53 results about "Nonlinear predictive control" patented technology

The disclosed process control system makes use of Optimal control (OC) and model predictive control (MPC) techniques for selection of the Expert Systems (ES) targets values U. The ES target values U are selected to minimize the performance criterion J. A mathematical model of an extended system given by the process P and the ES is developed. This hybrid mathematical model has both continuous dynamics and logical relationships. Controlled variables of the mathematical model are the ES target values U and inputs are the measurements y and the performance criterion J. The OC and / or MPC techniques are used to compute values U. An optimizer of the OC / MPC selects values of the ES target values U only. This activity has lower sampling rates than selection of controller values, which simplifies the design of the OC / MPC controller.

The invention discloses a combustion optimization control method for a boiler. The combustion optimization control method is characterized by comprising the following steps: sampling a combustion nonlinear system of the boiler to obtain input / output data at the current moment; training the real-time sampled input / output data by an online incremental learning fuzzy neural network, building an online incremental learning predicting model of the combustion nonlinear system of the boiler; performing a nonlinear prediction control algorithm on the online incremental learning predicting model for realizing the optimization and the control of the combustion process of the boiler. According to the combustion optimization control method for the power station boiler of the online incremental learning fuzzy neural network, the nonlinear optimization problem in the predication control algorithm is solved by utilizing a particle swarm optimization algorithm through the online identification of the boiler combustion optimization model; the real-time optimization and control of the boiler combustion process are realized.

The invention relates to a novel gray wolf optimization algorithm-based aero-engine nonlinear predictive control method. The method includes the following steps of: prediction model establishment: Gaussian white noises act on an aero-engine as input data, so that corresponding output data are obtained, as for the acquired input data and output data, a BP neural network method is utilized to offline train a corresponding neural network model, and a prediction model is established through using a recursive method on the basis of the neural network model; feedback correction: feedback correction is performed on the output value of the prediction model according to the deviation of the output value of the prediction model at a k time point and the actual output value of the engine; and rolling optimization: a novel gray wolf optimization algorithm is adopted to perform solving, with the difference of the output value of the prediction model and the output fixed value of the engine adopted as input, rolling optimization is performed, and therefore, optimal control quantity fuel flow can be obtained. Compared with an ordinary aero-engine control method, the method has better robustness and constraint processing capacity.

The invention discloses an interior point algorithm based LPV (Linear Parameter Varying) model nonlinear predicating control method. The method comprises the steps of linearizing a complex mechanism model of a system at a set working point to obtain a plurality of linear sub-models; selecting weight functions; weighting each linear sub-model to obtain a global approximation model of the system, namely, PLV model; treating the LPV model as the predicating model and selecting the secondary performance index function to create a nonlinear predicating control topic; solving the optimal topic by the interior point algorithm during rolling optimizing so as to obtain the optimal control sequence for achieving the nonlinear predication control. Compared with the prior art, the method has the advantages that the LPV-based full-simultaneous direct solving is performed, so that the solving precision is high, and the algorithm needs a little time; the control effect is obvious; the transition process of the system is reduced; the resource consumption is reduced; the control quality of the system is particularly obviously improved under a large variable working condition range.

The invention discloses a method for controlling non-linear prediction of a helicopter for spinning recovery. The method includes the following steps: after entering spinning, using a pre-trained helicopter requirement torque model to conduct real-time online prediction on current helicopter requirement torque; after entering the stage of spinning recovery, using a pre-trained engine dynamic parameter model to conduct real-time online prediction on current engine dynamic parameters, at the same time using online prediction results of the helicopter requirement torque model and the engine dynamic parameter model to resolve so as to reduce a difference between a helicopter requirement torque upon the connection of a clutch and a torque support provided by an engine, taking into consideration of rolling optimization of operation conditions for stability and safety of the engine, taking a first item of controlled variable sequence that is solved as the helicopter control variable that is currently input. The invention also discloses an apparatus for controlling non-linear prediction of the helicopter. According to the invention, the method and the apparatus can effectively shorten time delay at the stage of spinning recovery and reduce rotating speed transient downslide of helicopter rotors.

