138 results about "Constraint control" patented technology

A vehicle crash safety system includes a pre-crash sensing system generating an object threat assessment and vehicle dynamics data, an occupant sensing system generating occupant characteristic data, and an Occupant Safety Reference Model (OSRM) controller for generating a reference safety restraint deployment profile as a function of the object threat assessment, vehicle dynamics data and occupant characteristic data. An active restraint adaptation (ARA) controller in operative communication with the OSRM controller and a decentralized restraint controller. The ARA controller sends restraint deployment targets, and the safety restraint deployment profile to the decentralized restraint controller. The ARA controller may modify input signals to the decentralized controller based on the real-time occupant position trajectory. The decentralized restraint controller is adapted to operate the restraint system as a function of signals from the ARA controller and real-time occupant-restraint system interactions.

The invention relates to a control method, in particular to a control method for an automotive active suspension system. The problem that design models are simple, and single-target control is mainly adopted in the conventional suspension control technologies, so that external uncertain disturbance and unmodeled dynamics cannot be coped with is solved. The control method specifically comprises the following steps of: establishing a nonlinear active suspension model; designing a nonlinear robust controller; and regulating gains k1, k2, epsilon1 and epsilon2 to ensure that all constraints are limited within a permissible range, namely to realize constraint control. The control method is used for the control and the stabilization of the automotive active suspension system.

The invention discloses an aircraft crew scheduling method, which comprises the following steps: (1) acquiring original data meeting requirements of a user from a user interface and a database, and analyzing, converting and arranging the original data to acquire all essential data meeting required forms; (2) carrying constraint control on the essential data according to a scheduling rule, generating all probably scheduled task combinations for each aircraft crew, namely generating a relative task string LoW for each aircraft crew, and evaluating and correcting the task strings LoW; (3) converting all corrected task strings into a task column and establishing a constraint matrix; (4) solving the constraint matrix by utilizing an algorithm of disassembling a large-scale problem formed by integer programming problems into sub problems; and (5) outputting calculation results. The invention further discloses a system for realizing the aircraft crew scheduling method, wherein the system comprises a data input module, a model generation module, a solving module, a control module, a basic module and the like. According to the system for realizing the aircraft crew scheduling method, disclosed by the invention, requirements on equitable distribution of flight number plane, aircraft crew scheduling and flight task are met through cooperation of all modules.

The method comprises: constructing a token bucket that includes depth parameter and bandwidth parameter; the depth of the token bucket is sum of the fixing token bucket value and the dynamical token bucket depth value; in term of flow rate allocating to users, the relevant token numbers are added into the token bucket, and meanwhile in term of actual flow rate of users, the token numbers is subtracted from the token bucket; before subtracting the token numbers, it is made illegal that the token numbers is small than the depth value of fixing token bucket; when the token is added at the next time, firstly the depth value of fixing token bucket is filled up, and then the flow rate of users is allowed to pass through; it also can be implemented by adding tokens based on flow rate; the depth value of dynamical token bucket varies along with maximum bandwidth; the depth value of fixing token bucket can be a token numbers corresponding to a length of packet with maximum data length.

The invention provides an arch bridge cantilever assembling construction optimization calculation method. An actual line shape and a target line shape in each construction lifting stage are strictly controlled, so that the preraised height value of each control node is changed mildly, the cable force uniformity is good, and the stress concentration phenomenon of a material in a construction process does not occur. An arch bridge cantilever assembling construction optimization model has the advantage of relatively few constraint conditions; the constraint condition that a deviation between theline shape after cable tightening and release, and the target line shape is in an allowed range mainly achieves a constraint control effect; and the problems of large buckling cable force gradient, poor uniformity and the like during adoption of a conventional method for performing construction optimization calculation are solved.

The invention is related to a computer program product encoding a computer program of instructions for executing a computer process for interference control, the process comprising: setting a first power constraint for multi-antenna transmissions; setting a second power constraint for single-stream transmissions, wherein the first power constraint and the second power constraint control the use of predetermined resources in a way that the resources are reserved for the single-stream transmissions or the resources are allowed for the multi-antenna transmissions and for the single-stream transmissions, the power level of the multi-antenna transmissions being lower than the power level of the single-antenna transmissions.

The invention provides a model-free adaptive control method based on control input saturation. Under the condition of considering control input has position and rate saturation, the invention designs an adaptive neural network constraint controller based on observer technology; and in the design process, a dynamic anti-saturation compensator is provided for adjusting reference preset value in real time to ensure control input does not enter a saturation region. The method is characterized by, to begin with, carrying out model transformation on a common affine nonlinear system through a feedback linearization method; and then, designing a neural network observer and a constraint controller for the transformed system, and providing a dynamic anti-saturation algorithm to adjust a reference set value online to enable the input of the controller to be always within a constraint range.

