36results about How to "Guaranteed Global Convergence" patented technology

The invention provides a method and device for route programming in a dynamic unknown environment. In the moving process of a robot along a preset optimal route, a collision-free optimal route is obtained through calculation by using a linear programming gradient method and through environment information acquired by a preset rolling window, and then partial routes are obtained through calculation carried out on the obtained collision-free optimal route and acquired perceptual information in the rolling window. In the calculation process, the rolling window is used for reducing calculation amount and improving efficiency, and the linear programming gradient method is applied to ensure global convergence and to prevent from being caught in the problems of local minimum and oscillation.

The invention discloses a robust controller of a permanent magnet synchronous motor based on a fuzzy-neural network generalized inverse and a construction method thereof. The construction method of the invention comprises the following steps of: combining an internal model controller and a fuzzy-neural network generalized inverse to form a compound controlled object; serially connecting two linear transfer functions and one integrator with the fuzzy-neural network with determined parameters and weight coefficients to form the fuzzy-neural network generalized inverse, serially connecting the fuzzy-neural network generalized inverse and the compound controlled object to form a generalized pseudo-linear system, linearizing a PMSM (permanent magnet synchronous motor), and decoupling and equalizing the linearized PMSM into a second-order speed pseudo-linear subsystem and a first-order current pseudo-linear subsystem; and respectively introducing an internal-model control method in the two pseudo-linear subsystems to construct the internal model controller. The robust controller of the invention has the advantages of overcoming the dependence and local convergence of the optimal gradient method on initial values and solving the problems of randomness and probability caused by using the simple genetic algorithm, obtaining the high performance control, anti-disturbance performance and adaptability of the motor and simplifying the control difficulty, along with simple structure and high system robustness.

The invention relates to an urban air quality grade predicting method based on multi-field characteristics. The method comprises a data preprocessing stage, a training stage and a predicting stage. In the data preprocessing stage, mesh generation is carried out to obtain a training sample, and a training data set is obtained. In the training stage, the optimal parameter of a conditional random field model is estimated, an optimal model shown in the specification is obtained, and the conditional random field model is output. In the predicting stage, the maximum output sequence, shown in the specification, of the conditional probability P(Y / X<f>) is obtained by combining the viterbi algorithm and the conditional probability model shown in the specification and obtained in learning of the training stage, and a prediction result is output. The AQI grade can be predicted more accurately, the AQI grade prediction in the following time quantum of all stations is carried out, and the requirement for selecting the time quantum with the high air quality to go out and take activities of people is met.

The invention provides a goods allocation optimization method applied to a Flying-V untraditional layout warehouse. The method is characterized by comprising the steps that S1, goods allocation relevant parameters of the Flying-V warehouse are set; S2, the goods allocation parameters are initialized; S3, a population is initialized according to an information list of to-be-stored goods; S4, an adaptive genetic algorithm is adopted to perform individual optimal selection on the population; S5, whether the number of algorithm termination iterations is reached is judged, if yes, the step S6 is performed, and otherwise the step S4 continues to be cycled; and S6, an optimal goods allocation scheme is output. The goods allocation optimization method is applied to the application occasion of "Flying-V untraditional warehouse layout", goods storage and delivery efficiency and a lowest gravity center of a goods shelf after the goods are placed on the goods shelf are optimized, and a multi-target optimization problem processing method with different dimensions is provided; by the adoption of the adaptive genetic algorithm, the crossover rate and the variation rate change along with adaptation values; and population diversity is maintained, and global convergence of the genetic algorithm is guaranteed.

The invention discloses a classification method for a multi-kernel support vector machine, which relates to the artificial intelligence field, in particular to the data mining technology, and comprises a data pretreatment section, a kernel function selection section, a support vector machine realizing section, and a human-computer interaction section. The work processing comprises that users submit classification request of data to the DPP, then KSP chooses the kernel function, an SILP solution module converts a multi-kernel support vector machine problem to an SILP problem and then solves the problem, a condition detecting module detects whether the condition is satisfied, and if the condition is satisfied, the HIP returns the result to users, otherwise, the parameter and the objective function are updated, and the SILP solution module is transferred to solve. The invention improves the capability of processing complex data of the support vector machine through multi-kernel functions, promotes the complexity of a module and the calculation, and converts the multi-kernel support vector machine problem to a semi-infinite linear program for avoiding the increasing of kernel functions simultaneously, and solves through a method of global convergence.

