440 results about "Online optimization" patented technology

An apparatus optimizes a hybrid power system with respect to long-term characteristics of the hybrid power system. The apparatus includes a real-time controller of the hybrid power system and a processor. The processor cooperates with the real-time controller and is structured to input current measurements of information from the hybrid power system and hybrid dynamics information including continuous dynamics and discrete time dynamics that model the hybrid power system. The processor provides online optimization of the hybrid power system based upon the input, and outputs a power flow reference and a number of switch controls to the real-time controller based upon the online optimization. The processor is further structured to provide at least one of: real-time forecasts or real-time prediction of future information operatively associated with the hybrid power system as part of the input, and real-time processing of the online optimization.

The invention discloses an optimized operation control method and system of a distributed energy system. The method include: S1, collecting environmental information and actual operation data of a unit so as to acquire a change rule of cold and hot load of a distributed energy station user with season and moment, and establishing a cold, hot and electric load prediction model; S2, optimizing the cold, hot and electric load prediction model on line by introducing real-time calibration factors and the actual operation data of the unit; S3, on the premise that the energy utilization efficiency is met, establishing a dynamic optimized load distribution model according to the dynamic requirements of the predicated cold, hot and electric load by taking a whole-plant economic benefit optimization as an objective, and outputting dynamic optimized load distribution results; S4, based on the whole-plant economic benefit optimization, establishing an optimal combination model according to the dynamic optimized load distribution results, and outputting a unit operation optimization command. High-precision load prediction information can be acquired, a corresponding optimization command is formed, and online optimization control is performed on the load dynamics and unit operation.

The invention discloses a pure electric vehicle control unit calibration system based on CAN (controller area network) bus, which comprises an upper computer with two-way communication and a lower computer with two-way communication. The upper computer comprises a data storage module, an MAP (macro assembly program) chart optimization module, a parameter calibration module and a CAN bus communication processing module. The lower computer is a pure electric vehicle control unit (VCU) and comprises a CAN bus communication module, a data acquisition module, a calibrated data storage module and a control algorithm module. The invention further provides a pure electric VCU online calibration method based on the CAN bus. By introducing the CAN bus into the design of the electric vehicle control unit calibration system, the invention realizes online optimization of the control parameters of power, response speed, electricity consumption and the like of the pure electric vehicle, sloves the problems of difficulty in modifying the parameters, long development cycle and poor maneuverability in the prior art, and solves the problem that differences of the operating conditions and diversity of the requirements on power performance and life mileage of the vehicles under different uses, requirements to the control parameters of the vehicle control units are different due to the type variety of pure electric vehicles.

The invention relates to a method for optimizing combustion of a boiler. The combustion optimization of the prior boiler mainly depends on debugging stuffs to do experiments, thereby taking time and labor and obtaining limited parameter combinations. The method includes the following steps: collecting working parameters of the boiler and corresponding indexes characterizing the combustion characters of the boiler and building a real-time database; adopting an integrated modeling method supporting a vector machine to carry out modeling under the condition that the real work load is 60 percent smaller than the design load of the boiler and adopting a radial basis function neural network integrated modeling method to carry out modeling under the condition that the real work load is60 percent larger than or equal to the design load of the boiler to build boiler combustion models with different indexes; and utilizing the particle swarm optimization algorithm and combining with the built models to optimize the combustion parameter setting of the boiler according to different combustion indexes or index combinations of the boiler. The invention improves the predictive ability of the integral model, greatly improves the predictive ability of the models, and carries out one-line optimization and off-line optimization.

A system and method for latency assignment in a system having shared resources for performing jobs including computing a new resource price at each resource and sending the new resource price to a task controller in a task path that has at least one job running in the task path. A path price is computed for each task path of the task controller, if there is a critical time specified for the task. New deadlines are determined for the resources in a task path based on the resource price and the path price. The new deadlines are sent to the resources where the at least one job is running to improve system performance.