A vehicle restraint system has a vision-based occupant classification system for control of airbag deployment during a crash scenario. The classification system utilizes two imaging sensors which together create a stream of paired images received and stored by an occupant classification controller. A computer program product of the controller utilizes the paired images to extract disparity / range features and stereo-vision differential edge density features. Moreover, the controller extracts wavelet features from one of the two paired images. All three features or maps are classified amongst preferably seven classifications by algorithms of the computer program product producing class confidence data fed to a sensor fusion engine of the controller for processing and output of an airbag control signal input into a restraint controller of the vehicle restraint system.

A method, system, and computer readable medium for producing orthoimages of the images of a (typically) high-resolution imager of an urban area, the orthoimages of high buildings being imaged with improved accuracy by increasing constraint conditions on the building edges, such as perpendicularity, collinearity. These constraint conditions are merged into an orthorectification model for orthorectifying the images captured at a predetermined elevation, whether by airborne or spaceborne. Constraint conditions may be formed by the building edge points, such as corners, and may be used with a digital building model. Thus, an aspect of embodiments is that the constraint conditions may be directly formed from the building themselves. The higher the buildings, the more effective the constraint controls.

Radial compression mechanism includes a housing defining an inner chamber and a central opening for insertion and removal of product. A plurality of elongated compression dies are movably mounted for reciprocal movement within the inner chamber and define a central product receiving cavity coaxial with the axis of the central opening. Cam followers are affixed to the dies. First cam surfaces are affixed relative to the housing and second cam surfaces are movably mounted relative to the housing. Each cam follower engages a first cam surface to define a first position control constraint, and a second cam surface to define a second position control constraint. Each die has a position relative to each adjacent die and the coaxial central cavity that is controlled by the first position control constraint and the second position control constraint.

A buckle switch arrangement for a multi-point seat belt system. A restraints control module is provided to receive signals from sensors operatively associated with the vehicle seat which determine and identify seat occupancy and also to receive signals from other sensors operatively associated with the vehicle seat which sense seat belt webbing payout. The signals obtained by the restraints control module are interpreted and, based on the interpretation, a telltale device may be activated to warn the vehicle operator that a seat occupant is not in compliance with seat belt usage.

Control of access to video content is achieved by filtering information identifying the available video content according to one or more applicable profiles and then delivering to an authorized user only the filtered results for the information in a form such as an electronic program guide. In this way the filtered results in the exemplary electronic program guide represent only the content items that meet the profile criteria. Content information, which fails to meet the filtering criteria, can be blocked or scrambled or deleted from the exemplary electronic program guide as a further constraint prior to delivery of the filtered set of information to the authorized user.

The invention discloses a composite current constraint control method of a direct-current buck converter. The method comprises: a state space averaging model of a direct-current buck converter nominal system is established by using a capacitor voltage and an inductor current of the direct-current buck converter as state quantities; a reference current constraint controller is established; according to parameter perturbation of the direct-current buck converter system, input voltage fluctuation and load sudden-change disturbance, a disturbed-state averaging model of the direct-current buck converter is established; a generalized proportional-integral observer is constructed and a time-varying disturbance estimation value is obtained; the reference current constraint controller is introduced to carry out compensation on time-varying disturbance to obtain a composite current constraint controller; and calculation is carried out to obtain a controlled quantity, the controlled quantity is outputted by an output module to obtain a PWM drive signal, and a switching tube of the buck converter is controlled, thereby realizing voltage control of the buck converter. Therefore, requirements of high rapidness and accuracy of output voltage tracking and the anti-disturbance performance of the system can be met while the current constraint function is met.

Controlling flow of gas in a gas pipeline network, wherein flow of gas within each pipeline segment is associated with a direction (positive or negative). Minimum and maximum delivery rates to each gas receipt facility are determined. Lower and upper flow bounds of gas delivery rate are created by bounding minimum and maximum signed flow rates using minimum and maximum delivery rates, respectively, for each pipe segment. A pressure drop relationship for each pipeline segment within the lower and upper flow bounds is linearized to create a linear pressure drop model for each pipeline segment. A network flow solution is calculated, which includes flow rates for each pipeline segment and pressures for each network nodes to satisfy the lower and upper flow bounds on the gas delivery rate. The network flow solution is associated with control element setpoints used by a controller to control one or more control elements.

A construction method of a test case constraint control technology based on epigenetics includes steps of: 1: defining a fitness function, a genetic coding method, and a constraint control rule; 2: initializing parameters: setting a population size, an evolution number, and a termination fitness function value; 3: initializing a population: randomly generating an initial population; 4: performing evolution termination judgment; 5. performing constraint regulation based on the epigenetics: performing constraint methylation and constraint acetylation according to the constraint control rule; 6: selecting individuals; 7: completing epigenetic evolution of the population; and 8: outputting a test case set; wherein through the above steps, construction of an epigenetic test case constraint control technology is completed, so as to design test cases that are more in line with the actual operation conditions of the software, find more potential software failures, and improve the quality of software testing.

