23 results about "Linear optimization problem" patented technology

The invention discloses a vehicle kinetic parameter identification method based on constrained nonlinear optimization, which comprises the following steps: establishing a vehicle two-degree-of-freedom transverse kinetic model based on a small-slip-angle tire and a road cross slope, acquiring a vehicle historical driving data measurement value, acquiring a to-be-estimated vehicle kinetic parameter, and identifying the to-be-estimated vehicle kinetic parameter according to the to-be-estimated vehicle kinetic parameter. Constructing a constrained nonlinear optimization problem about the yaw velocity, solving to obtain an estimated value sequence of the kinetic parameters of the vehicle, carrying out statistics on the sequence, and taking a statistical median as a final estimated value; on one hand, a vehicle transverse dynamics model including a road cross slope is adopted; on the other hand, the constraint of vehicle parameters is considered, a nonlinear optimization problem is constructed, and the vehicle front axle distance (the longitudinal distance from a front axle to the vehicle mass center), the vehicle rear axle distance (the longitudinal distance from a rear axle to the vehicle mass center), the front wheel cornering stiffness, the rear wheel cornering stiffness and the vehicle total mass are identified at the same time.

The invention discloses a hydrogen energy storage capacity configuration and port comprehensive energy system multi-energy coupling collaborative optimization method, and belongs to the field of energy system control and optimal configuration, and the method comprises the steps: constructing a hydrogen-containing port comprehensive energy optimization system model comprising a hydrogen energy storage capacity configuration model and a port operation cost minimization model; and determining the hydrogen energy dissatisfaction cost of the port operator according to the foreground theoretical model and the potential economic loss evaluation of the hydrogen energy accident risk. According to the hydrogen energy storage capacity configuration, the operation cost function and the hydrogen energy dissatisfaction cost of the port operator, establishing a hydrogen energy storage capacity configuration and port integrated energy system multi-energy coupling collaborative optimization model including minimization of the sum of the port operation cost and the port operator dissatisfaction cost; and converting a nonlinear optimization problem into a linear optimization problem. The method effectively balances the operation cost of the port and the subjective worries of the port operator, thereby reducing the operation cost while maintaining the acceptable level of the satisfaction of the operator.

The present invention discloses a method for online identification of the dynamic parameters of an articulated serial robot, comprising the following steps: S1. For the articulated serial industrial robot to be identified, a robot dynamic model that takes friction into account is established; S2. The dynamic model of the articulated serial industrial robot is linearized; S3. An optimal excitation trajectory is designed for the identification experiment, using a five-term finite Fourier series to obtain the excitation trajectory by solving a constrained nonlinear optimization problem; S4. The serial robot runs the excitation trajectory while simultaneously collecting the position information of each joint of the robot and the driving current of each joint at each moment, and online calculating the parameters to be identified at each moment. The present invention can perform online identification of robot dynamic parameters, improves the efficiency of robot dynamic parameter identification, and can be used in industrial scenarios such as robot mobile handling, polishing and grinding, and heavy-load hydraulics.

Traditional algorithms for solving constrained optimization problems are complicated to implement, difficult to interpret, and require significant computational resources. Disclosed embodiments convert constrained optimization problems into parametric optimization problems, in which at least a subset of the constraints are converted into parametric quadratic penalty (PQP) terms that each depends on a translational parameter. The parametric optimization problem may be used for optimization in a power system (e.g., for optimal power flow, economic dispatch, etc.). When solving the parametric optimization problem, the translational parameters are updated to ensure convergence. The parametric optimization problem can be solved with reduced computational expense, using only a linear equation solver to solve a sequence of primal variables only, thereby reducing computational complexity and expense. In addition, the disclosed embodiments provide a means to incorporate constraints into machine-learning algorithms. The disclosed algorithmic framework also provides interpretability and insights for analysis.

