151 results about "Nonlinear optimization problem" patented technology

The invention discloses a power system external impact vulnerability assessment method based on a multi-target zero-sum game. The method comprises the following steps: establishing a power grid unit damage correlation propagation model according to a power grid structure and the types and number of actually included power grid units; on the basis of the power grid unit damage correlation propagation model, an attack and defense multi-target zero-sum game model is established according to optimization targets of attackers and defenders for different target functions and resource constraints of the attackers and the defenders; the attack and defense multi-target zero-sum game model is expressed as a double-layer mixed integer nonlinear optimization problem, and the problem is solved to obtain the minimum attack cost required by an external attacker to cause complete failure of the power system. According to the method, a multi-target attack and defense game model is established, and the minimum attack cost required by complete failure of the power system is solved from the perspective of attackers so as to reflect the vulnerability and security of a defense strategy of the power system on the overall level.

The invention is suitable for the technical field of integrated energy microgrid scheduling, and provides a power distribution network random distributed robust optimization scheduling method of an integrated energy microgrid group, comprising the following steps: constructing a DN structure comprising a plurality of microgrids; the DN day-ahead scheduling optimization model containing the IEMGs pays attention to operation cost and constraint conditions; for the DN, the problem of non-convex nonlinear optimization including line loss is solved; for the IEMG, the model is used for processing a convex optimization problem considering WT and PV output power uncertainty; power exchange is carried out between the DN and the IEMG through a connecting line, so that a double-layer model framework is constructed to analyze the system; the solving process is a double-layer optimization iteration process, an outer layer obtains an interaction power value between the DN and the IEMG by using an ATC method, and an inner layer uses Camp; the CG method solves the problem of random distribution robust optimization of IEMG. According to the method, the carbon emission and the operation cost of the system can be effectively reduced, meanwhile, stable operation of the DN is ensured, and a theoretical basis is provided for efficient utilization of ammonia energy in the DN.

The invention discloses an unmanned ship dynamic obstacle avoidance control system and method based on stochastic nonlinear model predictive control, and adopts a control structure of a planning layer and a control layer to solve the problem of dynamic obstacle avoidance of an unmanned ship in a complex obstacle environment. The planning layer is based on a random nonlinear model predictive control technology, and firstly, random modeling is carried out on the unmanned ship and dynamic obstacles encountered by the unmanned ship in the sailing process; designing obstacle avoidance switching conditions according to the whole actual obstacle avoidance process; in an obstacle avoidance random nonlinear model prediction planning control algorithm, uncertainty propagation of a dynamic obstacle and an unmanned ship in a whole prediction time domain is evaluated through unscented transformation, then obstacle avoidance constraint is described in a chance constraint mode, a chance constraint expectation operator transformation algorithm and a unit jump function approximation algorithm are designed, and a chance constraint expectation operator transformation algorithm and a unit jump function approximation algorithm are designed. Opportunity constraints are converted into deterministic constraints, and then a nonlinear optimization problem is constructed for planning. And the control layer adopts a PI D algorithm to track a planning value so as to realize dynamic obstacle avoidance of the unmanned ship.

A computer implemented method for obtaining one or more optimal solutions to a given classical constraint linear or non-linear optimization problem is provided. Each solution is specified and identified by a set of parameters and the one or more optimal solutions provide optimal values of a cost function that is a linear or non-linear function of one or more decision variables. The one or more optimal solutions fulfill one or more constraints comprising a set of equality constraints and / or a set of inequality constraints, where each equality constraint and each inequality constraint comprises a linear or non-linear complex functional form. A quantum computation component and a classical computation component of the hybrid quantum-classical computer system are used together to solve the classical constraint optimization problem by iteration. While performing the method, the quantum computation component executes a parameterized quantum circuit with surrogate phase operators and with the set of parameters.