A computer implemented method of conducting closed-loop control of a physical system comprising the steps of carrying out an initialization of the physical system to commencing closed-loop control, evaluating the optimal constrained control move using the system error and the initial normalized matrix using a control move solver; calculating a first control action by the sum of delta u(0) and the initial control action; and implementing the result to the physical system by converting the control action to an output control signal to effect a change in at least one operating variable.

The invention relates to a method for calculating the static strength of a cabin door structure and belongs to the field of airplane structures strength design. Static strength calculation is performed based on a linear contact analysis method, and a finite element model of the cabin door structure is firstly established; according to the cooperative relation of the cabin door structure and an airplane body structure, boundary conditions are determined; then, the loading conditions applicable to constraint conditions of the cabin door structure are determined according to the boundary conditions, finally constraint of a linear contact analysis model is performed, a constraint control file is established, and finally the result of linear contact constraint reaction is calculated. By comparing a calculation result of linear contact analysis with a calculation result of nonlinear contact analysis, the static strength of the cabin door structure can be more conveniently, quickly and accurately calculated through linear contact analysis.

The invention provides cloud computing big data privacy protection method. The method comprises the following steps: deploying a plurality of authentication servers in a cloud storage platform, and performing graded key distribution and feature authentication affair; and maintaining a global user feature list, and setting an authorization reading strategy and a constraint control strategy by the authentication servers. By adoption of the cloud computing big data privacy protection method, the secure strategy reading control of the environment and the strategy constraint is enhanced, the computing cost of a writing user is reduced on the premise of ensuring the security, and the application demands of cross-cloud and cross-grade data strategies of all types are satisfied.

The invention provides a plan execution control apparatus, a plan execution control method and a plan execution control program capable of giving instructions for sensitively and efficiently maintaining even installations located in a wide range. The plan execution control apparatus includes a linkage reliability calculating section for calculating a target reliability of a single or a plurality of mutually related installations, a damaged state transition control section that provides a damaged state transition structure of the installations, a maintenance time limit calculating section for calculating a time limit related to the maintenance work to meet the target level of reliability of each key point and section, the constraint control section for recognizing and recording the constraints related to traveling of maintenance personnel, the traveling workload and the acceptable workload, and the itinerary maintenance determining section for calculating the itinerary schedule indicating the assignment to the object to be maintained.

The invention discloses a neural network optimizing controlling equipment and method. The invention includes controller neural network which is used to calculate optimizing control signal serial according to complicated process object output signal serial; training structure equipment which is used to transform optimizing control rule integrated problem to combining network training problem, composed by controller neural network and complicated process object emulator neural network; training arithmetic equipment, which deducts jointing equal error calculating formula among different controller neural networks and complicated process object emulator neural networks in the combining network, and is used to train the combining network to get optimized controller neural network. The control method of the invention sets the consistency function of training network output and perfect output as guideline function of optimizing control, the invention has the excellence of achieving multiobjective control to complicated process objects which have no precise mathematical model, under restricted controlling condition, with intelligent method.

The invention provides a model prediction control system for an electronic throttle valve based on FPGA, and belongs to the field of automotive electronic technology. The purpose of the invention is to provide a model prediction control system for an electronic throttle valve based on FPGA, wherein the model prediction control system can well meet control demands of a throttle valve, can be flexibly to reduce, expand and upgrade on line, and overcome shortages of an electronic control system of a present electronic throttle valve. The method of the system comprises designing a model prediction controller and implementing a control system through FPGA. The invention designs an advanced model prediction controller of a throttle valve, constraint optimization is successful, multi-target constraint control can be achieved because the throttle valve can quickly and accurately track, a good control effect is achieved. The model prediction control system for the electronic throttle valve based on the FPGA can be conveniently to upgrade, expand and maintain, the service life of a product can be prolonged, and costs of development and maintenance are reduced. Compared with a control system based on a single chip microcomputer or the like, the control system based on the FPGA is strong in anti-interference, productization is easier, and design on a special control chip can be achieved.

A neural network prediction control method for a fuel cell oxygen surplus coefficient belongs to the technical field of control. In order to solve the problem of oxygen surplus coefficient control ofan automobile fuel cell, a neural network prediction control algorithm is used for designing a controller, so that the optimal power of a fuel cell system is guaranteed while sufficient oxygen is obtained. The method comprises the steps of software selection, training sample design, off-line learning of a neural network prediction model, learning of the neural network prediction model and design of a neural network prediction controller. According to the method, system features are extracted directly by learning input and output data, and complex mechanism modeling is effectively avoided. Theloss of system characteristics in an online learning process can be reduced, and the precision of multi-step prediction is improved, so that the problem of constraint control of a nonlinear system canbe effectively solved.

