmanned aerial vehicle. Compared with a common unmanned aerial vehicle prediction controller, the whole dynamic and stable process for controlling is considered in setting the parameters of the target function, thereby performing a function of improving an unmanned aerial vehicle attitude control dynamic-and-static response index, and realizing relatively high practical value and good application prospect.

The invention discloses a fuzzy PID adaptive controller design method of an aero-engine based on RBF neural network feedforward and belongs to the aero-engine numerical control system field. The method is used for improving acceleration and deceleration work states of the aero-engine and components under effects of an extraneous condition and internal control so that the engine possesses a good acceleration and deceleration characteristic. The technical scheme is characterized by based on an aero-engine steady state test data sample, using an RBF neural network to carry out off-line training so as to form a feedforward controller; and based on that, adding a fuzzy adaptive PID controller. In the invention, through carrying out acceleration and deceleration control on each state of the aero-engine, the controller possesses a good tracking effect and a good controller parameter online setting capability, control quality of the system in a large range can be further increased and a control difficulty brought by a non-linear characteristic of the engine can be effectively solved.

The invention discloses a variable air volume room temperature control method based on fuzzy PID and a prediction control algorithm. The method comprises the steps that the temperature deviation between an expected room temperature and an actual room temperature is input to a fuzzy adaptive PID controller, PID parameter incremental quantity of the opening degree of a tail end air valve is obtained through calculation, and calibrated opening degree of the tail end air valve is output; the opening degree of the tail end air valve of the fuzzy adaptive PID controller is subtracted from the opening degree of the tail end air valve of a previous moment by utilizing a time delay link to obtain the variable quantity of the opening degree of the tail end air valve; the prediction control algorithm is adopted to obtain the actual opening degree of the tail end air valve through the variable quantity of the opening degree of the tail end air valve, and the room temperature is maintained at a set value and is kept unchanged. The control method combining the fuzzy PID with the prediction control algorithm is adopted to solve the technical problems that parameter setting difficulty is large, working condition adaptive capacity is poor, and adaptive ability is weak in an air conditioning system in the prior art.

The invention relates to an online adjustment method for a PI (proportion integrate) parameter of an asynchronous motor, the method comprises the following steps of formulating a control rule table for changing values of parameters P and I according to certain principle; judging a current operation state of the motor according to an operating speed of the motor; adopting a conventional PI control when the motor operates at a stable speed; when the motor operates at an accelerating speed or a reducing speed, adopting a fuzzy PI control to carry out an online self-adjustment for the PI parameter until the motor operates in a stable state, the self-adjustment comprises that according to a distribution scope of a rotation speed deviation value e and the value of a changing rate ec of the rotation speed deviation, a domain and scale coefficients Ke and Kec of e and ec are defined to obtain a novel control rule table; setting an initial value for the PI parameter; and carrying out the online self-adjustment for the PI parameter. By the adoption of a fuzzy self-adaptive PI control method, the PI parameter is adjusted to enable the current PI parameter to always meet the requirement of current working condition, the reaction time can be accelerated by utilizing the method to adjust the PI parameter, and dynamic performance of the motor is improved.

The invention discloses a speed and current double closed-loop control system and method for a permanent magnet synchronous linear motor. The method comprises the steps of: designing a permanent magnet synchronous linear motor vector control system; designing a fuzzy adaptive sliding-mode speed controller; designing a nonlinear disturbance observation; designing a d-axis fuzzy PID controller; designing a q-axis fuzzy PID controller; obtaining a desired q-axis current reference value as an input of the q-axis fuzzy PID controller according to the designed nonlinear disturbance observer and thedesigned fuzzy adaptive sliding-mode speed controller; setting a d-axis reference current value as 0, and using the id obtained by subtracting park transform from 0 as the input of the d-axis fuzzy PID controller; after the processing by the d-axis fuzzy PID controller and the q-axis fuzzy PID controller, obtaining the ud, uq of the vector control system, and finally outputting the current operating driving voltage of the permanent magnet synchronous linear motor by the anti-park transformation and SVPWM modulation through the vector control and an inverter. The method enhances the robustnessof the system.

