52 results about "Fuzzy neural network controller" patented technology

The invention relates to a motor rotating-speed tracking control method based on a self-adaptive fuzzy neural network, wherein rotating-speed and current double closed-loop control is adopted, an outer ring is a rotating-speed ring, a sliding-mode control theory-based fuzzy neural-network controller (SMFNN) is designed, an inner ring is a current ring, and a PI (Proportional-Integral) controller is adopted; a fuzzy neural-network rotating-speed controller comprises two parts, wherein one part is a PID (Proportional-Integral-Derivative) controller, and the other part is the fuzzy neural network, online real-time learning is carried out through the fuzzy neural network by utilizing a parameter correcting method designed on the basis of the sliding-mode control theory, and the two parts jointly act to obtain the output ir of the rotating-speed controller, i.e. a difference obtained by subtracting the output iFNN of the fuzzy neural network from the output iPID of the PID controller is used as the output ir of the rotating-speed controller. The control precision and the anti-interference performance of a motor speed-adjusting system can be improved through the control strategy of the motor rotating-speed tracking control method.

The invention discloses a control method for sewage processing process based on the self-organizing neural network, and belongs to the fields of water treatment and intelligent information control. The method mainly comprises adjustment for fuzzy rules by a self-organizing mechanism and self-adaptive learning control of T-S fuzzy neural network. The method comprises the steps that comparison is carried out on the basis of a T-S fuzzy neural network controller; self-organizing adjustment is carried out on the fuzzy mechanism; self-adaptive learning of the neural network is carried out; and the fuzzy rule m at the time k is obtained, and the sewage treatment process at the time k is controlled. The method can be used to adjust the internal structure of the controller in real time according to the environment state, and an object is controlled stably. The self-organizing mechanism is used to adjust the controller structure in real time so that the controller can satisfy environment requirements more effectively; the intelligent control method can be used to control the sewage treatment process stably, so that the quality of output water meet the discharge standard; and the defect that a controller of a fixed network structure is low in environment adaptability is overcome.

The invention provides a mechanical arm flexible joint control method based on a fuzzy neural network. The method comprises the following steps that a fuzzy neural network model is established, a neural network parameter learning algorithm is developed; the learning algorithm determines a connection weight parameter of a consequent network and a subordinating degree function center value and a width parameter of an antecedent network; and a fuzzy neural network controller is established according to an identification model to overcome the influence of many nonlinear characteristics of a flexible joint. According to the provided technical scheme, two kinds of control methods of fuzzy logic and the neural network are combined, and the fuzzy neural network controller base on an X model is adopted, so that the neural network is provided with a structure of a fuzzy system, each layer and each node of the neural network correspond to a part of the fuzzy system, the network is different fromblack box operation of a general neural network, all parameters are of definite physical significances, and the network can adapt to the characteristics such as time varying of rigidity and nonlinearfriction of the flexible mechanical arm joint.

The invention discloses a robot path planning method based on an ANFIS fuzzy neural network, and mainly solves the problems of complex trap path reciprocating and path redundancy in conventional reactive navigation. The method comprises the following steps: to begin with, establishing a kinematic model for a mobile robot; providing a mobile robot navigation controller based on the fuzzy neural network by means of autonomous learning function of the neural network and fuzzy reasoning ability of the fuzzy theory; constructing a Takagi-Sugeno fuzzy inference system based on an adaptive fuzzy neural network structure and serving the Takagi-Sugeno fuzzy inference system as a reference model for local reaction control of the robot; the fuzzy neural network controller outputting offset angle and operation speed in real time, and adjusting the migration direction of the mobile robot online to enable the mobile robot to be able to adjust speed automatically and approach the goal collisionless; and through an improved virtual target method, selecting an optimal path capable of allowing the robot to escape a trapping state.