The invention, which belongs to the field of the FPGA realization technology, relates to a non-linear-model-predictive-control-based FPGA hardware acceleration controller and an acceleration realization method. The invention aims at extending application of non-linear model predictive control (NMPC) in rapid dynamic system; and a non-linear-model-predictive-control-based FPGA hardware acceleration controller and an acceleration realization method in non-linear planning can be realized by using a PSO algorithm. For a hardware acceleration controller, a WMR prediction control model is established; according to a control requirement of target model WMR trajectory tracking, an optimization problem is solved; and an NMPC-PSO algorithm flow is executed. According to the invention, on the basis of a one-to-one correspondence relationship between codes and bottom circuits, the parallel computing structure of the FPGA and the parallel computing characteristics of the PSO algorithm can be combined well, so that the rapid computing capability of the NMPC is improved, the real-time requirement of the controller can be met well, and NMPC application in the actual rapid dynamic system can be extended. Meanwhile, on-line flexible reducing, extending, and upgrading of the scheme can be realized; and thus the controller and the realization method can adapt to the current situation of the fast product updating speed; and the controller can be verified rapidly.

The invention relates to a non-linear prediction control system in an internal thermal coupling distillation process, which comprises a field intelligent instrument and a DCS system which are directly connected with an internal thermal coupling distillation tower; the field intelligent instrument is connected with a storage device, a control station and an upper computer; the upper computer comprises a non-linear prediction controller which is used to roll, optimize and solve a control law and output a control variable; the non-linear prediction controller comprises a component deduction module, a model parameter self-adaptive correction fitting module, and a control law rolling, optimizing and solving module; the component deduction module is used to obtain temperature and pressure data from the intelligent instrument, and calculate the component concentration of all tower plates of the high-efficiency energy-saving distillation tower, the model parameter self-adaptive correction fitting module is used to adopt the component concentration data calculated by the component deduction module in a historical database and fits the model parameters on line; and the control law rolling, optimizing and solving module is used to optimize and solve the ideal value of the current control variable according to the current component concentration data, model functions and the current time operation variable. The invention also provides a non-linear prediction control method. The invention has good control effect and ideal control quality.

A carrier-based aircraft autonomous landing method based on explicitly nonlinear model predictive control comprises a first step of simplifying a longitudinal motion nonlinear equation according to a flying condition of an aircraft; a second step of determining a control quantity and a controlled quantity; a third step of determining a relative order of each output quantity; a fourth step of, as K is a diagonal matrix composed of Ki, solving for the value of Ki through a first row of a matrix (as shown in the description); a fifth step of carrying out Taylor expansion for a command signal yDi, and obtaining an all-order derivative function of the command signal; a sixth step of carrying out integration to obtain the control quantity; and a seventh step of obtaining a carrier-based aircraft autonomous landing longitudinal control law based on the explicitly nonlinear model predictive control. The invention enables a carrier-based aircraft to quickly track the command signal autonomously, avoids online optimization process, saves a lot of time, and saves hardware resources. The explicitly nonlinear model predictive control can be widely applied to the nonlinear control fields of robots, aviation, aerospace, industrial production and so on.

The invention, which relates to the technical field of cut tobacco drying process moisture control, discloses a cut tobacco drying process moisture prediction control method and system based on a recurrent neural network. The method comprises the steps: step A, collecting related data of a cut tobacco drying process; step B, automatically identifying acquired trademark information to obtain control parameters; step C, judging the related data, and establishing a nonlinear prediction control model; step D, converting a nonlinear prediction model into a nonlinear prediction control model based on a recurrent neural network, and updating the weight of the recurrent neural network to obtain an outlet moisture content prediction value; and step E, constructing a performance index J, and acquiring an optimal moisture removal air door opening degree of the performance index J. According to the method, the nonlinear prediction control model is improved, the neural network training speed and stability are improved, and the stability of the outlet moisture content is improved.

The invention discloses a nonlinear prediction control design method for a variable cycle engine, and the method comprises the steps: building a standard form of a variable cycle engine model block diagram; obtaining a nonlinear generalized minimum variance controller according to the standard form; estimating the impact on the control performance of the variable cycle engine from all design parameters of the nonlinear generalized minimum variance controller; and carrying out the contrastive analysis to determine the design parameters of an optimal controller. Aiming at the problems that a variable cycle engine is strong in nonlinearity, is difficult to model and is not high in model precision, the method employs a novel nonlinear generalized minimum variance control method, and can enablethe control performance of the variable cycle engine to be remarkably improved.

The invention discloses a design of a nonlinear predictive controller for a permanent magnet synchronous motor with a disturbance observer, and belongs to the technical field of a high-performance motor driving control system. According to the invention, firstly, all model errors and external disturbances are considered under a dq coordinate system, and a nonlinear mathematical model of the PMSM (Permanent Magnet Synchronous Motor) is constructed; secondly, predictive controllers of an outer speed loop and an inner current loop are respectively designed on the basis of the model, and a disturbance observer is designed when there is a control device limitation. The design disclosed by the invention overcomes a defect that the system has limitations for processed variables and greatly depends on electric parameters of the motor on the aspect of the limiting current through designing the nonlinear model predictive controller with a cascade structure and designing the anti-saturation disturbance observer, and the disturbance is considered and compensated in predictive controller, thereby enhancing the robustness of the permanent magnet synchronous motor control system. Experiments showthat the method can enable the system to output a more accurate tracking reference trajectory, and it is considered that the current limitation can keep high robustness when there is a model parameter error or load change at the same time.