The invention discloses a nonlinear model predictive control method for the strip steel temperature of a continuous annealing furnace, and relates to the technical field of metallurgical industry automation control, and the method comprises the following steps: collecting historical data in the production process of the continuous annealing furnace; a nonlinear autoregression exogenous input model based on Sigmoid-ARX is established; the model parameters are optimized through a Levenberg-Marquardt algorithm, and the model parameters are optimized through the Levenberg-Marquardt algorithm; based on the optimized Sigmoid-ARX model, designing a nonlinear model prediction controller, performing local linearization near a given working point, and converting a nonlinear optimization problem into a quadratic programming problem; by constructing the Sigmoid-ARX nonlinear model and the predictive control capability and system-level constraint processing characteristics of the model predictive control algorithm, the strip steel temperature predictive precision is improved, stable control over strip steel of different specifications in the transition stage is achieved by optimizing the control strategy, the standard deviation of the strip steel temperature and speed is reduced, and the stability of the strip steel temperature and speed is improved. The average speed of a production line is improved, and the yield is increased.

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 an online identification method for kinetic parameters of a joint type series robot, which comprises the following steps: S1, for a to-be-identified joint type series industrial robot, establishing a robot kinetic model considering friction; s2, linearization is carried out on the joint type series industrial robot dynamic model; s3, designing an optimal excitation track for an identification experiment, and obtaining an excitation track by using five finite Fourier series and solving a constrained nonlinear optimization problem; and S4, the series robot runs the excitation track, meanwhile, the position information of each joint of the robot and the driving current of each joint at each moment are collected, and to-be-identified parameters are calculated on line at each moment. According to the method, online identification of the robot kinetic parameters can be carried out, the identification efficiency of the robot kinetic parameters is improved, and the method can be used for industrial scenes such as robot moving and carrying, polishing and grinding and heavy-load hydraulic pressure.

The invention discloses a whole-body motion trail optimization method and system for a four-foot single-arm operation robot, and relates to the technical field of four-foot single-arm robot control, and the method comprises the steps: respectively building kinematic models of four limbs and operation arms of a robot and a kinetic model of a trunk mass center; the motion gait of the robot is described through continuous time variables, the motion of the robot is decomposed into trunk mass center motion, leg motion and operation arm motion, motion curves of all the motions are described, and constraint conditions borne by the robot in the motion process are constructed; wherein the time variable is robot phase duration; according to the current state of the robot, a set expected operation target, a kinematics model, a dynamical model, a motion gait, motion curves of various decomposed motions and constraint conditions of robot motion, a whole-body motion track nonlinear optimization problem of robot operation is established, an optimization solution library is adopted for solving the problem, and the whole-body motion track nonlinear optimization problem of robot operation is obtained. And obtaining an optimal motion trail.

The invention discloses a composite material storage life prediction method based on variable transformation and a genetic algorithm, and relates to the technical field of composite material performance evaluation and life prediction, and the method comprises the steps: obtaining performance degradation data at different temperatures through an accelerated aging test, and building a semi-empirical performance degradation model; specific variable transformation is introduced, a nonlinear optimization problem is converted into a linear regression problem, partial model parameters are rapidly solved by adopting a least square method, and meanwhile, key nonlinear parameters are globally optimized by using a genetic algorithm, so that the defect that a traditional method is easy to fall into local optimum is overcome. And establishing a temperature-service life relationship by combining with an archenius model, and realizing storage life prediction at normal temperature. The method also integrates sensitivity analysis and uncertainty quantification functions, and provides reliability evaluation for a prediction result. The method fully considers the influence of the environment temperature, meets the requirement of efficient prediction, and improves the precision and stability of life prediction.

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 invention discloses a multi-parameter collaborative optimization method for deterministic grinding and polishing of a limited space robot, and the method comprises the steps: bringing a material removal amount prediction model, a grinding and polishing track, a robot posture and dynamic error compensation into a unified optimization process through a multi-step collaborative optimization framework; the nonlinear coupling influence of robot attitude adjustment and feeding speed fluctuation on removal amount distribution is reduced, the cooperative problems of removal amount out-of-tolerance, coverage uniformity and interference avoidance caused by multi-parameter coupling in a narrow flow channel are solved, finally, the high-dimensional nonlinear optimization problem is decomposed through an alternating direction multiplier method, the convergence efficiency is remarkably improved, and the convergence efficiency is improved. And it is ensured that the machined surface meets the preset tolerance requirement, and high-precision deterministic grinding and polishing under the limited space are achieved.