The invention discloses a room temperature control algorithm based on a fuzzy neural network. A double-input single-output fuzzy neural network controller is designed, the room temperature is detected in real time and output and a temperature setting value are tracked, and an online learning mechanism is adopted to adjust adjustable parameters in the controller in real time to enable the controller to adapt to the change of room temperature and track the temperature setting value. Learning and computing functions of a neural network are integrated into a fuzzy system, and the human-like IF-Then rule of the fuzzy system is embedded into the neural network, so that the adaptive ability of a fuzzy control system is improved on the premise of maintaining the strong ability of knowledge expression, and the system has the ability of self-learning.

The invention discloses a fractional order terminal sliding mode-based AFNN control method of an active power filter. The method comprises the steps of designing a mathematical model of an active filter, a fractional order-based nonsingular terminal sliding mode controller and a fractional order-based adaptive fuzzy neural network controller; and controlling the active power filter by using output of a fractional order-based nonsingular terminal sliding mode adaptive fuzzy neural network controller. According to the AFNN control method, the disadvantage that a nonsingular inversion terminal sliding mode control strategy needs accurate system information is overcome and the robustness is improved; good performance can still be kept when an external load changes; operation of the active power filter along a sliding mode track is ensured through designing the sliding mode controller; for the disadvantages of an inversion control law, an AFNN controller is adopted to approach a nonlinear part in the active power filter. A fractional order module is introduced into the sliding mode controller and the adaptive controller, so that an adjustable item is added by a fractional order in comparison with an integer order, and the overall performance of a system is improved.

The invention discloses a feedback blind equalization method of a dynamic wavelet neural network based on fuzzy control, which comprises the following steps: a) passing a transmitted signal sequence x(n) through an unknown channel h(n), and then superimposing on a Gaussian white noise N(n) to obtain an observation sequence y(n); b) processing an error signal e(n) by a constant modular algorithm (CMA) to obtain a tap coefficient c(n) of a linear segment formed by transversal filters in a wavelet neural network; c) passing an input value deviation E(n) and a deviation change deltaE(n) of a fuzzy neural network controller through a fuzzy neural network controller to obtain an iteration step change value delta mu of an extension factor and a shift factor of a wavelet function in a nonlinear segment formed by wavelet neural networks in a dynamic wavelet neural network; and d) sequentially passing the observation sequence y(n) through the dynamic wavelet neural network and a judger to obtain an output signal. The method has faster convergence speed and smaller steady-state error, thereby being completely applicable to underwater acoustic channels.

The invention belongs to the technical field of greenhouse intelligent regulation and control, and relates to a greenhouse intelligent regulation and control method based on agricultural solar term experience data. According to the method, by importing the agricultural solar term experience data and using a fuzzy neural network strategy as the basis, a fuzzy neural network controller in which a greenhouse environmental factor is coupled with a greenhouse regulation and control mode is constructed, the control precision of the fuzzy neural network controller is improved on the basis by using a neural network online backpropagation learning algorithm, auxiliary optimization is performed on a topological structure, a connecting weight, a membership function parameter or a fuzzy inference rule of the fuzzy neural network controller by using a genetic optimization algorithm, so that a genetically optimized fuzzy neural network controller is formed. According to the greenhouse intelligent regulation and control method provided by the invention, the fuzzy neural network controller is constructed based on the agricultural solar term experience data, a new idea is provided for the greenhouse regulation and control method, the guiding role of the agricultural solar term experience data on agricultural production is fully exerted, and meanwhile, the agricultural production cost is reduced.

The invention relates to a measurement and control system of plant growing nutrition solution and dissolved oxygen, which is based on fuzzy neural network control; the measurement and control system consists of a nutrition solution circulating system, a fuzzy neural network controller and a microcontroller; aiming at the problems in the regulation and control process of nutrition solution, the measurement and control system adopts a fuzzy neural network control theory to carry out intelligent control to various nutrition solutions required by plant growth and controls the parameters such as the components in the nutrition solution(e.g. NO3<->, K<+> and Ca<2+>, and the like), EC value, pH value, the concentration of dissolved oxygen, and the like, at set values which can meet the requirements for plant growth.