The invention discloses a model prediction based cut tobacco dryer outlet moisture control method. Aiming at characteristics of complicated state changes of a cut tobacco dryer during working and diversity of production process modes, an intelligent integrated optimizing control system for the cut tobacco dryer based on an intelligent prediction model and an artificial intelligent operating mode is constructed so as to achieve comprehensive optimization and automation during cut tobacco drying. Aiming at different stages and different production process modes in production, a model capable of describing dynamic process characteristics depending on feed quantity and feed moisture is constructed. On the basis of the model, an on-line optimizing control algorithm which is capable of simultaneously or selectively adjusting multiple process variables, adapting to changes of the feed quantity and the feed moisture and overcoming mutual interference among the variables and influences of various uncertainties during cut tobacco drying and has self-adaptive and self-adjustment functions is designed, and strict requirements on outlet cut tobacco moisture in different working conditions can be met.

An on-line optimizer is provided wherein a boiler (720) is optimized by measuring a select plurality of inputs to the boiler (720) and mapping them through a predetermined relationship that defines a single value representing a spacial relationship in the boiler that is a function of the select inputs. This single value is then optimized with the use of a plant optimizer (818) which provides an optimized value. This optimized value is then processed thought the inverse relationship of the single modified value to provide modified inputs to the plant that can be applied to the plant.

The invention provides an online calibration system for an automobile ABS ECU based on a CCP protocol. The system comprises an upper computer and a lower computer which are interconnected with each other. The upper computer comprises a data storage module, a data analysis module, a control parameter optimization module, a control parameter calibration module and an upper computer command processor CCP; the lower computer is the automobile ABS ECU and comprises an algorithm module and a data communication module. The invention also provides an online calibration method for the automobile ABS ECU based on the CCP protocol. In the method, the CCP protocol is introduced into design of the calibration system of the automobile ABS ECU, which realizes online optimization and modification of control parameters of an ABS; and the control parameters are monitored in a real-time manner by a real-time message processing mechanism of the CCP protocol, which overcomes the problems of hard parameter modification, long development period and poor operability in the existing development methods.

An aero-engine online optimization and multivariable control design method based on model predictive control achieves the control and online optimization of multiple variables of an aero-engine undera constraint and according to requirements of thrust and speed. A control system consists of two parts. The first part is a prediction model acquisition layer. Based on the actual working state of each control cycle and the external environmental parameters of the aero-engine, an engine small deviation linear model near different steady-state points is continuously established, and model parameters are provided for a model predictive controller. The second part is a control law decision layer. A closed-loop structure is formed by the model predictive controller and an external output feedback.The model predictive controller, based on an engine model in the current state, a control command and relevant constraint limits, determines the output of the controller at the next moment by solvinga linear optimization problem. The external output feedback introduces the aero-engine actual output into the decision on future controlled quantity of the controller to compensate for the influenceof model mismatch and external disturbances.

The invention discloses a method for generating a double-deck multi-objective locomotive optimized manipulating sequence. The method is characterized by comprising steps of 1. collecting current vehicle parameters of locomotives, route data and historical data of driving of drivers and preprocessing the data; 2. defining all routes as different types of child segments based on the equivalent summing gradient obtained in route data preprocessing; 3. confirming a manipulating method based on the segmentation result in the step 2, and building a base layer multi-objective calculation model comprising a traction calculation model; 4. building an upper-layer multi-objective calculation optimized model based on the output provided by the base-layer multi-objective calculation model; 5. finishing the optimized locomotive manipulating by combining the upper-layer optimized calculation model and the base-layer optimized calculation model. The method greatly shortens running time, and can be used in offline optimization and short-distance online optimization.