The utility model discloses a nonlinear predictive control method of a trough reactor based on a multi-kernel support vector machine (SVM), belonging to the field of industrial automatic control, which mainly comprises the modeling based on the multi-kernel support vector machine and the closed loop design of the predictive control; wherein, the multi-kernel support vector machine comprises a dynamic part based on a linear kernel support vector machine and a static part based on a spline kernel support vector machine which are connected in series; according to the static and dynamic input and output data of the trough reactor, the model of multi-kernel support vector machine is created to change the future reference path of the temperature of the trough reactor by the inverse based on the spline kernel support vector machine, to change the nonlinear predictive control into the linear predictive control which aims at the linear kernel support vector machine model and to give the optimal control law analytical solution with unified form of multi-step predictive control according to predictive control mechanism; the optimal control law analytical solution is acted on the reactor to raise the temperature to the set value and to complete the control circulation.

The invention discloses a quick non-linear predictive control method for voltage of a solid oxide fuel cell (SOFC). The method has strong non-linearity aiming at an SOFC object, the non-linearity of the object is mainly reflected in characteristics on object gain, and a relational model between each item of coefficient in a gain related polynomial in an SOFC system discrete model and load current is fitted by performing local linear model identification on a plurality of load point objects so as to be used in on-line calculation of the control quantity of predictive control. According to the method, the calculation quantity of the predictive control algorithm is reduced, on-line control is facilitated, meanwhile, the influence of the strong non-linearity of the fuel cell on the fuel cell control can also be effectively solved, and the SOFC voltage control quality is improved. In addition, the method considers the limitation of fuel utilization rate in an operation process, and dynamic constraint varying with load is added in the predictive control algorithm so as to guarantee that the fuel utilization rate of the SOFC is kept in a rational range all the time.

The invention provides a nonlinear prediction control system for an energy-saving air separation process, which comprises a field intelligent instrument and a DCS system which are directly connected with an air separation tower. The DCS system comprises a memory device, a control station and an upper computer, wherein the intelligent instrument is connected with the memory device, the control station and the upper computer; the upper computer comprises a nonlinear prediction controller which has a function of optimizing and solving a control law output operation variable value; and the nonlinear prediction controller comprises a component inferring module, a model parameter self-adaptation correction module and a control law rolling optimizing solution module. The invention also provides a nonlinear prediction control method for the energy-saving air separation process. The invention provides the nonlinear prediction control system and the nonlinear prediction control method for the energy-saving air separation process, which can effectively achieve highly accurate tracking control effect, have a fast online solution speed and greatly improve the work efficiency.

The invention discloses an electric automobile compound energy management method based on a rule and nonlinear prediction control. By the adoption of the method, energy management is conducted according to the power requirement of a vehicle at every moment and the SOCs of a lithium battery and a super capacitor. In a nonlinear prediction control strategy, a controller will predict the speed in a certain period in the future, the speed is converted into power through a vehicle running speed and power model, the output current of the lithium battery is optimized with the minimum power consumption as an index through a quadratic programming effective set method, and power distribution of the lithium battery and the super capacitor is finished. By the adoption of the method, on the basis thatthe required power is met, system energy loss can be reduced, use of the lithium battery is reduced, the service life of the lithium battery is prolonged, and the efficiency of a hybrid power system is improved.

The invention relates to a multi-objective optimization control method for predictive control of a desulfurization system based on a multi-modal model. The multi-objective optimization control methodfor predictive control of the desulfurization system based on the multi-modal model comprises the following steps: establishing a database by taking historical data of the desulfurization system and ahost system recorded by a DCS control system as a data source for process characteristic analysis; performing normalization processing on the database; establishing an absorption tower model, establishing a slurry pool model, establishing a desulfurization efficiency model and establishing other necessary models and connecting units; identifying a steady-state point linear state space; and predicting future system dynamics based on the linear state space model. According to the multi-objective optimization control method for predictive control over the desulfurization system based on the multi-modal model, multi-objective steady-state optimization, uncertainty compensation and nonlinear predictive control are combined, and multi-objective real-time optimization control over the desulfurization system is achieved.

