661 results about "Nonlinear model" patented technology

A nonlinear model-based predictive temperature control system is described for use in thermal process reactors. A multivariable temperature response is predicted using a nonlinear parameterized model of a thermal process reactor. The nonlinear parameterized model is implemented using a neural network. Predictions are made in an auto-regressive moving average fashion with a receding prediction horizon. Model predictions are incorporated into a control law for estimating the optimum future control strategy. The high-speed, predictive nature of the controller renders it advantageous in multivariable rapid thermal processing reactors where fast response and high temperature uniformity are needed.

A nonlinear model-based predictive temperature control system is described for use in thermal process reactors. A multivariable temperature response is predicted using a nonlinear parameterized model of a thermal process reactor. The nonlinear parameterized model is implemented using a neural network. Predictions are made in an auto-regressive moving average fashion with a receding prediction horizon. Model predictions are incorporated into a control law for estimating the optimum future control strategy. The high-speed, predictive nature of the controller renders it advantageous in multivariable rapid thermal processing reactors where fast response and high temperature uniformity are needed.

An arrangement and method for converting an input signal z(t) into a mechanical or acoustical output signal p(t) comprising an electro-magnetic transducer using a coil at a fixed position and a moving armature, a sensor, a parameter measurement device and a controller. The parameter measurement device identifies parameter information P of an nonlinear model of the transducer considering and the saturation and the geometry of the magnetic elements. A diagnostic system reveals the physical causes of signal distortion and generates information for optimizing the design and manufacturing process of this transducer. The controller compensates for nonlinear signal distortion, stabilizes the rest position of the armature and protects the transducer against mechanical and thermal overload.

A method and apparatus capable of monitoring performance of a process and of the condition of equipment units effecting such process is disclosed. A process model predicated upon mass and energy balancing is developed on the basis of a plurality of generally nonlinear models of the equipment units. At least one or more of such equipment models are characterized by one or more adjustable maintenance parameters. Data relating to mass and energy transfer within the process is collected and is reconciled with the mass and energy characteristics of the process predicted by the model. The condition of the equipment units and process performance may then be inferred by monitoring the values of the maintenance parameters over successive data reconciliation operations.

A method and apparatus are disclosed for generating a reduced nonlinear model, whose significant properties include accuracy, compact size, reliability and speed. These properties enable nonlinear real-time optimization and detailed analysis of any operation that can be modeled this way. An array of related applications is disclosed for providing a deep understanding of the behavior of the optimum of an operation. Such understanding includes the ability to directly visualize an objective function response surface and the detailed interrelationships between independent and dependent variables for optimal operation. Additional applications are disclosed for verbally expressing operating instructions based on optimality of an operation, and an accurate means to quantify the ongoing benefits due to nonlinear optimization.

A method of and a system for stabilizing High Voltage Power Supply (HVPS) DC and AC voltages in multi-energy X-ray computed tomography scanners are provided. The method comprises generating filter ratios, computing DC and AC voltages, and feeding back the computed DC and AC voltages to the commanded voltages. The filtered ratios including an air ratio and a copper ratio are modeled as nonlinear functions of the DC and AC voltages. Computing DC and AC voltages include computing an m-ratio and an n-ratio. The parameters of the nonlinear model comprise an exponent parameter and a set of polynomial coefficients. The parameters are determined by a calibration procedure, which performs scanning at different combination of DC and AC voltages. The optimal parameters are obtained through a nonlinear least square minimization, which is solved through a brute force search over the exponent parameter and a closed form solution of the polynomial coefficients. Feeding back the computed DC and AC voltages include comparing the computed voltages with commanded voltages, integrating the difference between the computed voltages and commanded voltages, and adding the integrated voltage differences to the commanded voltages.

A soft measurement method of industrial production melt index on PP polymerization includes selecting nine variables influencing variation of melt index as input variables of soft measurement mode, using master element analysis to pick up main composition from input variables for eliminating off relativity between variables, applying radial based function neuro-network to set up nonlinear model between input and output as well as simultaneously utilizing fuzzy genetic algorithm to carry out optimum selection on model parameter.