The invention discloses a performance prediction method applicable to dynamic scheduling for a semiconductor production line. An extreme learning machine (ELM) is applied to prediction and modeling in the performance prediction method. Feeding control and scheduling rules are considered in a unified manner in the method, short-term scheduling key performance indexes such as an equipment utilization rate and a movement step number are predicted on the basis of a real-time state of a system, and a foundation is provided for dynamic real-time scheduling. A novel feed-forward neural network of the ELM is introduced into the semiconductor manufacturing system, and a prediction model is built by the aid of available data of the production line. As shown by test results, ideal prediction results can be quickly acquired by the method implemented by the aid of the ELM, the method has obvious advantages and an obvious application prospect in the aspects of parameter selection and learning speed as compared with the traditional neural network modeling method, and a new idea is provided for online optimal control.

The invention discloses an autonomous obstacle crossing programming method of a deicing and line inspecting robot for a high-voltage transmission line. The method comprises the following steps: step 1, detecting environment information by utilizing a laser radar mounted at the tail end of a mechanical arm, so as to obtain a robot movement ahead obstacle signal; step 2, according to a difference value between the current position and the expected position of the mechanical arm and the obstacle signal in the current condition, programming out a fuzzy programmed angle of the movement ahead mechanical arm by utilizing a fuzzy planner; step 3, performing online optimization to the fuzzy programmed angle by utilizing the particle swarm optimization, so as to obtain a particle swarm fuzzy programmed angle of the movement ahead mechanical arm; step 4, obtaining control moments of all joints by utilizing a neural network self-adaptive controller, and guiding the mechanical arm to act. By adopting the fuzzy programming method, an obstacle crossing programming decision can be made in real time according to the current condition of the deicing and line inspecting robot, and the inaccuracy and hysteretic nature of information perception can be overcome; meanwhile, by adopting the particle swarm optimization, the fuzzy programmed angle can be optimized online, so that the track can be smoother and the redundancy can be smaller.

The invention discloses an adaptive cruise control method based on approximate policy iteration. The adaptive cruise control method comprises the steps of (1), collecting samples; (2), learning on the samples by using an approximate policy iteration algorithm to obtain an approximately optimal policy; (3), optimizing PI controller parameters online in the cruise control, namely, optimizing the PI controller parameters online in a data driving mode by using the approximately optimal policy, so that the cruise control can achieve expected performances. The adaptive cruise control method has the advantages that the principle is simple, online optimization can be achieved, control performances can be improved, and the like.

The invention discloses an online learning method for optimizing a signalized intersection queuing length. The online learning method comprises the following steps of: 1, selecting states, behaviors and rewards; 2, reinforcing a learning matrix updating formula; 3, establishing a simulation optimizing platform; and 4, carrying out online operation. The online learning method is a signal timing dial optimizing technology which is capable of calculating a globally optimal solution and has the memorability. Compared with the risk neutral reinforcing learning technology, the online learning method has the advantages of no need of advanced offline learning, and better instantaneity and adaptability.

The invention discloses an ATO (Automatic Train Operation) speed command optimization method of an urban rail transit train. The method comprises the following steps: establishing a data module for optimizing an ATO speed command; establishing an ATO speed command combination evaluation module to evaluate superiority-inferiority of a current ATO speed command combination; establishing a multi-objective genetic algorithm NSGA-II (Non-dominated Sorting Genetic Algorithm II)-based ATO speed command energy-saving optimization method to determine an energy-saving ATO speed command so as to finally obtain an interval energy-saving ATO speed command set. According to the method, the optimal ATO speed command set of all operation intervals of a metro line can be obtained, the optimization time is greatly shortened, off-line optimization can be performed, on-line optimization can also be performed, the metro traction power consumption is reduced, and a large amount of electric energy can be saved for the urban rail traffic system of China each year.

A deep learning based distributed optical fiber vibration sensing type intelligent safety monitoring method includes: signal demodulation and disturbance positioning of a distributed optical fiber vibration sensing technique; demodulation pattern acquisition; sample library construction and network training for network model generation; online real-time disturbance type recognition with a networkmodel; network model online training optimization and the like. Safety monitoring is realized by adoption of detection lines or zone boundary communication cables, and the method has advantages of high extensibility, convenience in networking, low cost, lightning interference prevention and the like. In addition, the method takes full distributed advantages of distributed optical fiber vibration sensing to realize classification and recognition of disturbance information by the aid of a deep learning network, high intelligent recognition accuracy and online optimization performances are achieved, long-distance and large-range circuit safety alarm information management cost and onsite confirmation cost can be reduced, and engineering application process and development of the field of distributed optical fiber safety monitoring systems are greatly promoted.