The invention discloses a hydro governor parameter optimization method based on the fuzzy self-adaptive DFPSO algorithm. The method comprises the steps of (1), establishing a hydroturbine regulating system mathematical model; (2), determining a fitness function of the fuzzy self-adaptive DFPSO algorithm; (3), implementing fuzziness setting on the speed inertia factor of the algorithm, wherein the linearly-decreasing inertia factor and the current particle optimal performance evaluation index are input fuzzily; (4), calculating the fitness values of particles, maintaining the individual optimal values and the global optimal values of the particles and updating the speeds and positions of the particles; (5), carrying out gene crossover between the particles when the crossover condition is met; (6), judging whether the end condition is met, stopping gene crossover and outputting the optimal value when the end condition is met, and implementing the steps (4) to (6) when the end condition is not met. According to the invention, a system can have good dynamic property on the conditions of frequency disturbance and loading disturbance, small overshoot, short stabilization time and short adjustment time.

The control system includes computer, potentiometric amplifier, A/D converter, D/A converter, filter, position finding system, learning device, optimizing decision maker. Position finding system is setup in ship. Information output of the position finding system is connected to input end of A/D converter. Output end of the A/D converter is connected to computer, which is connected to an input end of filter. Through the learning device and the optimizing decision maker, the output end of the filter is connected to the input end of the potentiometric amplifier. The output end of the potentiometric amplifier is connected to actuator. The invention can adapt to loading change of ship and change of sea condition so as to raise dynamic-positioning precision and lower positioning energy consumption.

The invention relates to a control system of an opening-closing die motor and a push-out motor of a fully-electric injection molding machine, comprising a computer controller, a PLC (Programmable Logic Controller), an opening-closing die push-out driver, the opening-closing die motor and the push-out motor which are mutually connected. The invention also analyzes the motion characteristics and the dynamic characteristics of a toggle lever mechanism of a five-hinge die-closing double-toggle die closing mechanism object, provides a die protection implementation method of a die closing mechanismunder the driving of a fully-electric motor as well as a die closing mechanism control method and a field oriented control algorithm on the basis of a fuzzy adaptive theory from the angle of a control theory, meets the control requirements on three aspects of the die closing force, the speed, and the position, ensures the reliable closing of the molded die and realizes the opening-closing motion of the die and accurate and stably push-out of products from the die; and the designed DSP (Data Signal Processing) driver ensures that the system has stable reliability under the condition of high load and simultaneously improves the disturbance compensation.

The present invention discloses an electric vehicle asynchronous motor fuzzy adaptive dynamic surface control method with the consideration of an iron loss. According to the control method, by using the nonlinear function in a fuzzy logic system approximation system, an adaptive backstepping method is used to design a controller, and the dynamic surface control technology and a fuzzy adaptive method are combined. Through introducing a first-order low-pass filter, a 'computing explosion' problem caused by continuous derivation in traditional backstepping control is successfully overcome. In addition, control signals uqs and uds only exist in an adaptive parameter Theta<^>, and the amount of computation is reduced. Through fuzzy adaptive dynamic surface control method adjustment, motor operation can rapidly reaches a stable state and is more suitable for a control object with the need of rapid dynamic response like an electric vehicle driving system, a simulation result shows that the influence of parameter uncertainty is overcome and an ideal control effect is ensured according to the control method, and the rapid and stable response of rotation speed is realized.

The invention discloses a high-voltage parallel hybrid active power filter device and an iterative learning control method with a forgetting factor. The device is composed of a passive power filter, an active power filter and a coupling transformer. A steplength-variable fuzzy self-adapting harmonic wave detection method is adopted, so as to coordinate the requirements of steady-state performance and convergence rate of the system, and the detected electric network harmonic current is used as the system control target. An iterative learning control algorithm with the forgetting factor is adopted, and an improved Ziegler-Nichols method is utilized to realize the optimization of control parameters and improve the system control performance. The high-voltage parallel hybrid active power filter device has certain advantages in response time and control accuracy, can satisfy the requirements of large-capacity hybrid active filtering system, not only can well control the harmonic wave in the electric network, but also can compensate large-capacity reactive power, and is reliable in systems operation.