The application belongs to the technical field of power systems, and relates to a carbon emission flow constraint linearization method for low-carbon scheduling of power systems. The method comprises the following steps: establishing a power carbon emission flow model based on power flow direction decomposition to obtain power flow direction constraints and node carbon flow balance constraints for coupling carbon flow calculation; introducing auxiliary state variables and processing the power flow direction constraints by using a large M method; constructing a carbon flow balance equation simplification model based on piecewise linearization and a large M relaxation method; based on the power flow direction constraint and node carbon flow balance constraint linearization method, establishing a low-carbon economic scheduling model of the power system coupled with carbon flow calculation, and converting the model into a linear optimization problem for solving. The application converts the original nonlinear problem into a linear optimization programming problem, improves the solving universality of the low-carbon economic scheduling method of the power system containing carbon flow calculation, can guarantee good carbon accounting accuracy, and significantly improves the model solving efficiency.

This invention discloses a cost- and reliability-aware profit optimization method for a heterogeneous multi-server system. The method includes: establishing a heterogeneous multi-server system model to obtain expected service revenue and multi-server system utilization; establishing a soft error reliability model to formulate the application domain investment selection and profit maximization problem as a constrained nonlinear optimization problem; utilizing an iterative method based on a penalty function to obtain the optimal multi-server system configuration, minimum cost, and maximum profit for any single application domain, and converting the profit and configuration optimization problem of the heterogeneous multi-server system into a 0-1 knapsack problem; and utilizing a dynamic programming-based algorithm and a greedy-based algorithm to solve the optimal application domain investment strategy and the corresponding maximum profit. This method can consider budget constraints and the soft error reliability of service requests while configuring the heterogeneous multi-server system configuration, thereby improving cloud service quality and provider profits.

The present invention discloses a real-time solution and reconfiguration planning method for a rope-pulled parallel robot with human-machine interaction, belonging to the field of robot configuration planning. The method comprises the following steps: 1. constructing a kinematic and dynamic model based on the positional relationship between the rope index point and the moving platform, and establishing an admittance model; 2. using the hyperplane movement method based on the dynamic and admittance models to express the force feasibility condition, and setting the optimization problem objective function according to the force feasibility condition; 3. constraining the rope index point solution problem to be expressed as a nonlinear optimization problem, approximating the nonlinear optimization problem to a linear optimization problem through linear approximation, and solving the approximate optimal solution of the linear optimization problem; and 4. correcting the approximate optimal solution based on an artificial potential field set according to the dynamic characteristics, while ensuring solution speed and ensuring that the obtained rope index point position does not lie at the boundary of the solution space, thereby completing the reconfiguration planning of the robot's real-time solution of the rope index point. This method can ensure real-time solution performance and human-machine interaction stability.

The invention discloses a power distribution network low-carbon high-quality scheduling method, system, equipment and medium considering distributed robust optimization segmented affine, and belongs to the technical field of power distribution network scheduling, and the method comprises the steps: taking the minimization of the carbon emission of the product of the main network carbon potential and the net load power as a target function; a Wasserstein distance is adopted to measure the deviation between historical data and real distribution, and multiple types of operation constraints are considered at the same time; the above distribution robust optimization model is converted and solved by adopting a piecewise affine theory, an original nonlinear optimization problem is converted into a linear optimization problem based on a simplex dominant uncertainty set, and the model solving efficiency and robustness are improved. According to the method, the uncertainty of new energy output and carbon potential is effectively dealt with through Wasserstein distance constraint, and the robustness is high; by means of segmented affine transformation, nonlinear robust optimization is simplified into linear optimization, and the solving speed is remarkably increased.

The application discloses a multi-parameter collaborative optimization method for deterministic polishing of a robot in a confined space, and the method is characterized in that: through a multi-step collaborative optimization framework, a material removal amount prediction model, a polishing track, a robot posture and dynamic error compensation are integrated into a unified optimization process, the nonlinear coupling influence of robot posture adjustment and feed speed fluctuation on the removal amount distribution is reduced, the collaborative problems of removal amount out-of-tolerance, coverage uniformity and interference avoidance caused by multi-parameter coupling in a narrow flow channel are solved, and finally, the high-dimensional nonlinear optimization problem is decomposed through an alternating direction multiplier method, the convergence efficiency is significantly improved, the processing surface can meet the preset tolerance requirement, and the deterministic polishing with high precision in the confined space is realized.