The invention discloses a vehicle multiparameter identification method based on a multitask convolution nerve network. The method comprises the following steps of designing and training of a convolution nerve network structure; and vehicle parameter identification based on the convolution nerve network. In the invention, through the convolution nerve network, original data is converted into an abstract high-level expression through a simple and nonlinear model. Therefore, in the convolution nerve network, a recessive character reflecting an essence of an object to be identified can be learned from a lot of training samples. Compared to a shallow learning classifier, by using the method, high expandability is possessed, identification of various kinds of objects in a traffic environment is satisfied and identification precision is high. When being applied to the complex traffic environment, the method has a high anti-environment interference capability. In the invention, the convolution nerve network is applied to multiparameter identification of a vehicle; the trained convolution nerve network is used to identify a type characteristic, pose information and a vehicle light state of the vehicle in an image so that predictable performance of a potential vehicle behavior is enhanced.

The invention discloses a nonlinear robust controller design method based on back-stepping and sliding mode control technologies and aimed at a nonlinear model of a quad-rotor unmanned plane. A sliding mode controller of a speed-constant reaching law is designed to an attitude angle system of the quad-rotor unmanned plane, and rapid tracking for the attitude angle is ensured. To realize track tracking for the spatial position of the quad-rotor unmanned plane, a sliding mode surface and a virtual control quantity are constructed according to a stepping back control idea to realize nonlinear control law design and system stability design from the kernel to the external layer of a system. After an equation of the related virtual control quantity is obtained, an expected track value of the attitude angle is obtained by solving the equation via arithmetic inverse operation, and a design method of the speed-constant reaching law is used to determine a final input control law of the quad-rotor unmanned plane system. According to the method of the invention, the characteristic that sliding mode control is uncertain for the model and insensitive to external interference is utilized, robust trace tracking for the nonlinear quad-rotor unmanned plane can be controlled under interference.

The invention discloses a credit assessment model training method, and a credit assessment method and apparatus. The training method comprises the steps of obtaining original training behavior data of a training user in a business system; extracting original training features in the original training behavior data; performing feature combination on the original training features according to a GBDT model to generate corresponding training cross combination features; and performing training on a logistic regression model according to the training cross combination features to build a credit assessment model. According to the method, the original training features are trained through the nonlinear GBDT model to generate the corresponding training cross combination features, and the linear LR model is trained through the training cross combination features to build the credit assessment model, so that the credit assessment model has not only high performance of the nonlinear model but also interpretability of the linear model.

The invention discloses a pre-distortion model device, a signal pre-distortion treatment apparatus, a signal pre-distortion processing system, and a method for pre-distortion treatment of signal by utilizing the pre-distortion model device. Only one pre-distortion parameter should be exported by the pre-distortion model that the invention realized, and can cascade the complex multiplier, and can calibrate memory distortion and transient distortion of signal amplification equipment simultaneously. By using the invention, nonlinear model of the signal amplification equipment can not be extracted, which directly extract the pre-distortion model, thereby reducing the complexity of digital pre-distortion treatment, and can reduce the error.

A method and apparatus is featured for providing linear control of a regulated electrical property (e.g., current or voltage) in a switch mode power supply of a thermal processing system that includes an inductive element and at least one switching element. The method and apparatus feature structure, or steps, for generating a nonlinear model that predict values for a regulated electrical property of the inductive element based on a given duty cycle of the at least one switching element and structure, or steps, for generating a nonlinear model that determines a duty cycle for the at least one switching element based on the nonlinear predictive model for the regulated electrical property, the nonlinear model determining the duty cycle such that a linear relationship results between the regulated electrical property and a selected value. A method and apparatus is also featured for providing cross regulation between pre-regulator and post-regulator power stages of a switch mode power supply of a thermal processing system.

A method for controlling flying of a tilt-rotor unmanned helicopter in a transition section is characterized in that the optimal preview control is adopted, and by fusing system motion equation constraints, a preview ideal state and the optimal performance index constraint information, the flying of the tilt-rotor unmanned helicopter in the transition section is controlled; specifically, the method comprises the steps that firstly, a nonlinear model is set up for the transition section of the tilt-rotor unmanned helicopter, and then a liner equation for state space description is obtained in the mode that trimming and linearization at different nacelle dip angles are achieved; secondly, based on the linear model, the optimal preview controller is designed, and the flying speed, the flying height and the flying attitude angle in the transition process are controlled; finally, inertia delay desalted equipment is designed for achieving smooth transition of the tilt-rotor unmanned helicopter between different nacelle dip angles. According to the method for controlling flying of the tilt-rotor unmanned helicopter in the transition section, by fusing a predicable expected speed, a height, an attitude angle and other information, safe switching between modes of the tilt-rotor unmanned helicopter is achieved.