A goal programming based hypersonic flight vehicle re-entry trajectory online optimization method comprises the steps of giving a kinetic model of hypersonic flight vehicle re-entry process, including height, latitude, longitude, course angle and trajectory angle kinetic equations; calculating a re-entry corridor within a speed-height plane, setting an attack angle alpha as a segmented linear function and using two speed values V1and V2 as a segmentation points to obtain the attack angle alpha; respectively designing a longitudinal trajectory and a transverse trajectory. The goal programming based hypersonic flight vehicle re-entry trajectory online optimization method is applicable to online trajectory generation of the hypersonic flight vehicle re-entry process, is feasible and effective for solving the problem of the re-entry trajectory optimization under the condition that a terminal point position is known, can generate a feasible trajectory within short time, meets quick and real-time trajectory optimization, can further make the obtained trajectory to meet all constraint conditions within a certain error accuracy range and ensures trajectory feasibility.

The invention relates to an automatic start-up and shut-down optimization control system of a heat-engine plant unit plant. The automatic start-up and shut-down optimization control system is characterized in that: a basic control logic generated by data communication system (DCS) standard control algorithm configuration is operated in a DCS process controller and applied to the automatic control of processing equipment and processing parameters in the start-up and shut-down process of the unit plant, and operated in optimization calculation software of an optimization controller and applied to online optimization calculation of a key processing parameter target value and a target value change rate in the start-up and shut-down process of the unit plant as well as fitting and learning of a multi-target optimization control law; a bidirectional data communication function is formed between the optimization controller and the DCS, so acquisition of DCS data can be finished; and an optimization calculation result is written into a DCS real time database so as to realize online optimization. An automatic start-up and shut-down control system is implemented by combining four functions, namely basic control, optimization calculation, communication interfaces and online optimization, so that the practicability and the applicability of the automatic start-up and shut-down optimization control system are improved greatly.

The invention provides a production-data-driven dynamic job-shop scheduling rule intelligent selection method and belongs to the manufacturing enterprise job shop production planning and scheduling application field. The method mainly comprises the following steps: introducing a Multi-Pass algorithm simulation mechanism, establishing a job-shop production scheduling simulation platform, and generating production planning and scheduling sample data; screening the obtained sample data and generating a scheduling parameter set; designing BP neural network models for scheduling knowledge learningunder different scheduling targets; optimizing training of the BP neural networks through a new firefly algorithm to obtain NFA-BP models; integrating the NFA-BP models under various scheduling targets into an intelligent scheduling module, which is integrated with a job shop MES system to guide on-line scheduling; manually adjusting online production planning and scheduling deviation and updatingthe scheduling parameter set, and the intelligent scheduling module carrying out online optimization learning; and the intelligent scheduling module adapted to real workshop production status outputting optimal scheduling rules according to current job conflict decision points.

A computer-implemented method of optimizing demand-response (DR) of a heating, ventilation, and air-conditioning (HVAC) system of a building, includes determining (30, 31, 32) a value of an objective function Fij of a HVAC system for each of a plurality of DR strategies j for each of a plurality of weather patterns i that is a weighted sum of an energy cost of the HVAC system and a thermal comfort loss of the HVAC system, assigning (33, 34, 35, 36) a likelihood score Li,j to each of a selected subset of near-optimal DR strategies j for each weather pattern i, and selecting (37, 38) those near-optimal DR strategies with large overall likelihood scores Lj to create an optimal strategy pool of DR strategies. An optimal strategy pool can be searched (39) in real-time for an optimal DR strategy for a given weather pattern.

