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1794 results about "State-space representation" patented technology

In control engineering, a state-space representation is a mathematical model of a physical system as a set of input, output and state variables related by first-order differential equations or difference equations. State variables are variables whose values evolve through time in a way that depends on the values they have at any given time and also depends on the externally imposed values of input variables.

System and methodology and adaptive, linear model predictive control based on rigorous, nonlinear process model

A methodology for process modeling and control and the software system implementation of this methodology, which includes a rigorous, nonlinear process simulation model, the generation of appropriate linear models derived from the rigorous model, and an adaptive, linear model predictive controller (MPC) that utilizes the derived linear models. A state space, multivariable, model predictive controller (MPC) is the preferred choice for the MPC since the nonlinear simulation model is analytically translated into a set of linear state equations and thus simplifies the translation of the linearized simulation equations to the modeling format required by the controller. Various other MPC modeling forms such as transfer functions, impulse response coefficients, and step response coefficients may also be used. The methodology is very general in that any model predictive controller using one of the above modeling forms can be used as the controller. The methodology also includes various modules that improve reliability and performance. For example, there is a data pretreatment module used to pre-process the plant measurements for gross error detection. A data reconciliation and parameter estimation module is then used to correct for instrumentation errors and to adjust model parameters based on current operating conditions. The full-order state space model can be reduced by the order reduction module to obtain fewer states for the controller model. Automated MPC tuning is also provided to improve control performance.
Owner:ABB AUTOMATION INC

System and method of collision avoidance using intelligent navigation

A system and method of intelligent navigation with collision avoidance for a vehicle is provided. The system includes a global positioning system and a vehicle navigation means in communication with the global positioning system. The system also includes a centrally located processor in communication with the navigation means, and an information database associated with the controller, for identifying a location of a first vehicle and a second vehicle. The system further includes an alert means for transmitting an alert message to the vehicle operator regarding a collision with a second vehicle. The method includes the steps of determining a geographic location of a first vehicle and a second vehicle within an environment using the global positioning system on the first vehicle and the global positioning system on the second vehicle, and modeling a collision avoidance domain of the environment of the first vehicle as a discrete state space Markov Decision Process. The methodology scales down the model of the collision avoidance domain, and determines an optimal value function and control policy that solves the scaled down collision avoidance domain. The methodology extracts a basis function from the optimal value function, scales up the extracted basis function to represent the unscaled domain, and determines an approximate solution to the control policy by solving the rescaled domain using the scaled up basis function. The methodology further uses the solution to determine if the second vehicle may collide with the first vehicle and transmits a message to the user notification device.
Owner:TOYOTA MOTOR CO LTD

Cloud computing system reliability modeling method considering common cause fault

The invention discloses a cloud computing system reliability modeling method considering a common cause fault, and belongs to the technical field of network reliability. The method comprises the steps of determining a state combination of a similar single server of a cloud computing system and performing simplification; calculating an existence probability of the simplified state combination of the similar single server by adopting a fault tree method; determining state combinations of similar servers of the cloud computing system, performing simplification, and calculating an existence probability of each state combination; enumerating state combinations of different servers of the cloud computing system, and calculating an existence probability of each state combination; and according to the state space of the cloud computing system, calculating the system reliability according to a given demand. According to the method, a common cause fault of all virtual machines running in the servers, caused by server faults, is considered, the state space modeling is adopted, and the state space is simplified, so that the problem of state space explosion during system scale increment is solved and the modeling efficiency is improved.
Owner:BEIHANG UNIV

Heterogeneous multi-agent collaborative decision-making method based on depth deterministic policy gradient

InactiveCN108600379AAchieve collaborative decision-makingData switching networksState spaceComputer science
The invention relates to a heterogeneous multi-agent collaborative decision-making method based on a depth deterministic policy gradient, belonging to the collaborative decision-making field of a heterogeneous intelligent unmanned system, comprising the following steps of: firstly, defining heterogeneous multi-agent characteristic attributes and reward and punishment rules, defining multi-agent state space and action space, and constructing multi-agent motion environment for collaboratively making decision; then, establishing an actor module for decision-making action and a critic module for evaluating feedback based on the depth-deterministic strategy gradient algorithm, and training the parameters of the learning model; using the trained model to obtain the multi-agent state sequence; and evaluating the situation of the multi-agent motion state sequence according to the reward and punishment rules set in the environment. The invention may construct reasonable sports environment according to actual needs, achieve the purpose of intelligent sensing and strategy optimization through the synergy between multiple agents in the system, and has a positive effect on the development of the unmanned system field in China.
Owner:INST OF SOFTWARE - CHINESE ACAD OF SCI

Heterogeneous cloud wireless access network resource allocation method based on deep reinforcement learning