The invention belongs to the technical field of cloud, and particularly relates to a multi-cloud heterogeneous computing power scheduling method and system, and the method comprises the steps: 1, describing the distribution of each node resource along with time evolution through employing a partial differential equation of the node resources; 2, comparing the macroscopic available resource vector with a measured task time-varying demand to form a feasibility constraint; 3, constructing a comprehensive optimization objective function; the objectives of the comprehensive optimization objective function comprise resource mismatch, delay and energy consumption; and 4, constructing a KKT system by using a primal-dual interior point method, and solving an overall problem of a partial differential equation containing node resources in combination with a large nonlinear optimization problem to obtain a solving result. According to the method, the dynamic evolution and spatial distribution of resources can be accurately described, the time-varying property and uncertainty of task requirements are reasonably dealt with, the risk in the resource scheduling process is effectively controlled, and global optimization is achieved in the aspects of resource mismatching, delay, energy consumption and the like.

The invention discloses a method for diagnosing the ground insulation defect of an urban rail transit steel rail, and the method specifically comprises the steps: building a steel rail potential and stray current collection network polarization potential synchronous monitoring system, and building a synchronous data set of the steel rail potential and stray current collection network polarization potential; calculating an influence coefficient by using the position of the stray current collection network polarization potential monitoring point and the position of the steel rail potential monitoring point, and establishing a steel rail insulation defect diagnosis equation by using the steel rail potential monitoring data and the stray current collection network polarization potential monitoring data; rewriting the steel rail insulation defect diagnosis equation to obtain a scalar function; and converting the problem of diagnosing the ground insulation defect of the steel rail into an unconstrained nonlinear optimization problem, and solving to obtain the value of the ground resistance of the steel rail. According to the invention, effective monitoring of the ground insulation defect of the steel rail can be realized, the position of the ground insulation defect of the steel rail can be positioned, and the size of the ground transition resistance of the steel rail is evaluated; and the treatment cost of the stray current is reduced, and the harm of the stray current is reduced.

The invention discloses a heterogeneous detection sensor optimal configuration method based on an improved subtraction averaging optimizer, and belongs to sensor optimal configuration. The method comprises the following steps: constructing an air-ground heterogeneous robot sensor optimization configuration model; a multi-check mechanism is introduced to improve a subtraction averaging optimizer, a feasibility criterion is fused to process constraint conditions, the rapid convergence capability of the algorithm under the condition of a small number of individuals is enhanced, the rapid optimization performance of the algorithm in the process of processing a multi-constraint highly nonlinear optimization problem is improved, and the sensor optimization configuration problem is solved by improving the algorithm. Compared with the prior art, scientific and reasonable guidance is provided for the space-ground heterogeneous robot sensor configuration problem by applying an advanced artificial intelligence algorithm, and intuitive deviation possibly caused by dependence on individual engineering experience is effectively avoided.

The invention discloses a real-time solution reconstruction planning method for a man-machine interaction rope traction parallel robot, which belongs to the field of robot configuration planning, and comprises the following steps of: 1, constructing kinematics and dynamics models according to the position relationship between a rope leading-out point and a movable platform, and establishing an admittance model; 2, according to the dynamics and admittance model, a hyperplane movement method is used for representing a force feasible condition, and an optimization problem objective function is set according to the force feasible condition; 3, expressing a rope leading-out point solving problem as a nonlinear optimization problem through constraint, approximating the nonlinear optimization problem as a linear optimization problem through linear approximation, and solving an approximate optimal solution of the linear optimization problem; and 4, an artificial potential field is set according to dynamic characteristics, the approximate optimal solution is corrected, the solved rope leading-out point position is not located at the boundary of a solution space while the solving speed is guaranteed, and reconstruction planning of real-time solving of the robot rope leading-out point is completed. According to the method, the solving instantaneity and the man-machine interaction stability can be ensured.