The invention discloses an adaptive PID controller optimization design and setting method based on binary ant colony algorithm. The method can automatically optimize the design of the PID controller structure according to the set performance index in the open-loop and closed-loop states and optimize the corresponding control parameters online. In the present invention, a maximum-minimum binary ant colony optimization algorithm is proposed to realize object system identification, control structure design and parameter optimization. In the specific implementation, parameters such as the encoding length of the binary ant colony algorithm are adaptively set according to the parameter accuracy and range. The proposed method of judging re-initialization based on the maximum and minimum limit probability of pheromone can further improve the global optimization performance of the algorithm and improve the optimal control quality of the controller. The PID controller optimization design and parameter tuning method has universal applicability and flexibility, simple application, and can be widely used in the optimization design tuning of PID controllers in industrial control.

The invention provides a power plant pulverized coal boiler combustion performance online optimizing method and system. The method comprises the steps of receiving basic data of boilers in different load work conditions, which are acquired by a Distributed Control System (DCS) and an instrument, wherein the basic data is used for establishing non-linear mapping relationship between pulverized coal boiler adjustable input variables and output variables through a radial basis function network, and the non-linear mapping relationship is used as a boiler combustion mathematic model; obtaining an optimal input combination and value of boiler combustion system adjustable variables under the corresponding expectation coal consumption and NOX emission level. By the aid of the method and the system, optimal control can be performed on boiler operation engineering, relationships among all operation parameters of the boiler are coordinated, the safety, the economy and the reliability of the system are further improved, and the boiler combustion system comprehensive performance is improved comprehensively.

The invention discloses an energy-heat integrated real-time management system of an intelligent networked hybrid electric vehicle, which belongs to the technical field of energy-saving control of hybrid electric vehicles. The invention aims to provide real-time dynamic traffic preview information by using network connection information. According to the energy-heat integrated real-time managementsystem of the intelligent networked hybrid electric vehicle, the temperature effect of a thermal chain is considered in the energy efficiency optimization problem of the whole vehicle, the multi-dimensional requirements of a driver in the aspects of dynamic property, temperature and the like are considered, and the fuel economy of the whole vehicle is further improved. The method comprises the steps of acquiring real-time traffic information flows on all road sections in combination with traffic flow cloud data; determining a global route, combining queue vehicle speed information on a drivingroute, transmitting vehicle speed prediction results of long and short time scales to a hybrid vehicle power chain-thermal chain dynamic coupling mechanical prediction module, and designing an SOC trajectory real-time optimization controller by utilizing the vehicle speed information of the long and short time scales provided by a multi-scale vehicle speed prediction module. According to the invention, the online optimization solving efficiency is improved, and the real-time performance of the system is ensured.

The invention discloses a video vehicle detection method for adaptive learning. The video vehicle detection method for the adaptive learning treats a video vehicle detection problem as a mode classifying problem, mainly comprises an image feature extracting step, a classifier off-line training step, a classifier on-line optimizing step and a vehicle counting step, and comprises the following specific steps of firstly extracting a plurality of discriminative image features from a monitoring video, wherein the image features can be used for discriminating vehicles and backgrounds and also comprise environment information associated with light and weather conditions; secondly off-line training a mode classifier by utilizing a supervised learning method, and also online optimizing the mode classifier to automatically adjust the structure and the parameter of each component classifier, so that the classifier has the adaptive learning capability and the better classifying effect is obtained in a complex traffic scene; and finally carrying out post-process on a classifying result sequence to further improve the vehicle detecting and counting precision. The video vehicle detection method for the adaptive learning disclosed by the invention has the advantages of reinforcing the traditional virtual coil vehicle detection method, having a remarkable engineering application value and being capable of facilitating the development of the video monitoring field and the intelligent traffic field.