The invention belongs to the field of control of an electro-hydraulic servo system, and relates to a force / position cooperative control method of an electro-hydraulic servo system. In the implementing process of the method, a position output signal and a force output signal of a valve control cylinder of the electro-hydraulic servo system in a work process are analyzed, outer-loop control of force is additionally provided as feedforward compensation based on position control, a PID controller and an adaptive fuzzy neural network controller are designed to respectively and individually control a position control portion and a force control portion, and cooperative control of the position signal and the force signal of the electro-hydraulic servo system is finally realized. The object of the invention is to reduce the vibration and the impact in the work process of the electro-hydraulic servo system due to stress and improve the positioning precision and stability of the system. The method includes steps: the position control portion measures the position output signal of the valve control cylinder through a displacement sensor and feeds back the position output signal to a position signal input portion, the position output signal is compared with an input signal, and a position error signal is obtained; the force control portion measures the force output signal of the valve control cylinder through a force transducer and feeds back the force output signal to a force input portion, the force output signal is compared with a force input signal, and a corresponding force error signal is obtained; and finally the error signal of the position control portion and the error signal of the force control portion are added (namely the force error signal is regarded as feedforward compensation) as a position expected input error signal of the whole valve control cylinder, the valve control cylinder dynamically adjusts the position signal and the force signal of the valve control cylinder by employing incremental control, and cooperative control of the position and the force of the electro-hydraulic servo system is finally accomplished.

The invention discloses an ant colony-particle swarm hybrid algorithm optimized fuzzy neural network-based speed control model of an ultrasonic motor. An ant colony algorithm and a particle swam algorithm form a master-slave hierarchical structure optimized fuzzy neural network, a structural parameter of a fuzzy neural network controller is optimized by employing a global searching function of theant colony algorithm and a local searching function of the particle swam algorithm, the controller is introduced to a rotational speed control system of the ultrasonic motor, and adaptability and theintelligence of speed control of the ultrasonic motor are achieved. Simulation analysis and experiment result show that by employing an ant colony-particle swarm hybrid algorithm optimized fuzzy neural network-based speed control strategy, the adaptive tracking of the system on speed of the ultrasonic motor can be achieved, and the speed control model is small in speed pulse, high in adjustment accuracy, good in dynamic performance and high in interference-resistant capability and robustness.

The invention discloses a permanent magnet synchronous motor rotation speed controller based on a recursive fuzzy neural network. The permanent magnet synchronous motor rotation speed controller combines a bat algorithm and an artificial bee colony algorithm to form a bat-artificial bee colony hybrid algorithm, which is used for optimizing structural parameters of a recursive fuzzy neural networkcontroller, and introducing the recursive fuzzy neural network controller into a rotation speed control system of a permanent magnet synchronous motor. A simulated and experimental analysis shows that: by adopting the recursive fuzzy neural network rotation speed controller optimized based on the bat-artificial bee colony hybrid algorithm, rapid response of a permanent magnet synchronous motor control system can be realized without overshoot, the control precision is high, the robustness is good, the anti-interference capability is high, and precise rotation speed control can be realized.

The invention relates to a valve position cascade control method based on a fuzzy neural network PID controller, and belongs to the field of automatic control. According to the method, a valve position cascade control model comprising a regulating valve position control loop and a proportional valve downstream pressure control loop is established, the regulating valve position control loop is a main loop, and the valve position of a regulating valve is used as a main loop control object; the proportional valve downstream pressure control loop is used as an auxiliary loop, and the downstream pressure of a proportional valve is used as an auxiliary loop control object; the valve position cascade control model takes the valve position of the regulating valve as a control target, and a fuzzy neural network PID algorithm is adopted in the regulating valve position control loop. The problem that a traditional PID control method is poor in control effect and external disturbance can be hardlyeliminated by single-loop control due to the fact that the valve position control process is complex and changeable and a precise mathematical model is difficult to establish is solved, the valve position control process can be dynamically controlled in real time, the rapidity, accuracy and robustness of the control process are improved, and stable and continuous work of the regulating valve is facilitated.