The invention relates to a priority-based high-visibility stream-by-stream data packet sampling method and system. Comprising the following steps: step 1, flow initiation and priority marking; step 2, flow establishment and controller decision making; step 3, rule issuing and strategy execution; and step 4, data forwarding, scheduling and sampling: a priority scheduling mechanism of a data plane and global optimization of a control plane are decoupled and coordinated. In a data plane, a terminal host marks a degradation priority for a data packet according to the number of bytes sent by a stream, and a switch performs strict queue scheduling and probability sampling based on the priority. In a control plane, a controller carries out collaborative modeling on the configuration of routing, sampling probability and degradation threshold as a nonlinear optimization problem with maximization of sampling utility as a target, and an efficient online primitive-dual interior point method is adopted for solving. According to the method, high visibility and load balancing are finally ensured, and meanwhile, the control and calculation overhead is remarkably reduced.

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.

The application discloses a flexible load intelligent optimization scheduling method for improving the economy of photovoltaic access distribution networks. A multi-type flexible load grading and scheduling optimization model is established. On the one hand, according to the demand side response demand, the flexible load is divided into three categories, namely interruptible load, translatable load and adjustable load, a flexible load operation optimization model is constructed, the reasonable allocation of various flexible loads in different periods is realized, the peak regulation pressure and resource waste problems in the distribution network are effectively alleviated, the purchase of electricity during the power consumption peak can be effectively avoided, and the economy of the distribution network is improved. On the other hand, for the discrete and continuous mixed data distribution environment, a Latin square quantum-inspired genetic algorithm (LSQGA) is used to process the nonlinear optimization problem in the environment, so that the optimal feasible region in the macro data space can be quickly searched, and the feasibility of the optimization model under the extreme scenario is improved.

The invention relates to the technical field of new energy automobile intelligent driving, and discloses a new energy automobile multi-mode sensing trajectory planning method and system, and the method comprises the steps: obtaining multi-mode original sensor data, and carrying out the preprocessing; feature extraction and cross attention fusion are carried out; performing dynamic obstacle prediction, and setting a dynamic safety buffer area; constructing a behavior decision input state vector, and reasoning and adjusting a behavior decision; generating a candidate trajectory curve and performing feasibility check on the candidate trajectory curve; comprehensively evaluating the candidate track set; predicting a nonlinear optimization problem by taking the initial optimal trajectory curve as a reference trajectory, and solving and verifying the nonlinear optimization problem; according to the method, robust fusion and adaptive weight adjustment of multi-modal sensor data are realized by establishing a sensor health monitoring mechanism, a dynamic weight distribution strategy and a cross attention fusion network.

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 invention discloses a vehicle three-way cooperative auxiliary control method fusing PINN tire transverse and longitudinal force prediction. Comprising the following steps: constructing a dynamic model of a vehicle based on a force balance equation and a moment balance equation of the vehicle; constructing a physical information neural network model; training the physical information neural network model by taking the dynamic model as a dynamic constraint and taking the minimum physical residual error loss as a target to obtain a physical prediction model; the physical prediction model is fused, a cost function with the longitudinal resultant force, the additional yawing moment, the slippage loss power and the motor loss power of the vehicle under the action of the auxiliary control quantity as factors is constructed, the cost function is solved with the minimum cost function as the target, and the minimum cost function is obtained. The auxiliary control quantity used for conducting three-way cooperative auxiliary control on the vehicle is obtained; and adopting the auxiliary control quantity pair. According to the method, the problems that tire model parameters are difficult to calibrate and the cost is high during vehicle three-way cooperative auxiliary control, and key performance indexes of a cost function in a nonlinear optimization problem have characterization defects are solved.