The invention discloses a rapid variable load optimizing control method for an oxygen and nitrogen rectification outer compression air separation plant. A two-layer system structure in which large-range technology optimization and nonlinear predictive control are combined is adopted in the rapid variable load optimizing control method. A technology optimization calculation (RTO) module and a model predictive control (MPC) module are included. The RTO module calculates the optimal steady-state value of the process variable related to the load change according to the variable load requirement of the device and through the air separation low-temperature deep cooling technology optimizing calculation and sends the optimal steady-state value to the multi-variable predictive control MPC module; and the MPC module gradually pushes the device to the optimal steady-state work point obtained through calculation of the RTO module on the premise that equipment constraint is not violated and the product quality is guaranteed. The method solves the problem about nonlinearity in the variable load process, the problem about operation coupling, the problem about time optimum and the problem about energy consumption optimization, fluctuation of key variables of equipment can be more effectively reduced, and variable load operation is more rapidly and stably achieved.

The invention discloses a liquid level control method of a dual-loop water tank based on model prediction; the method comprises the steps: performing off-line recognition to the liquid level system of the water tank according to the openness of an input valve of the liquid level system of the dual-loop water tank and the historical data of the output liquid level, and establishing an overall non-linear mathematic model of the system depending on a liquid level non-linear auto-regression (ARX) model of the water tank; optimizing the parameters of the non-linear ARX model and confirming the order of the non-linear ARX model by utilizing an AIC (Akaike Information Capacity) criteria; designing a non-linear predication control algorithm by means of a standard quadratic performance index, obtained by the off-line recognition, based on the overall non-linear mathematic model depending on the liquid level non-linear ARX model of the water tank; acquiring a prediction controlled quantity v at each sampling moment by means of the global non-linear characteristic depending on the liquid level non-linear ARX model of the water tank; and realizing the liquid level control performance index by setting the parameters of a predication controller. With the combination of a system recognition technique and an automatic control technique, the method is a modeling and prediction control method with wide applicability for the liquid level system of the dual-loop water tank.

The invention discloses a nonlinear predictive control method for acquiring the maximum wind energy of variable-speed wind turbine generator system. The method includes: introducing a dynamic area toconstrain the allowed range of generator torque; determining the finite control set of candidate generator torque; searching for optimal generator torque sequence; using the first element of the optimal generator torque sequence as the controller output. The method has the advantages that the optimal generator torque sequence is directly searched on the basis of a variable-speed wind turbine generator system nonlinear prediction model, so that the provided controller can sufficiently utilize a large prediction range; the method has high efficiency and excellent performance in maximum wind energy acquiring.

The present invention belongs to that technical field of control and plastic forming, and particularly relates to a full closed-loop control system and method based on a servo press without a sensor.The whole electric control system of the present invention is provided with a process curve interpolation algorithm, dynamic analysis and model building module, a nonlinear prediction controller and arobust controller; a speed observer without a position sensor is adopted in a servo driver, including a Luenberger observer. The difference between a process forming curve and the actual position ofa sliding block is input into the dynamic analysis and model building module, the given output torque is taken as the input parameter of the robust controller, the nonlinear prediction controller replaces the function of PI in the robust controller, the output of the nonlinear prediction controller is taken as the input parameter of the robust controller, and the output of the robust controller istaken as that of the Luenberger observer, so that the position of the sliding block is precisely controlled, and the problem of overshooting in the position control mode of the traditional servo press is solved.

The invention discloses a memory, a heating furnace outlet temperature control method, a heating furnace outlet temperature control device and heating furnace outlet temperature control equipment. The nonlinear predictive control method comprises the following steps of: presetting a set value of a nonlinear predictive controller; constructing an object model group of the nonlinear control system through model identification; generating a dynamic equation of the object model group, and calculating disturbance characteristics of the object model group according to the dynamic equation; respectively acquiring an actual output measurement value and a state estimation value of the nonlinear control system at the current moment; calculating the optimal state estimation value of the nonlinear control system at the next moment through an extended Kalman filter; and substituting the optimal state estimation value of the dynamic equation of the object model group into a nonlinear predictive control algorithm of the nonlinear control system to obtain an optimal solution, wherein the optimal solution is used for input of the object model group. According to the heating furnace outlet temperature control method, the heating furnace outlet temperature control device and the heating furnace outlet temperature control equipment, the outlet temperature of the heating furnace can be effectively controlled, and the control failure phenomenon is reduced.