The invention belongs to the technical field of aero-engines, and particularly relates to an aero-engine nonlinear model modeling method. The method comprises the following steps: calculating an engine design point nonlinear model, and calculating a transmitter transition state nonlinear model; the method comprises the steps that an engine subsection model is established, and the model is mainly divided into an air inlet channel, a fan, a gas compressor, a combustion chamber, a turbine and an exhaust port according to the structure and function of an aero-engine; describing the aerothermodynamic law obeyed by each component during working by using a nonlinear equation in sequence, and establishing a component-level model of an aeroengine design point; the latter assumes that the characteristics of the parts in the transition state are the same as those in the steady state, and the volume effect of the parts is considered in numerical simulation in the transition state; only the volumetric effect of parts with large volumes is considered in calculation, and meanwhile momentum conservation is simplified to be replaced by the total pressure recovery coefficient of the channel, so thatenough engineering precision can be ensured, and calculation is greatly simplified.

An arrangement and method for converting an input signal z(t) into a mechanical or acoustical output signal p(t) comprising an electro-magnetic transducer using a coil at a fixed position and a moving armature, a sensor, a parameter measurement device and a controller. The parameter measurement device identifies parameter information P of an nonlinear model of the transducer considering and the saturation and the geometry of the magnetic elements. A diagnostic system reveals the physical causes of signal distortion and generates information for optimizing the design and manufacturing process of this transducer. The controller compensates for nonlinear signal distortion, stabilizes the rest position of the armature and protects the transducer against mechanical and thermal overload.

The invention relates to a vehicle yaw stability predicting model control method and belongs to the technical field of a vehicle control. The disturbance-observer-based vehicle yaw stability predicting model control method uses a model predicting control method to design a linear model predicting controller, considers constraint conditions and can reduce computation time, track an expected value as much as possible and keep vehicle stability. The method includes: designing a reference model; performing linearization on a vehicle two-freedom-degree nonlinear model to obtain a linear model withmodel error disturbance terms; designing error terms in a disturbance observer pair model according to the linear model; using a model predicting control algorithm to build a target function, and solving an optimal problem corresponding to a cost function to obtain control input acting to a system to allow the vehicle system to track the expected value as much as possible so as to guaranteed vehicle stability. By the method, model complexity can be lowered effectively, control precision requirements are satisfied, and the constraint of driving torque is considered.

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 nonlinear-model-based SINS / DVL (strapdown inertial navigation system / doppler velocity log) integrated navigation method. The method comprises the following steps of: building a quaternion-based SINS nonlinear speed, posture and position error model according to the working principles of an SINS and a DVL, and confirming an error model of the DVL; building a state equation of the systems according to the error models of the two systems, measuring by taking the difference between the actually-measured speeds of the SINS and the DVL as the quantity, and building a measurement equation of the system; discretizing an actual continuous system to obtain a discrete nonlinear model which is convenient to compute; initializing the system, and computing sampling points and corresponding weight numbers by the discrete nonlinear model and unscented conversion; and sequentially carrying out time update and measurement update on unscented kalman filter on the basis of the discrete nonlinear model according to the constructed Sigma point. After the SINS / DVL integrated navigation system is used, information of each subsystem can be effectively used, and the best of each subsystem can be taken, so that the overall positioning accuracy can be greatly improved; the UKF (unscented kalman filter) estimation can be carried out by the nonlinear model of the SINS / DVL integrated navigation system, so that the positioning error of the system can be effectively reduced and the accurate positioning of the navigation system can be realized better.

Advanced combustion modes, such as PCCI, operate near the system stability limits. In PCCI, the combustion event begins without a direct combustion trigger in contrast to traditional spark-ignited gasoline engines and direct-injected diesel engines. The lack of a direct combustion trigger encourages the usage of model-based controls to provide robust control of the combustion phasing. The nonlinear relationships between the control inputs and the combustion system response often limit the effectiveness of traditional, non-model-based controllers. Accurate knowledge of the system states and inputs is helpful for implementation of an effective nonlinear controller. A nonlinear controller is developed and implemented to control the engine combustion timing during diesel PCCI operation by targeting desired values of the in-cylinder oxygen concentration, pressure, and temperature during early fuel injection.