The invention discloses a transient stability control method for ultra-high voltage direct current transmission, and proposes a transient stability control method for ultra-high voltage direct current transmission based on pi-sigma fuzzy neural network. system, using the error backpropagation neural network learning algorithm to optimize the relevant parameters of the fuzzy system, combined with UHV DC transmission system to design a pi-sigma fuzzy neural network controller with simple structure and easy implementation. This method can still provide great damping for the system when the system control structure or system operating point changes, quickly suppress system oscillation, and has a simple structure and reliable operation, and has good adaptability and robustness. The development of transmission transient stability control system provides necessary data support and favorable reference.

The invention discloses a photovoltaic power generation system reactive power control method based on a probabilistic fuzzy neural network. The method comprises the following steps: S1, a photovoltaic power generation system mathematical model is built, and maximum allowable values of active power and reactive power injected into a power grid by the photovoltaic power generation system are solved; S2, a power grid fault controller model for the photovoltaic power generation system is built; S3, a probabilistic fuzzy neural network controller is built, and reference values of active current and reactive current injected to the power grid by a three-phase inverter are solved; S4, an error back propagation learning algorithm mechanism for the probabilistic fuzzy neural network controller is built; and S5, a Boost chopper circuit inner loop controller model and a three-phase inverter inner loop current control model are built. In conditions of power grid voltage mutation and fall, the working mode of the photovoltaic power generation system can be quickly adjusted so as to be adaptive to limitations of the photovoltaic array maximum output power, grid-connected inverter rated capacity and the maximum output current, and stability is strong, and the tracking speed is quick.

The invention provides a three-phase rectification control method based on an improved adaptive fuzzy neural network, and relates to the technical field of power electronics. According to the invention, the defects of the traditional fuzzy neural network are improved; a mode of combining a type I function and a type II function is adopted; parameters are updated using an elliptical membership function through a gradient descent method; the output of the antecedent parameters with respect to the function is non-linear, and since there is a normalization layer in the structure of the fuzzy neural network, the parameter of the antecedent of each membership function exists at least in the denominator of the output of the normalization layer regardless of whether the parameter participates in the rule. Sliding mode control enables the whole system to be more stable, and can be ensure that the fuzzy neural network converges faster in the online adjustment process; and the PD controller and the improved self-adaptive fuzzy neural network controller are combined to achieve better control over three-phase rectification compared with a traditional PID controller.

The invention discloses a satellite channel complex-valued neural polynomial network blind equalization system and a method. According to the invention, a complex-valued neural polynomial network is adopted as a blind equalization module, wherein a single-layer neural network is used to compensate the linear characteristic of a satellite channel, and a nonlinear memoryless processor is used to compensate the nonlinear characteristic of the satellite channel. A six-layer fuzzy neural network controller is designed by building fuzzy control rules, and a sixth-layer weight vector is adjusted by a fixed-step constant modulus approach. The six-layer fuzzy neural network controller controls the iteration step length of the weight vectors of the single-layer neural network and the nonlinear memoryless processor at high precision. The satellite channel complex-valued neural polynomial network blind equalization system has the advantages of simple structure, high convergence rate and small steady-state error. The problem of high complex-valuedity is well solved. The contradiction between convergence rate and mean square error is overcome effectively.