The invention relates to a heterogeneous cloud wireless access network resource allocation method based on deep reinforcement learning, and belongs to the technical field of mobile communication. Themethod comprises the following steps: 1) taking queue stability as a constraint, combining congestion control, user association, subcarrier allocation and power allocation, and establishing a random optimization model for maximizing the total throughput of the network; 2) considering the complexity of the scheduling problem, the state space and the action space of the system are high-dimensional,and the DRL algorithm uses a neural network as a nonlinear approximation function to efficiently solve the problem of dimensionality disasters; and 3) aiming at the complexity and the dynamic variability of the wireless network environment, introducing a transfer learning algorithm, and utilizing the small sample learning characteristics of transfer learning to enable the DRL algorithm to obtain an optimal resource allocation strategy under the condition of a small number of samples. According to the method, the total throughput of the whole network can be maximized, and meanwhile, the requirement of service queue stability is met. And the method has a very high application value in a mobile communication system.
Owner:CHONGQING UNIV OF POSTS & TELECOMM

NEXT-GENERATION BANDWIDTH MANAGEMENT CONTROL SYSTEMS FOR MULTIPLE-SERVICE CALLS, SESSIONS, PACKET-LEVEL PROCESSES, AND QoS PARAMETERS - PART 1: STRUCTURAL AND FUNCTIONAL ARCHITECTURES

System and method for addressing immense, long-standing problem of bandwidth management, for example, in enterprise networks, VPNs, real-time and stored video services, mobile applications, wireless networks, and cloud computing applications. Described features include an automatic closed-loop control system infrastructure encompassing multiple time-scales and performing control actions optimized to the extent possible with respect to administrator-provided performance metrics. One aspect utilizes available or innovatively accessible means of session and QoS control (settings in configuration files, gateway APIs, QoS parameters, application bit-rate settings, etc.) within the context of practical multiple-vendor products in evolving multiple-service networks. Another aspect utilizes available or innovatively accessible means of session and QoS observations (values in reporting log files, gateway APIs, network monitoring, etc.) within the context of practical multiple-vendor products in evolving multiple-service networks. Traffic-measurement controlled adaptive reservations for distributed myopic single-service gatekeepers effectively shapes the permitted state-space boundary over a range of arbitrary curvatures.
Owner:AVISTAR COMM

Reinforcement learning based air combat maneuver decision making method of unmanned aerial vehicle (UAV)

InactiveCN108319286AEnhance autonomous air combat capabilityAvoid tedious and error-proneAttitude controlPosition/course control in three dimensionsJet aeroplaneFuzzy rule
The invention provides a reinforcement learning based air combat maneuver decision making method of a UAV. A motion model of an airplane platform is created; principle factors that influence the air combat situation are analyzed; on the basis of the motion model and analysis on the air combat situation factors, a dynamic fuzzy Q learning model of air combat maneuver decision making is designed, and essential factors and an algorithm flow of reinforcement learning are determined; a state space of air combat maneuver decision making is fuzzified and serves as state input of reinforcement learning; typical air combat motions are selected as basic motions of reinforcement learning, and the triggering intensities of fuzzy rules are summed in a weighted manner, and a continuous motion space is covered; and on the basis of an established air combat dominant function, a return value of reinforcement learning is set in a rewards and punishment values weighing-superposing method. Thus, the autonomous maneuver decision making capability of the UAV during air combat can be improved effectively, the robustness is higher, an autonomous searching optimization performance is higher, and decisionsmade by the UAV are improved continuously in continuous simulation and learning.
Owner:NORTHWESTERN POLYTECHNICAL UNIV

Galloping positioning system and positioning method of transmission conductors based on micro-inertial measurement combination

ActiveCN102279084AHigh precisionMeet the needs of long-term operation in the fieldVibration testingGyroscopeMathematical model
The invention discloses a transmission line oscillation positioning system based on micro inertial measurement combination. The transmission line oscillation positioning system comprises a monitoring center and a pole and tower monitoring host machine which are connected with each other, wherein the pole and tower monitoring host machine is wirelessly connected with at least two wireless inertialsensor nodes; and each wireless inertial sensor node comprises a triaxial acceleration sensor and a triaxial gyroscope. By the method for positioning by adopting the system, the wireless inertial sensor nodes acquire acceleration values and state space angles of a lead wire monitoring point in three directions; the monitoring center processes and analyzes the data of each monitoring point by adopting algorithms, such as Fourier transformation, a least square method, digital filtering, Kalman filtering, matrix coordinate transformation, frequency domain integral operation so as to fit to acquire an oscillation trace of a whole line, and corrects the oscillation line according to a digital model of a relation between an oscillation characteristic value and a micro meteorological condition; therefore, the precision of oscillation monitoring is improved and the most direct and intuitive monitoring of transmission line oscillation can be realized.
Owner:西安金源电气股份有限公司