The invention relates to the technical field of power grid planning, in particular to a high-performance parallel automatic differential method, system, equipment and medium for main distribution micro power flow nonlinear optimization, and by adopting the method provided by the invention, modeling is carried out based on a main distribution micro power flow nonlinear optimization problem, and an expression diagram is constructed; considering that a large number of isomorphic constraints exist in the problem, the constructed expression graphs are subjected to grouping management, and an automatic differential calculation function pointer used for calculating each group of instances is generated for a plurality of obtained expression groups. And finally, based on the constructed automatic differential calculation function pointer, introducing an OpenMP parallel framework, and carrying out automatic differential parallel calculation on each expression group. Based on the experimental result of opfbenchmark, the method provided by the invention improves the calculation efficiency of automatic differentiation by 47% in the main distribution micro nonlinear optimization problem.

The application provides a sequential linearization solving method and device for network-constructed energy storage optimal control problems, comprising: establishing a system frequency dynamic characteristic representation model and an energy storage dynamic characteristic representation model; constructing a model prediction optimal control framework for network-constructed energy storage, considering the coupling characteristics of system frequency dynamics and energy storage dynamics, and optimizing the optimal control strategy under the minimum cost of network-constructed energy storage; analyzing the linearization constraint conditions of the model prediction optimal control framework, and realizing the linearization representation of the system frequency dynamics and the energy storage dynamics; analyzing the linearization objective function of the model prediction optimal control framework, and realizing the linearization mapping between the optimization objective and the system state and the energy storage state; constructing a sequential linearization solving framework, converting the nonlinear optimization problem of the optimal control framework into a group of iterative and progressive linear optimization problems, and realizing the fast and stable convergence of the algorithm through an acceptance rejection mechanism and a credible domain updating mechanism.

The invention belongs to the technical field of power systems, and relates to a power system low-carbon dispatching-oriented carbon emission flow constraint set linearization method. The method comprises the following steps: establishing an electric power carbon emission flow model based on tidal current direction decomposition to obtain tidal current direction constraint and node carbon flow balance constraint for coupling carbon flow calculation; introducing an auxiliary state variable, and processing a power flow direction constraint by adopting a large M method; constructing a carbon flow balance equation simplified model based on piecewise linearization and a large M relaxation method; and based on a power flow direction constraint and node carbon flow balance constraint linearization method, establishing a power system low-carbon economic dispatching model coupled with carbon flow calculation, converting the power system low-carbon economic dispatching model into a linear optimization problem, and solving the linear optimization problem. According to the method, an original nonlinear problem is converted into a linear optimization planning problem, the solving universality of the power system low-carbon economic dispatching method containing carbon flow calculation is improved, good carbon accounting accuracy can be guaranteed, and the model solving efficiency is remarkably improved.

The present invention discloses a method for optimizing energy storage operation strategies that takes into account charging and discharging losses and real-time electricity prices. The method comprises the following steps: establishing an optimization model for user energy storage system operations; determining optimal charging and discharging periods based on electricity price curves; determining the charge and discharge states of the energy storage system during each period based on the optimal charging and discharging periods; converting the optimization model into a linear programming model based on the energy storage system charge and discharge states; and finally solving for an operation strategy that maximizes the operational efficiency of the energy storage system. The present invention converts the original model into a linear programming model, thereby simplifying an NP-hard combinatorial optimization problem into a linear optimization problem. This significantly improves the computational speed of the optimization process and can be conveniently run on resource-limited embedded industrial computers deployed on-site in energy storage systems.

This invention discloses a method and apparatus for optimizing the design of a space-based natural fly-around trajectory for a medium-to-high orbit target, belonging to the field of spacecraft orbital dynamics and optimization design technology. It includes: establishing a dynamic model of a medium-to-high orbit satellite considering complex perturbations; generating initial values ​​for the fly-around trajectory using a dual-mode approach combining direct position offset and CW equation analysis; constructing a multi-objective weighted optimization objective function, jointly constraining the root mean square of the relative distance difference, the extreme difference of the relative distance, and the mean absolute value of the difference in the relative distance interval time to evaluate trajectory morphological stability; setting three-dimensional coordinate constraints in the target satellite orbital coordinate system to avoid invalid solutions, and using the interior point method to solve the constrained nonlinear optimization problem; and achieving trajectory correction through dynamic model extrapolation, quantitative evaluation of performance indicators, and iterative optimization. This invention overcomes the defect of large extrapolation errors of the CW equation under complex perturbation environments by combining an accurate dynamic model with optimization methods, resulting in a natural fly-around trajectory with high accuracy and long holding time.