The invention relates to a brushless direct current motor vector control system and a construction method thereof, and the brushless direct current motor vector control system comprises a dual closed-loop adjustor, a SVPWM controller and an inverter, wherein the dual closed-loop adjustor comprises a revolving speed adjusting external loop and a current adjusting inner loop, moreover, a revolving speed adjustor of the revolving speed adjusting external loop is suitable for being a fuzzy neural network controller; a preset revolving speed is input into the SVPWM controller after being adjusted through the dual closed-loop adjuster; and the SVPWM controller is suitable for generating a control pulse signal, and the control pulse signal is inverted through the inverter and then taken as a control signal of the brushless direct current motor. For the brushless direct current motor vector control system provided by the invention, a fuzzy neural network is introduced on the basis of the conventional control method, stability of the system is improved, and adaptive performance of the system is increased.

The invention discloses an active power filter FNN (Fuzzy Neural Network) control method based on fuzzy inversion, comprising the following steps: designing a mathematical model of an active power filter; designing an inversion controller and a sliding mode surface of the active power filter; designing a fuzzy neural network controller of the active power filter and an adaptive law thereof; and designing a fuzzy controller of the active power filter and an adaptive law thereof. The method has the advantages as follows: an active power filter can be controlled effectively and reliably, and under the condition that the parameters of the system are unknown, the parameters of the system can be estimated effectively, and the global stability of the system can be ensured; on the basis of designing the active power filter fuzzy neural network controller based on fuzzy inversion control, the dynamic control law and the adaptive law can be acquired gradually; and command current can be tracked in real time, the dynamic performance of the system can be strengthened, the robustness of the system can be improved, and the method is not sensitive to parameter variation.

The invention relates to an ultrasonic motor fuzzy neural network control method based on a base function network, comprising a base and an ultrasonic motor arranged on the base. The output shaft at one side of the ultrasonic motor is connected with a photoelectric encoder, and the output shaft at the other side of the ultrasonic motor is connected with a flywheel inertia load. The output shaft of the flywheel inertia load is connected with a torque sensor through a coupling. The signal output ends of the photoelectric encoder and the torque sensor are connected to a control system. The control system is composed of a fuzzy neural network controller based on a recursive radioactive base function network and a motor. The system of the whole controller is established on the basis of the recursive radioactive base function network, a fuzzy neural network is taken as the adjustment function, and therefore, better control performance is achieved. The control accuracy is high, the structure is simple and compact, and the using effect is good.

The invention discloses a fuzzy neural network control method for an active electric power filter. According to the method, self-adaptation control, RBF (Radial Basis Function) neural network control and fuzzy neural network control are combined. When the method is applied, firstly, a mathematic model of the active electric power filter with disturbance and error is established, and secondly, a fuzzy neural network controller is obtained based on design of a self-adaptation RBF neural network. According to the method, an instruction current is tracked in real time, the dynamic performance of a system is improved, the robustness of the system is improved, and the system is not sensitive to parameter change. Through design of the sliding mode variable structure system, the active electric power filter is ensured to operate along a sliding mode track, the uncertainty of the system can be overcome, the robustness to interference is very high, and the high control effect on a nonlinear system is realized. The nonlinear part in the active electric power filter is approximated by designing a self-adaptation RBF neural network controller. The instruction current can be tracked in real time and the robustness of the system is improved by designing the fuzzy neural network controller.

The invention provides a trajectory tracking control algorithm for a pneumatic muscle driving system. The algorithm comprises the steps that an expected trajectory of the pneumatic muscle driving system is input; a tracking error is determined according to the feedback actual trajectory of the pneumatic muscle driving system; a tracking error derivative is obtained through the tracking error; according to a hierarchical fusion deep fuzzy neural network, a control signal is obtained through combination with the tracking error and the tracking error derivative; the control signal is transmittedto a pneumatic driving joint; and the pneumatic driving joint conducts trajectory tracking according to the control signal. According to the trajectory tracking control algorithm, through a hierarchical fusion deep fuzzy neural network controller without pre-training, the problems in the existing technical scheme are effectively solved, and the trajectory tracking control of the pneumatic muscle driving system is achieved.