The invention aims at providing a novel CKF(Crankshaft Fluctuation Sensor)-based SINS (Ship Inertial Navigation System) large misalignment angle initially-aligning method comprising the following steps of: determining an initial position parameter of a carrier by using a GPS (Global Position System); acquiring data output by an optical fiber gyroscope and a quartz accelerometer; finishing the coarse alignment of the system by using an analysis method; preliminarily determining the posture information of the carrier; establishing an initial aligning nonlinear model of a strapdown inertial navigation system; establishing a CKF filtering state equation by taking the speed error as the state variable and a measuring equation by taking the speed error as the measurement quantity under a static base; carrying out filtering estimation by using a CKF filtering method to estimate the misalignment angle of the platform; and obtaining an accurate strapdown initial posture matrix by using a strapdown initial posture matrix of a platform misalignment angle correcting system, thereby finishing the accurate initial alignment. The invention can greatly improve the aligning precision of the strapdown inertial navigation system at the large misalignment angle and provide the accurate strapdown initial posture matrix for the navigation process.

A novel method for adaptive Nonlinear Model Predictive Control (NMPC) of multiple input, multiple output (MIMO) systems, called Sampling Based Model Predictive Control (SBMPC) that has the ability to enforce hard constraints on the system inputs and states. However, unlike other NMPC methods, it does not rely on linearizing the system or gradient based optimization. Instead, it discretizes the input space to the model via pseudo-random sampling and feeds the sampled inputs through the nonlinear plant, hence producing a graph for which an optimal path can be found using an efficient graph search method.

The invention provides a mixed integer nonlinear model optimization method for a region distributed type cooling, heating and power poly-generation system. A distributed energy supply system is designed and optimized according to regional load characteristics, and the guidance is provided for the overall planning of a regional integrated energy system. Through the feedback on the operating state of a model optimized output, the envelop enclosure of an existing building is transformed definitively and exemplarily, so that the load level of a system balance object is reduced. According to the technical scheme of the invention, according to chronological load characteristics, the technology components and the technology parameters of the system are designed, and a production running scene of the poly-generation system is set. Meanwhile, the running state and the running parameters of the CCHP poly-generation system within each hour during the full production period can be accurately simulated and optimized in real time under the consideration of comprehensive benefits. Therefore, a reasonable solution is provided for the design and the optimization of the region distributed type cooling, heating and power poly-generation system, wherein the advantages of economy, environmental protection, energy conservation and the like are simultaneously realized.

The invention discloses a hybrid electric vehicle mode switching coordination control method based on a model prediction control algorithm and relates to the field of vehicle control theories. A fast and systematic model prediction control strategy is applied to the method. On the basis of building hybrid electric vehicle mode switching process linear and nonlinear models, system operation status signals in the vehicle mode switching process are collected, control commands of all components of a system in a control period are determined through model prediction control, real-time output torque conflict of an engine, a motor and a clutch in the mode switching process is coordinately controlled, smooth and steady switching between modes is guaranteed, and the power performance of a vehicle and the riding comfort level of passengers in the mode switching process are improved. According to the control system, a good control effect can be achieved because the future requirement information of the vehicle and the performance of an optimized objective function in a prediction time domain are comprehensively considered.

The invention discloses a flexible spacecraft underactuated system based on the switching control method and an attitude control method of the flexible spacecraft underactuated system based on the switching control method and belongs to the technical field of spacecraft attitude control. According to the flexible spacecraft underactuated system based on the switching control method and the attitude control method of the flexible spacecraft underactuated system based on the switching control method, a dynamical model of a flexible spacecraft with an execution mechanism completely ineffective is established, coupling, caused by elastic vibration of a flexible accessory, with a rigid body is taken as an uncertainty of the system, a dynamical model of a conversion system is a standard nonlinear model, all states are divided into three sliding model surfaces according to the layering sliding model idea, an equivalent control component is designed for the first layer of sliding model surface according to the Filippov equivalent theorem, a switching control law is designed by means of the second layer of sliding model surface and the third layer of sliding model surface, and a final control law is formed by combining equivalent control input and the switching control law. By the adoption of the flexible spacecraft underactuated system based on the switching control method and the attitude control method of the flexible spacecraft underactuated system based on the switching control method, the defect that in the prior art, a flexible spacecraft can not operate normally under the condition that an actuator becomes ineffective completely during operating of the flexible spacecraft is overcome, and operating reliability of a flexible spacecraft attitude control system is improved.

The invention discloses a star sensor star point image enhancement method, which is a superposition enhancement method after reconstruction of a star point image. Through motion information acquired by the star sensor and a star point extraction and recognition result of the current frame, a nonlinear model for star point motion is adopted to realize star point position prediction and tracking andrecognition in a next frame, and position prediction results of star points at different positions in an image plane are more accurate; by using the predicted star point position and the star point information in the former frame, a star point motion energy distribution model is used to generate one frame of noise-free simulated star map, the energy distribution is closer to the actual star points, no complex matching alignment process is needed, better star point superposition enhancement effects can be obtained, and the star point extraction precision is improved. The method is suitable when the star sensor is in a static condition and a dynamic condition, star point position prediction and tracking and recognition can also be realized, the star sensor processing time is further reduced, and the star sensor attitude output errors are reduced.