The invention provides a steering and torque distribution integrated control method for a four-wheel independent drive vehicle based on nonlinear predictive control, which comprises the following steps of: firstly, establishing a nonlinear state-space equation of the four-wheel independent drive vehicle, and establishing an optimal control problem based on the state-space equation; and then the optimal control problem of the original continuous time system is converted into a nonlinear optimization problem by using a forward Euler method and a multiple targeting method, and the singularity of the nonlinear optimization problem is effectively eliminated and the convexity of the nonlinear optimization problem is improved by adding a regular term in an optimization target. Finally, a moderate-scale sequential quadratic programming mode is designed, and compared with the prior art adopting hierarchical nonlinear predictive control, an open source solver IPOPT and the like, the control precision and the solving speed can be effectively improved.

The application provides a power and price segmentation declaration method and device for a wind storage power station participating in a forward market, wherein the method comprises the following steps: constructing a forward market declaration model of the wind storage power station based on an initial energy of the storage and a wind power output-out clearing price scenario set, a target function of the forward market declaration model comprising a current period net income and a next period storage energy value function; converting the forward market declaration model into a segmented linear optimization problem about accumulated declaration power; and solving the segmented linear optimization problem by using a dynamic programming algorithm to obtain a stepwise power and price declaration curve. The method generates a stepwise power and price declaration curve for the wind storage power station under the premise of not depending on information of other market subjects, not depending on accurate probability distribution assumptions, and not needing to call an external commercial optimization solver, and the stepwise power and price declaration curve takes into account current income, power shortage risk and future storage value, and has high calculation efficiency and strong engineering implementability, and the economic income and operation reliability of the wind storage power station in the forward market are improved.

The application relates to a robot data-driven control method and device based on an iterative algorithm. The method comprises the following steps: constructing a control strategy of a robot dynamics model according to a linear gain matrix to be optimized and a dimension-up characteristic function; the dimension-up characteristic function is used for mapping an original state space of the robot to a high-dimensional characteristic space in a Koopman operator framework, so that a nonlinear optimization problem of the control strategy is converted into a high-dimensional linear optimization problem; a Q function is defined; the Q function is converted; and data samples of z=(s, u) are used to iteratively calculate an updated value h of the matrix in a data-driven manner i The linear gain matrix is calculated, so that the control strategy of the robot dynamics model is obtained. The method can avoid introducing model errors and can calculate a more optimal control strategy.

The invention discloses a large-size high-uniformity coil design method based on adaptive PSOGWO fusion optimization, and the method comprises the steps: carrying out the Taylor expansion of a central magnetic field, and converting the solving of the axial position of a coil into a multivariable nonlinear optimization problem; and under geometric and unified manufacturing constraints, a self-adaptive PSOGWO fusion optimization algorithm is provided to obtain the optimal axial position of each pair of coils. According to the algorithm, particle swarm global exploration and grey wolf hunting type local mining are combined, and adaptive factors and weight proportions are introduced, so that the search precision and robustness in a complex multi-peak problem are improved, and the convergence stability and the local extremum resistance of the algorithm are improved. Under the background of a large-size coil, the uniform interval and the center uniformity of the coil designed by the novel optimization algorithm are obviously superior to those of a traditional coil and a coil designed by a single intelligent algorithm. The design method is suitable for scenes of SERF atom magnetometer compensation / modulation coils and the like, and can be used in cooperation with a multi-channel measurement scheme to improve the spatial resolution.