The invention discloses a memory, a non-linear predictive control method, a non-linear predictive control device and non-linear predictive control equipment. The non-linear predictive control method comprises the following steps of: constructing an object model group of a non-linear system through model identification; generating a dynamic equation of the object model group, and calculating disturbance characteristics of the object model group according to the dynamic equation; respectively acquiring an actual output measurement value and a state estimation value of the nonlinear system at the current moment; calculating the optimal state estimation value of the nonlinear system at the next moment through an extended Kalman filter; and substituting the optimal state estimation value of the dynamic equation of the object model group into a nonlinear predictive control algorithm of the nonlinear system to obtain an optimal solution, wherein the optimal solution is used for input of the object model group. According to the non-linear predictive control method, the non-linear predictive control device and the non-linear predictive control equipment, the influence caused by model mismatch can be reduced by reducing the frequency and degree of model mismatch.

The invention discloses a method for controlling a satellite nominal orbit unbiased flight. The method comprises the following steps of: establishing a motion equation of the satellite relative to a mass block; constructing a nonlinear prediction control model according to the motion equation; rolling and optimizing to calculate the optimal thrust control quantity borne by the satellite accordingto the nonlinear prediction control model; and enabling the satellite to fly along a nominal orbit in an unbiased way according to the optimal thrust control quantity. The method provided by the invention solves the technical problem of insufficient precision existing in the satellite nominal orbit unbiased flight control in the prior art. The invention effectively improves the accuracy of the satellite nominal orbit unbiased flight control in the prior art.

The present invention discloses a robot locus tracking method, and belongs to the technical field of robot control. A robot kinematic model is constructed according to geometrical parameters between each joint variable of the robot and a connecting rod, and a robot tail-end position model is obtained according to the kinematic model; a robot kinetic model with driving system parameters is established; a prediction model for predicting an actual tail-end position of the robot is established according to the tail-end position model and the robot kinetic model; and a non-linear prediction controller is established according to the prediction model, a first constrained condition of the joint variables and a second constrained condition of an input voltage of a drive system. According to the kinematic model and the kinetic model of the robot, and the dynamics of the driving system of the robot and the constraint problem of a work environment where the robot is located, the non-linear prediction controller of robot locus tracking control is established to allow the locus tracking control of the robot to be more accurate and to better meet the actual application demand.

The invention discloses a vehicle transverse and longitudinal coupling nonlinear model prediction controller based on parallel Newton solution, which obtains a transverse and longitudinal coupling nonlinear control model through a vehicle three-degree-of-freedom kinetic model, and by adopting a front wheel steering angle and front and rear wheel driving force as control variables, according to a model prediction control algorithm, vehicle physical constraints are considered and a cost function is constructed. Aiming at the vehicle path tracking control problem, a transverse and longitudinal coupling control model is obtained by using vehicle dynamics, a nonlinear model prediction controller is designed by using the model, and rapid solution of the nonlinear controller is realized by usinga parallel Newton method. The vehicle transverse and longitudinal coupling path tracking nonlinear model prediction controller is derived through a vehicle three-degree-of-freedom kinetic model, the mutual influence between the transverse direction and the longitudinal direction is considered, the nonlinear prediction controller is designed according to the model, the nonlinearity of a vehicle system is reserved, and the model precision is ensured.

The present invention provides a closed loop neural stimulation simulation system for a Parkinson state. The method comprises a basal nucleus- thalamus loop, a DAC, an ADC, a non-linear autoregressive Volterra model and a non-linear model prediction controller, which are connected to each other. The system comprises a basal nucleus-thalamus loop that is established by an FPGA, a DAC, an ADC, a non-linear autoregressive Volterra model and a non-linear model prediction controller. The DAC and the ADC are respectively connected to an input end and an output end of the basal nucleus-thalamus loop, and are used for acquiring input and output data of the basal nucleus-thalamus loop. The effects of the system are as follows: a basal nucleus-thalamus loop modeling method driven by physiological data is proposed, so that modeling mismatching and uncertainty that exist in conventional physiological modeling can be effectively dealt with; a non-linear prediction control policy based on the model is capable of automatically and optimally adjusting a clinical state; and an FPGA hardware technology is used as an implementation means, and the characteristics of an FPGA parallel operation, a high operation speed and high calculation precision are fully exerted, and the response speed and control effect of a control system are improved.

The invention belongs to the technical field, and particularly relates to a multi-point nonlinear prediction control system of a press machine. According to the multi-point nonlinear prediction control system of the press machine, by measuring the travel positions of a sliding block near different force application points are measured, the rotating speed and the torque of a motor corresponding todifferent force application points are controlled through a nonlinear servo control method, and thus the stroke positions of different force applying points of the sliding block are equal, synchronouscontrol and nonlinear control of the multi-point press machine are realized, the defect that mechanical vibration is easily generated is overcome, and the problem that the mechanical coupling multi-point synchronous control leads to inclination of the plane of the sliding block due to too large pressure deviation by is solved.