SOC (state of charge) estimation method

The present invention discloses an SOC (state of charge) estimation method. According to the method, a battery OCV-SOC relationship module, a parameter acquisition module, an offline identification parameter value, a parameter discrete state space model, a battery parameter online identification module, a battery dynamic parameter update module and a battery SOC estimation module. The method includes the following specific steps that: 1, a discharge-standing experiment is performed, an OCV-SOC relationship expression is obtained through fitting, parameter values in an equivalent circuit model are identified; 2, a battery second-order RC system discrete state space model is established, and the battery model parameters are identified online and dynamically updated; and 3, the SOC of a battery is estimated online. With the method of the invention adopted, the defects of inaccuracy and accumulative error of the initial value of the SOC of a battery of in an ampere-hour integration method can be eliminated. The method is applicable to the dynamic change of the characteristics of the battery, can improve the accuracy of SOC online estimation and can be widely applied to the electric vehicles and storage battery management system field. The method has the advantages of high battery model precision, fast convergence, high stability and high reliability.
Owner:SUNWODA ELECTRIC VEHICLE BATTERY CO LTD

Method and device for estimating power battery charge state on line

The invention discloses a method and device for estimating a power battery charge state on line. The method comprises the steps that a capacity correction factor of a power battery is calculated according to the accumulated released energy of the power battery, the internal resistance of the power battery is calculated, a state space model of the power battery is established, and the state space model of the power battery is substituted into an unscented Kalman filter to obtain the estimated value of the battery charge state. According to the method and device for estimating the power battery charge state on line, the capacity correction factor of the power battery is calculated according to the accumulated released energy of the power battery, the accumulated released energy of the power battery is the sum of energy consumed by load acting and energy consumed by the internal resistance from the first time up to now, the accumulated released energy of the power battery can reflect the using history of the battery like the cycle index and is more beneficial to accurate measuring in numerical value, and therefore the method of correcting the related parameters of the battery through the accumulated released energy of the power battery is better in operability and is more accurate in estimated value.
Owner:SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV +1

Method for predicting dynamic risk and vulnerability under fine dimension

The invention relates to a method for predicting dynamic risk and vulnerability at fine scale and belongs to the scientific field of global information. The method is mainly characterized in that an optimized Bayesian network is searched from multi-source heterogeneous spatiotemporal data on the basis of a grid format with certain resolution at fine scale; domain knowledge is combined to improve the network; therefore, the uncertain estimation of disaster risk and the vulnerability, namely probability estimation, is carried out. In the method, a nuclear density method is put forward to train a sample according to a sample derivative grid; an optimized discretization method is put forward to discretize continuous variables so as to provide discrete state space input for the network; a simulated annealing optimization algorithm is adopted to search an optimized network structure; and a method of accurate reasoning combined with approximate reasoning to predict the probabilities of risk and the vulnerability is adopted. The method provided by the invention can position the positions of the disaster risk and the vulnerability in real time at the fine spatial scale, estimate the spatial distribution of the risk probability and has important theoretical significance and practical value for improving the effects on the reduction and relief of disaster and building an intelligent public emergency pre-warning system by the state.
Owner:INST OF GEOGRAPHICAL SCI & NATURAL RESOURCE RES CAS

Dynamic spectrum access method based on policy planning constrain Q study

The invention provides a dynamic spectrum access method on the basis that the policy planning restricts Q learning, which comprises the following steps: cognitive users can divide the frequency spectrum state space, and select out the reasonable and legal state space; the state space can be ranked and modularized; each ranked module can finish the Q form initialization operation before finishing the Q learning; each module can individually execute the Q learning algorithm; the algorithm can be selected according to the learning rule and actions; the actions finally adopted by the cognitive users can be obtained by making the strategic decisions by comprehensively considering all the learning modules; whether the selected access frequency spectrum is in conflict with the authorized users is determined; if so, the collision probability is worked out; otherwise, the next step is executed; whether an environmental policy planning knowledge base is changed is determined; if so, the environmental policy planning knowledge base is updated, and the learning Q value is adjusted; the above part steps are repeatedly executed till the learning convergence. The method can improve the whole system performance, and overcome the learning blindness of the intelligent body, enhance the learning efficiency, and speed up the convergence speed.
Owner:COMM ENG COLLEGE SCI & ENGINEEIRNG UNIV PLA

Adaptive code rate video transmission method and system based on reinforced learning

The invention discloses an adaptive code rate video transmission method and an adaptive code rate video transmission system based on reinforced learning. The method comprises the steps of inputting astate space corresponding to a video block needing to be downloaded into a code rate prediction neural network, and outputting a code rate strategy by the code rate prediction neural network; downloading the video block needing to be downloaded according to the code rate strategy output by the code rate prediction neural network; after each video block is completely downloaded, computing a video playing quality index corresponding to each video block and returning the video playing quality index back to the code rate prediction neural network; and performing training by the code rate prediction neural network according to the returned video playing quality index and the state space corresponding to the completely downloaded video block. According to the method and the system provided by the invention, the algorithm well adapts to multiple different network conditions while the quality of service is improved, the labor time cost of rule setting and parameter optimization is greatly reduced, the efficiency problem caused by artificial feature selection and rule setting is avoided, and the video quality experience is also greatly improved while the configuration and debug time is saved.
Owner:SHENZHEN NAIFEI TECH CO LTD
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