The present application relates to the technical field of wisdom agriculture, and particularly relates to a cloud-edge collaborative computing offloading method for wisdom agriculture. The present application constructs an adaptive task quantity prediction function, a response time model, a power consumption model and a load model, converts actual problems in the system into basic mathematical models, and specifically abstracts problems into optimal solution problems of mixed integer nonlinear optimization problems. Finally, the optimal computing offloading result is found by using the defined adaptive function and the particle swarm algorithm. Compared with other cloud-edge collaborative computing offloading methods applied in wisdom agriculture, the present application overcomes the error conflicts caused by traditional static modeling, improves the accuracy of the computing offloading result, and greatly improves the real-time performance and accuracy of terminal task computing.

The present invention aims to solve the high latency problem in 5G ultra-dense networks caused by users with insufficient computing power processing low-latency, high-reliability applications, and implements a joint optimization strategy for offloading strategy and resource allocation under the condition of limited computing resources and channel resources. First, a system model of MEC and local computing in a 5G ultra-dense network scenario is constructed, and a mixed integer nonlinear optimization problem that minimizes task completion time is constructed. Then, a joint optimization strategy for task offloading decision and resource allocation is proposed for the optimization problem. This strategy first uses variable substitution to simplify the problem, and then solves it by decomposing the sub-problems. The original problem is decomposed into two sub-problems: computing resource allocation and channel resource allocation. The Lagrange multiplier method is first used to obtain the optimal solution for computing resources, and then a channel resource allocation algorithm based on the idea of differential evolution is used to perform channel resource allocation under the condition of determining the optimal solution for each computing resource allocation.

The invention relates to a resource scheduling method based on risk awareness, and belongs to the technical field of radio communication. The method comprises the following steps: respectively constructing communication models, time delay models and energy consumption models of a space-based network, an air-based network and a ground-based network in a space-air-ground integrated network; under the energy constraint of the unmanned aerial vehicle, the problem is expressed as an integer nonlinear optimization problem with uncertainty, and a conditional risk value CVaR is utilized to measure the check risk; the cost and the risk are evaluated by using double-critic networks, each network independently evaluates an action value function, and then comprehensive evaluation is carried out; through deep neural network parameterization and time sequence difference updating, an optimal task unloading and resource scheduling strategy is realized; the influence of inaccuracy of a single network on strategy updating is reduced, and meanwhile, the learning stability and convergence are improved; the resource scheduling method based on risk awareness provided by the invention not only can meet diversified requirements of users, but also can solve the problem of high risk caused by traditional decisions.

The invention relates to the technical field of energy efficiency optimization, in particular to a self-adaptive load intelligent energy supply platform of a fuel cell range extending power system. Multi-source load data is collected through the environment perception and multi-source load monitoring module, after preprocessing and feature extraction, the dynamic power optimization decision module constructs a real-time rolling optimization model, the multi-constraint nonlinear optimization problem is solved in real time based on an equivalent hydrogen consumption minimization strategy, the optimal fuel cell output power is calculated, and the fuel cell output power is optimized. Precise energy supply is achieved, and the energy utilization efficiency is improved; accurate power following under complex working conditions is realized through multivariable decoupling control, cooling fan and air inlet valve optimization calculation is performed based on the optimal fuel cell output power, fault detection decision, fault category determination and corresponding optimization operation execution are performed by using an SVM support vector machine, and stable operation of the system is guaranteed.

The invention discloses a virtual filling method for chemical mechanical polishing based on neural network gradient feedback, and belongs to the field of semiconductors and CMOS (complementary metal oxide semiconductor) super-large-scale integrated circuits. The method is divided into three stages of layout data processing, neural network prediction and optimization solution and virtual filling insertion, an input GDS layout is divided into uniform windows, a fillable area is determined according to pattern information in the windows, the window density and the upper and lower limits of the window density are extracted, window data is converted into a nonlinear optimization problem, and the optimization problem is solved. The flatness loss of the layout after CMP is predicted by calling a neural network to serve as an optimization target, meanwhile, the input density of each window is iterated according to gradient information provided by the neural network, and finally, a virtually filled layout file is output. According to the method, the interpretability of a prediction result is enhanced in the calculation process, and the problems of low efficiency and suboptimal solution of a traditional optimization algorithm in the aspect of large-scale optimization are solved.