The invention relates to a fuzzy neural network-based oxygenator pressure control method. The method comprises the following steps of: 1, constructing a fuzzy neural network controller, wherein the fuzzy neural network controller comprises an antecedent network and a seccedent network, the antecedent network comprises four layers such as an antecedent network input layer, a fuzzification layer, afuzzy rule matching layer and a normalization layer, and the seccedent network comprises three layers such as a seccedent network input layer, a rule seccedent calculation layer and a controller output layer. The fuzzy neural network-based oxygenator pressure control method combines the advantages of fuzzy control and neural network control, is used for dynamically controlling system pressure of oxygenators in real time, is capable of solving deficiencies of oxygenators working in a manner of controlling switching via time or fixing pressure limit values, and is beneficial for stably and continuously preparing high-concentration oxygen.

The invention relates to a brushless DC motor vector control system and a construction method thereof. The vector control system comprises a double-closed-loop regulator, an SVPWM controller and an inverter, wherein the double-closed-loop regulator comprises a rotating speed regulation outer ring and a current regulation inner ring, and a rotating speed regulator of the rotating speed regulation outer ring is suitable to adopt a fuzzy neural network controller. A given rotating speed is input into the SVPWM controller after being regulated by the double-closed-loop regulator. The SVPWM controller is suitable to generate a control pulse signal, and the control pulse signal is inverted by the inverter and then is used as a control signal of the brushless DC motor. According to the invention,the fuzzy neural network is introduced on the basis of a traditional control method, the stability of the system is improved, and the self-adaptive performance of the system is improved.

The invention discloses an automatic gear shifting control method based on a fuzzy neural network. The automatic gear shifting control method comprises the following steps that corresponding gear values of a vehicle driven by an excellent driver at different accelerator opening degrees and driving vehicle speeds are collected; a T-S model is selected for designing a fuzzy neural network controller, and the collected vehicle speed, accelerator pedal opening and corresponding gear information serve as training samples to train a training model; the trained model is embedded into a gear shifting controller, and when the actual accelerator pedal opening degree and the vehicle speed are input into the gear shifting controller, a target gear is output through calculation; and the output gear information acts on a real vehicle system, so that control output is realized. According to the method, the fuzzy algorithm and the neural network algorithm are used in an integrated mode, the trained model can generate a complex nonlinear function through a small number of fuzzy rules, parameter identification and setting are conducted from the excellent driver operation angle, and the whole vehicle drivability is improved.

The invention discloses a weight control method based on a bi-clustering adaptive fuzzy neural network, which is characterized in that according to habits of field operators, existing data are utilized to realize weight control under the conditions of no target value record and expert experience. The method comprises the following steps: obtaining a weighing bin weight target value and a fuzzy rule among weighing bin weight deviation, deviation change rate and feeding quantity deviation by utilizing biclustering, further learning the fuzzy rule by utilizing a fuzzy neural network, and finally obtaining a biclustering adaptive fuzzy neural network controller, thereby realizing control on the weight of the weighing bin. According to the invention, the target value of the weight of the weighing bin can be adaptively obtained, and the experience of an operator is learned to obtain the dual-clustering adaptive fuzzy neural network controller, so that the real-time control of the weight of the weighing bin can be realized.

The invention discloses a corn harvester loss rate control method and device, a storage medium and equipment. The method comprises the following steps: constructing a dual-input single-output fuzzy neural network controller which comprises a front part network and a rear part network; carrying out offline learning by adopting a genetic algorithm-particle swarm algorithm, and determining a weight, a membership function center and a width which need to be learned in the fuzzy neural network controller by learning prior operation data of a system; carrying out online learning by adopting a BP algorithm, establishing the connection weight of the controller, detecting the rotating speed of the corn harvester kernel recovery device in real time, and adjusting adjustable parameters in the controller in real time in combination with online learning, so that the controller adapts to mechanical property changes of the corn harvester kernel recovery device and tracks a corn harvest rate set value. The invention further provides a corresponding corn harvester loss rate control method and device, a storage medium and equipment.