A method for reconstructing gusts and / or structural loads at aircraft, in particular passenger aircraft. The method includes generating an observer on the basis of a nonlinear model of the aircraft which describes the movement of the aircraft in all six degrees of freedom (DoF) and the elastic motion of the aircraft structure; continuously supplying all the data and measurements substantial for the description of the state of the aircraft to the observer; and calculating the gust velocities and structural loads (manoeuvre and gust loads) by the observer from the supplied data and measurements.

The invention provides a large-size wind turbine feedback linearization power control method based on a radial basis function (RBF) neural network, and relates to the field of wind turbine power control. The control method comprises the steps that 1, on the basis that a wind turbine state space is built, an affine nonlinear model of a variation paddle controller is put forward, and the feedback linearization control law is designed; 2, an affine nonlinear model of a torque controller is built, and the feedback control law is designed; 3, on the basis of analyzing blade force vibration and tower vibration, the threshold value definition of a dual-loop controller is put forward for serving as the basis of controller transformation; and 4, the RBF neural network is designed to serve as a compensation control method for controlling errors. According to the dual-loop controller based on feedback linearization provided by the invention, the constant power control problems that the wind speed change is large, small, and fast after the rated wind speed is exceeded can be solved, the control precision is also improved through error compensation, and the robust performance of the system is improved.

The invention discloses a higher-order non-singularity Terminal sliding-mode control method of a high supersonic speed aircraft. A nonlinear model of the high supersonic speed aircraft is processed ina feedback linear method, and modeling errors and outside disturbance of a system are compensated via an RBF neural network control strategy. On the basis of the linearized vertical model, a novel neural network sliding-mode controller is in a nominal cruse flight condition based on a sliding mode surface of a regression structure, and an instruction signal and elevator deflection signal set by an engine throttle valve of the high supersonic speed aircraft are controlled to control the speed and height of the aircraft. The controller is highly robust for uncertainty of aerodynamic nonlinearity, pneumatic interference and system parameters. According to a simulation result, the instruction signal can be tracked effectively, and the response speed is higher.

The invention discloses an aero-engine gas path component health diagnosis method based on particle filtering. The aero-engine gas path component health diagnosis method includes the steps that a nonlinear mathematical model of an engine is established; a particle filtering algorithm is designed based on significance weight value adjustment of a neural network; finally, a gas path component health diagnosis is achieved based on the nonlinear model of the engine by the adoption of the designed algorithm. The nonlinear mode is that on the basis of a physical equation reflecting the aerothermodynamics performance of the engine, a shared working equation set among the components is established, and by the adoption of a Newton Laphson interactive algorithm, the nonlinear equation set is solved to obtain working parameters of the cross section of the engine; the particle filtering algorithm based on the significance weight value adjustment of the neural network is that a BP neural network algorithm and a typical sampling algorithm are combined, on the basis of a standard particle filtering algorithm, two steps of weight value splitting and particle adjustment are added, and therefore the phenomena of particle degradation and sample depletion are effectively avoided. The health diagnosis of gradual performance degradation and sudden faults of the gas path components within the service life of the engine can be achieved.

The invention provides a nonlinear model-based multispectral remote sensing water depth inversion method and an apparatus thereof. The method comprises the following steps: acquiring the multispectral remote sensing image of a preset area and the actually measured control point water depth of a preset water area, and preprocessing the multispectral remote sensing image to obtain a preset area reflectivity; carrying out water-land separation on the preset area reflectivity through a near infrared waveband spectrum characteristic-based threshold technique to obtain the reflectivity of the water surface of the preset water area; establishing a nonlinear inversion model corresponding to the preset water area according to the water surface reflectivity and the actually measured control point water depth; and regressing the nonlinear inversion model through a stepwise regression algorithm, and inversing according to the regressed nonlinear inversion model to obtain the water depth of the preset water area. The highly-precise water depth of the island reef water area far from the land is rapidly obtained according to the nonlinear inversion model on the basis of the actually measured water depth, model establishing and the solving process are simple, and the nonlinear inversion model is suitable for various types of water depth inversion engineering and has good portability.