The invention proposes a seeding leakage detection-reseeding system and a potato seeding leakage detection-reseeding integrated control system and method. The system is independent of an original seeding mode that a return pulley drives a seeder to carry out seeding, and employs an independent seed arrangement DC motor to drive a spoon chain to operate. The seed arrangement DC motor is connected with an intelligent controller, and the speed of the return pulley is detected in real time through a coding speed sensor. The intelligent controller carries out the precise detection of a seeding leakage position through receiving the signals of photoelectric, piezoelectric and Hall sensors which are installed on side surfaces of a seed spoon, and carries out the precise judgment of the reseedingmoment in a counting mode. During reseeding, the system employs a mode of crossing a spoon without seeds to employ a next spoon with seeds for reseeding through acceleration control, so the mode is novel, and the system is high in precision of seeding leakage detection and reseeding. Moreover, the seed arrangement DC motor is used for independently controlling the seed spoon to rotate, thereby achieving the variable-speed adjustable seed arrangement and reseeding control of the independent constraint control of potato seeding, and achieving the integrated control of the seeding leakage detection and reseeding.

A method and apparatus for multiple variable control of a physical plant with high dimension multiple constraints, includes: mathematically decoupling primary controlled outputs of a controlled physical plant from one another and shaping the pseudo inputs / controlled outputs desired plant dynamics; tracking primary control references and providing pseudo inputs generated by desired primary output tracking for selection; mathematically decoupling constraints from one another; mathematically decoupling constraints from non-traded off primary controlled outputs of the controlled physical plant; shaping the pseudo inputs / constraint outputs desired plant dynamics; tracking constraint control limits; providing pseudo inputs generated by desired constraint output tracking for selection; selecting the most limiting constraints and providing the smooth pseudo inputs for the decoupled primary control; and controlling the physical plant using the decoupled non-traded off primary controlled outputs and the decoupled selected most limiting constraints.

The invention discloses an air conditioner fault auxiliary rapid diagnosis control method. The air conditioner fault auxiliary rapid diagnosis control method comprises the following steps that air conditioner constraint coding is conducted, wherein various constraints during running of an air conditioner are coded; constraint type displaying is conducted, wherein during running of the air conditioner, when the running frequency of a compressor of the air conditioner becomes abnormal due to the constraints, the constraint causing the abnormal change of the frequency is found, and a corresponding displaying device is controlled to conduct displaying according to coding of the constraint. According to the air conditioner fault auxiliary rapid diagnosis control method, current common constraint control conditions of the air conditioner are utilized fully, due to the fact that necessary relation exists between constraint producing and faults in some links of the air conditioner, before the air conditioner breaks down, in other words, the frequency of the compressor changes due to the constraint condition, the type of the constraint can be displayed in a coding mode, and technicians can check the type of the constraint conveniently and can quickly position the fault.

An aspect of the present invention provides a passenger protection device that includes, a brake pedal sensor configured to detect the amount by which a brake pedal of a vehicle is operated, a webbing, one end of the webbing fixed to the vehicle, configured to restrain a passenger seated on a seat of the vehicle, a retractor connected to the other end of the webbing, the rector configured to retract the webbing and inhibit of the webbing extraction, and an ECU electrically coupled to the brake pedal sensor, the ECU configured to detect that the brake pedal operation amount detected by the pedal sensor exceeds a first threshold value, the ECU configured to revise the threshold value based on safety related information of the vehicle to control the inhibition of the webbing extraction of the retractor.

The invention discloses an automatic parking control system based on self-adaptive neural network constraint control, and belongs to the intelligent vehicle technology field. The automatic parking control system comprises path planning based on an ultrasonic sensor, a PID controller based on an RBF neural network, dynamic constraint restriction, a complete vehicle dynamical model, and an observer and an anti-saturation compensator based on the RBF neural network. By adopting the above mentioned structure and considering an input constraint restriction condition, the whole system has a complete control idea. According to an actual condition, and after being verfied by simulation examples, the automatic parking control system has advantages of good tracking performance, robustness, and adaptability.

A power generation system, apparatus and method to buffer power fluctuations are provided. At least one inverter (14) may be coupled to a photovoltaic power generator (10). The inverter may be subject to at least one operational constraint. A power-buffering circuitry (16) may be connected between the photovoltaic power generator and the inverter to buffer power generation fluctuations which can occur in power generated by the photovoltaic power generator, and satisfy the operational constraint of the inverter notwithstanding an occurrence of the power generation fluctuations. A controller (14) may be coupled to the power-buffering circuitry and may be responsive to the parameter of the photovoltaic power generator to perform at least one control action regarding the power fluctuations. Control actions may be performed by the controller independently of a control strategy of the inverter.