The invention discloses a three-dimensional positioning method and system for a bimodal target, and relates to the technical field of indoor positioning, and the method comprises the steps: collecting the phase ranging data of a connected state base station and the received signal strength indication data of a broadcast state base station, and forming a bimodal perception data set; selecting a static positioning model or a dynamic positioning model according to the motion state of a to-be-positioned point, and converting a positioning problem into a nonlinear optimization problem based on the bimodal perception data set; and solving the nonlinear optimization problem by adopting a CBS algorithm to obtain the position estimation of the point to be positioned in the three-dimensional space. According to the method, three-dimensional positioning of a static target and smooth track positioning of a dynamic target are realized at the same time, the optimization range is effectively constrained, the system deployment complexity and the calculation load are reduced, the precision and robustness of indoor three-dimensional positioning are effectively improved, and the method is suitable for application scenes with strict requirements for quick response and low-cost deployment.

The application relates to a vehicle longitudinal and lateral motion cooperative control method based on a fast solving algorithm, which comprises the following steps: calculating a desired yaw angular velocity according to a steering wheel turning angle and a current vehicle running speed; constructing a nonlinear optimization problem according to the desired yaw angular velocity and a current actual motion state of the vehicle, wherein an objective function of the nonlinear optimization problem is used to track the desired yaw angular velocity, and meanwhile, lateral speed and tire slip rate of the vehicle are inhibited; solving the nonlinear optimization problem to calculate desired slip rates of four tires; calculating additional torques of the tires according to actual slip rates and desired slip rates of the tires; and sending the additional torques of the tires into an executor of the vehicle for cooperative control. Compared with the prior art, the application can effectively inhibit the tire slip rate, avoid vehicle skidding, and the provided fast solving algorithm can greatly improve solving efficiency and improve real-time performance.

This application belongs to the field of unmanned surface vessel (USV) control technology, specifically relating to a method and apparatus for predictive control of USV trajectory tracking using dynamic reference linearization. The method includes: establishing and discretizing a nonlinear dynamic model of the USV to construct a predictive model; constructing an online nonlinear optimization problem including terminal stability components based on this model; generating a dynamic reference trajectory in each control cycle based on the optimal solution of the previous moment, and linearizing the nonlinear model point-by-point along this trajectory to obtain a high-fidelity linear time-varying predictive model; further transforming the original nonlinear programming problem into an efficiently solvable quadratic programming problem; and outputting control quantities in real time by solving this problem and integrating reference commands. This application ensures the theoretical stability of the closed-loop system, achieves a synergy of high precision, high real-time performance, and high reliability, and effectively improves the trajectory tracking performance of USVs in complex marine missions.

The invention discloses a parameter and tire force real-time estimation method and system based on multi-model fusion, and relates to the technical field of vehicle parameter identification, and the method comprises the steps: building a three-state monorail vehicle dynamics model and a tire model according to vehicle parameters and dynamics parameters; performing formula transformation on the three-state monorail vehicle dynamics model to respectively obtain formulas of longitudinal, transverse and yaw angular accelerations; dividing parameters in the formula into parameters which can be collected by a low-cost sensor and parameters to be estimated, and respectively updating the formulas of longitudinal, transverse and yaw angular accelerations; constructing a nonlinear optimization problem based on the updated formula; according to the nonlinear optimization problem, based on vehicle parameters and kinetic parameters in the historical driving process, parameter estimation is carried out, and based on a parameter estimation result, real-time state estimation is carried out on tire force. Complete kinetic parameters can be estimated in real time through a low-cost sensor, and the real-time state of the tire is estimated based on the complete kinetic parameters.