The invention discloses a virtual power plant optimal configuration method and system based on a hunting optimization algorithm, and the method comprises the steps: constructing a virtual power plant model according to the operation characteristics of distributed energy resources; according to the virtual power plant model, constructing an optimization model with the goal of minimizing the operation cost of the virtual power plant and the unsupplied electric quantity in extreme weather; according to the optimization model, performing iterative optimization on the position and capacity of the virtual power plant in the radial power distribution system by using a hunting optimization algorithm to obtain an optimal configuration scheme; and performing scheduling control on the virtual power plant according to the optimal configuration scheme. According to the method, a hunting optimization algorithm is adopted, the preying behavior of a hunter is simulated, global search and local development capabilities are balanced through a position updating mechanism, and the high-dimensional nonlinear optimization problem in virtual power plant configuration is effectively solved. Compared with a traditional method, the method can remarkably improve the toughness of the intelligent micro-grid under the extreme fault condition, reduces the operation cost, and improves the power supply reliability.

The present invention belongs to the field of cloud technology, and specifically relates to a multi-cloud heterogeneous computing power scheduling method and system, the method comprising: step 1: using the partial differential equation of node resources to describe the distribution of each node resource evolving over time; step 2: comparing the macroscopic available resource vector with the measured time-varying task requirements to form a feasibility constraint; step 3: constructing a comprehensive optimization objective function; the objectives of the comprehensive optimization objective function include resource mismatch, delay, and energy consumption; step 4: constructing a KKT system using the primal-dual interior point method, combining it with a large-scale nonlinear optimization problem, solving the overall problem of the partial differential equation containing node resources, and obtaining a solution result. The present invention can accurately describe the dynamic evolution and spatial distribution of resources, reasonably deal with the time-varying and uncertainty of task requirements, effectively control the risks in the resource scheduling process, and achieve global optimization in terms of resource mismatch, delay, and energy consumption.

The invention discloses a configuration optimization method and device for series capacitance compensators, terminal equipment and a storage medium, and the method comprises the steps: taking the equipment operation parameters of a power distribution network, the installation positions of the series capacitance compensators, and the reactance compensation coefficients of the series capacitance compensators for a line where the series capacitance compensators are located as control variables; randomly generating a plurality of groups of initial control variable sets, taking one group of initial control variable sets as an initial planet, and repeatedly performing iterative optimization operation on the plurality of initial planets under a preset constraint function for maintaining the power flow stability of the power distribution network until the fitness value of the generated target sun is greater than a preset threshold value, and stopping the iterative optimization operation, and generating a configuration optimization scheme of the series capacitance compensator. Therefore, through the advantages of the Kepler optimization algorithm in the complex nonlinear optimization problem, the Kepler optimization algorithm is applied to configuration optimization of the series capacitor compensator, and the problem that a TCSC configuration scheme output by an existing optimization configuration algorithm cannot effectively reduce the power transmission loss is solved.

The present invention discloses a Ceph storage system data migration method based on P4 network telemetry optimization. By introducing P4 network telemetry technology, real-time monitoring of network and storage status is achieved, detailed key performance indicators are obtained, and data migration decisions are made fine-grained down to the level of a single placement group based on this information. During the data migration decision-making process, complex network and storage problems are abstracted into a dual-objective optimization problem. Based on this, a migration control algorithm based on time-triggered TOPSIS and KM is proposed. By dividing and controlling time slots, the constrained nonlinear optimization problem is converted into a linear optimization problem controlled by multiple time slots. The correspondence between the outgoing set and the incoming set is determined at intervals. The OSD migrates based on this correspondence, and adjustments are made after the time slot ends. The present invention can enhance the adaptability of heterogeneous storage environments, improve system response speed, and enhance decision-making capabilities.

The application discloses a waveform design method based on a weighted fuzzy function and relates to the technical field of radar detection. The method comprises the following steps: defining a weighted fuzzy function, constructing an optimization problem based on the weighted fuzzy function, converting a quartic objective function optimization problem into a linear optimization problem through continuous convex approximation, relaxing the constraint, converting the original problem into a convex optimization problem, normalizing the amplitude of the optimization result, repeating the step until the objective function converges, and finally obtaining an optimal waveform. The application can obtain an optimal waveform with an ideal fuzzy function shape through the response weight adjustment of each distance-Doppler pair of the fuzzy function, and supports the clutter suppression in radar coherent processing and other engineering applications.