The invention relates to an infrared target detection method based on a space-time cooperation framework. The method comprises the following steps: 1. acquiring a background frame Bg and a current frame Ft of a video, combining the background frame Bg and the current frame Ft to carry out background clutter suppression and acquiring a background suppression graph Gt after the background clutter suppression is performed; 2. for the background suppression graph Gt obtained in the step 1, firstly establishing a space-time background model, and then carrying out target positioning aiming at space-time background model information after the model is established; 3. according to an imaging mechanism of the infrared target, analyzing a space difference of the infrared target and the surrounding background, using a fuzzy adaptive resonance nerve network to carry out local classification aiming at the target which is positioned in the step 2 and then extracting the infrared target. The method has the following advantages that: the method does not depend on any target shapes and motion information priori knowledge; the method is suitable for a complex outdoor scene; a signal to noise ratio can be increased; a target detection rate can be increased and a calculated amount can be reduced; false targets can be effectively removed and a false alarm rate can be reduced; the method is beneficial to follow-up target identification.

Disclosed is a fuzzy adaptive pictorial information digitizer (PID) control method of a motor train unit based on multi-model switch. According to the method, a data driving modeling method is proposed according to collected running process data of the motor train unit and field experience knowledge, the number of braking models is determined by means of subtraction cluster, and then a plurality of partial linear models describing a braking process of the motor train unit are established. The partial linear models adopt the recursive least-squares method to identify model parameters. At each sampling moment, an optimal partial model is selected based on the multi-model switch strategy, and the fuzzy adaptive PID control algorithm is adopted to control the braking process of the motor train unit, and therefore safe, punctual and effective running of the motor train unit is achieved. The multi-model adaptive PID control of the braking process of the motor train unit is simple, practical, capable of achieve multi-purpose control of the braking process of the motor train unit, and suitable for on-line monitoring and automatic control of the braking process of the motor training unit.

The invention provides a bearing-only underwater target tracking algorithm based on multilevel information fusion. Three levels are divided. In the first level, a least square method is used for initially tracking and positioning a target, and the estimated state value is calculated as an input value of a second-level AFIMM (Adaptive Fuzzy Interacting Multiple Model). In the second level, the AFIMM is designed, the motion model set of the AFIMM is designed optimally, and a method of a fuzzy adaptive model transition probability is brought forward. In the third level, resampling particle filter is designed as an iterative filtering structure at each step for the AFIMM, and information fusion is realized. During the bearing-only target tracking process for a multi-UUV (underwater unmanned vehicle) cooperative system, through the design of the multilevel information fusion framework, the filter accuracy is higher, and the target of multi-UUV cooperative underwater target tracking can be met.

The invention discloses a direct torque control method for a brushless direct current motor. By speed and torque double closed loop control, a rotating speed loop is used as an outer loop, and a torque loop is used as an inner loop; motor speed is calculated according to an output signal of a Hall sensor of the brushless direct current motor, and the motor speed is compared with reference speed to obtain speed error; an output value obtained after fuzzy self-adaptation adjustment is used as a torque expectation value; torque error is obtained by subtracting actual torque from expectation torque, wherein the actual torque is calculated by multiplying back-EMF (Electromotive Force) and phase current; the torque error is inputted into a hysteresis comparator; the hysteresis comparator outputs a position signal of a rotor; and an appropriate voltage vector is selected from a formulated switch list to control an inverter bridge connected with a DC power supply so as to make the brushless direct current motor output stable torque. By the method, the defect that current hysteresis control has a poor effect of inhibiting electromagnetic torque is avoided, and the problem that a traditional PID control method has low precision and poor anti-jamming capability in control of BLDCM is overcome.

The invention discloses a transformer winding state recognition method based on a fuzzy adaptive resonance neural network. The transformer winding state recognition method comprises the steps of (1) arranging monitoring points of a vibration sensor, setting the sampling frequency and the sampling time of a data acquisition instrument, and acquiring transformer vibration signals; (2) performing wavelet packet decomposition and reconstruction on the transformer vibration signals; (3) extracting vibration signal sub-band energy values; (4) analyzing the vibration signal sub-band energy values of each vibration monitoring point, and selecting feature band energy of valid monitoring points to construct a feature vector so as to act as input of the fuzzy adaptive resonance neural network; (5) building the fuzzy adaptive resonance neural network, adjusting network parameters until set precision is reached; and (6) performing recognition on transformer winding state through the fuzzy adaptive resonance neural network. The transformer winding state recognition method can judge a transformer winding pressing state accurately and quickly, and can be applied to online monitoring and recognition for the transformer winding pressing state.