The invention relates to an online optimized scheduling method for workflow groups with deadline constraint in a mixed cloud environment. The method comprises the steps of: preferentially processing a workflow smallest and longest load capacity according to space-time correlations of workflows arriving in real time and a limit characteristic of the private cloud processing capability, increasing the workflow completion rate, and reducing the data transmission cost; based on characteristics of the workflows, dividing tolerance time for the deadlines in an equal-weighted manner according to subtask weights so as to meeting the requirements of deadline constraint and service quality; utilizing a greedy choice strategy to searching for a suitable example lowest in subtask execution cost increment on line, and further reducing the execution cost; and according to the characteristics of the mixed cloud environment, designing an integral mapping scheme from the workflows to the execution examples, and ensuring that the service quality of the workflows are met on line and the execution cost is simultaneously lowered. On the premise that the requirements of the deadline constraint of existing practical workflow groups are met, the online optimized scheduling method is capable of effectively improving the completion rate of the workflow groups, and the execution cost is substantially lowered.

The invention discloses a method of wheel torque distribution for a multi-axle drive electric vehicle based on online optimization of driving energy. The method comprises the following steps that thevehicle parameters are obtained and required torque difference values between a left side vehicle body and a right side vehicle body are obtained, after a total torque difference value is applied to the left side vehicle body or the right side vehicle body separately, whether the required torque of the one-side vehicle body is larger than the maximum torque which all drive motors of the one-side vehicle body can output is judged, according to an objective function and a constraint condition, the initial optimization of data is carried out, and first distribution of drive torque of each wheel is carried out; the slip ratio of each drive wheel is calculated, and a fitting coefficient is obtained by fitting a characteristic curve of an electric drive system; and combined with the fitting coefficient, according to the following optimal objection function, the data optimization is carried out once again, and drive torque of each wheel at the optimal performance of a whole vehicle is obtained.

A system and method for optimizing a series of traces to be executed by a processing core is disclosed. The lines of a trace are sent to an optimizer each time they are sent to a processing core to be executed. Runtime information may be collected on a line of a trace each time that trace is executed by a processing core. The runtime information may be used by the optimizer to better optimize the micro-operations of the lines of the trace. The optimizer optimizes a trace each time the trace is executed to improve the efficiency of future iterations of the trace. Most of the optimizations result in a reduction of the number of μops within the trace. The optimizer may optimize two or more lines at a time in order to find more opportunities to remove μops and shorten the trace. The two lines may be alternately offset so that each line has the maximum allowed number of micro-operations.

Techniques to control flight of an aircraft are disclosed. In various embodiments, a set of inputs associated with a requested set of forces and moments to be applied to the aircraft is received. An optimal mix of actuators and associated actuator parameters to achieve to an extent practical the requested forces and moments is determined, including by minimizing a weighted set of costs that includes costs associated with one or more errors each corresponding to a difference between a requested force or moment and a corresponding force or moment achieved by the computed solution.

A hardware environment for low-overhead profiling (HELP) technology significantly reduces profiling overhead and supports runtime system profiling and optimization. HELP utilizes a specifically designed embedded board. An embedded processor on the HELP board offloads tasks of profiling / optimization activities from the host, which reduces system overhead caused by profiling tools and makes HELP especially suitable for continuous profiling on production systems. By processing the profiling data-in parallel and providing feedback promptly, HELP effectively supports on-line optimizations including intelligent prefetching, cache managements, buffer control, security functions and more.

The invention discloses a method for designing current and speed controller PI parameters of a permanent-magnet synchronous motor by using an online particle swarm optimization algorithm. The method uses an improved particle swarm optimization algorithm, the real-time operation speed is improved, online optimization design of the current and speed controller PI parameters is completed without considering the condition of without speed sensor control or the change for stator winding resistance value, and an optimized object function value can be changed according to dynamic and static operation conditions of the system; and the method has the characteristics of algorithm intelligentization, high control accuracy, good stability, high self-adaptive capacity and the like, full digitalization intelligent control is realized, and the hardware cost of traditional permanent magnet synchronous motor servo control is not increased. The method is applicable for high-precision speed closed-loop control of the permanent-magnet synchronous motor and the work occasions that the load is easily changed and the optimization and modulation for the controller PI parameters are required to be quickly completed.