The invention relates to the technical field of underwater positioning, in particular to a single beacon positioning method of an underwater vehicle. According to the single beacon positioning methodof the underwater vehicle with global convergence, an underwater vehicle is provided with a hydrophone, a Doppler velocimeter, a depth meter, an attitude heading reference system and a GPS; and underwater acoustic beacons periodically broadcast underwater acoustic signals. According to the method, a nonlinear single beacon positioning model in a discrete state is converted into a linear time-varying model through state augmentation; when the underwater acoustic signal is not received, the relative speed and the attitude of the underwater vehicle and water by a device carried by the underwatervehicle are obtained to carry out dead reckoning; after the underwater acoustic signal is received, the underwater acoustic signal transmission time is collected through known underwater acoustic signal transmission time and is taken as an observation variable, and dead reckoning data and observation data of various sensors are simultaneously integrated to predict and update the single beacon positioning based on Kalman filtering. On the premise of meeting the observability of a positioning model, the method has global exponential convergence.

The invention discloses a speed sensorless control method of a permanent magnet synchronous motor, and belongs to the field of permanent magnet motors. A conventional assumption rotating coordinate method is adopted as a basis, on the basis of a model motor method, an estimation rotating speed is directly adopted as a control quantity through a sliding mode control method, then, a self-adaption law of the estimation rotating speed is obtained through a Lyapunov non-linear design method, an algorithm is greatly simplified, the system response speed is increased, meanwhile, forward and reverse rotation of the motor can be achieved, and the method can be used for the permanent magnet synchronous motor.

The invention relates to a voltage sag estimation method based on a quantum-behaved particle swarm optimization algorithm. The method comprises the steps: (1), each line in a power grid is divided into a plurality of sections by using a fault position method, a P fault section is set among the multiple sections, and a fault point is used for replacing a fault section; (2), an observation matrix M of a monitoring bus is established by using a random fault point and is used for expressing a relation between a state variable vector X and a measurement vector H; (3), T critical voltage values are set in an overall power grid and a general model of a state estimation method is established; (4), according to the general model of the state estimation method and the relation between a state variable vector Xt and a measurement vector Ht, an objective function and a constraint condition of an optimization problem are obtained; (5), on the basis of a quantum-behaved particle swarm optimization algorithm, an optimal solution of the optimization problem is obtained, wherein the optimal solution expresses a voltage sag frequency of a bus that is not monitored in the grid. Compared with the prior art, the provided method has advantages of comprehensive consideration, high advanced level, high efficiency, and wide application range and the like.

The invention discloses an attribute selection method based on a binary system firefly algorithm. The attribute selection method is characterized in comprising the following steps: 1: utilizing a fractal dimension box-counting method to calculate a fractal dimension of a high-dimension data set, and obtaining the number of attributes which need to be selected; 2: initializing a firefly population; 3: utilizing the binary system firefly algorithm to select a plurality of attributes of the high-dimension data set to obtain an optimal attribute subset; 4: outputting an optimal solution. The attribute selection method uses the binary system firefly algorithm as a search strategy of attribute selection, takes the fractal dimension as an attribute selection evaluation measurement criterion, and selects the optimal attribute subset from the plurality of index attributes of the high-dimension data set, so that the data processing complexity can be lowered, and the data processing efficiency is improved so as to meet the requirements of solving practical problems.

The invention belongs to the field of robot control and nonlinear systems, and particularly relates to a robot self-adaptive impedance control method based on a biological heuristic neural network. The problem that real-time accurate control of a robot in a complex nonlinear system cannot be realized in the prior art is solved. The robot self-adaptive impedance control method comprises the steps that initial control moment, expected impedance and movement trails of a system are obtained; a dynamic equation and an expected impedance model of n-degree-of-freedom mechanical arm system containingimpedance are built and a t-moment system real state and an expected state of the robot are correspondingly obtained; a self-adaptive controller is built and a (t+1) moment control moment is obtainedbased on full-state feedback and the biological heuristic neural network; and states are circularly obtained and self-adaptive impedance control and movement control are carried out, until a robot mechanical arm completes the movement trails. According to the robot self-adaptive impedance control method based on the biological heuristic neural network, by combining with a biological heuristic neural network structure and delay feedback, a Hebbian algorithm with an award value adjustment and a structure combining network estimation and full-state feedback are adopted, the system is stable, andthe control precision is high.

The invention provides a polarity searching method of a fixed-polarity Reed-Muller logic circuit. The best polarity of an FPRM logic circuit is searched by using the new binary differential evolution algorithm, compared with a polarity searching method of the FPRM logic circuit based on the genetic algorithm, the capacity of local optimum and the capacity of premature convergence are avoided, and the convergence rate and the polarity searching efficiency are improved. The method comprises the following steps that firstly, a Boolean logic circuit is read; secondly, an evolution parameter is input; thirdly, an initial population is generated randomly, wherein the polarity is encoded into a binary individual; fourthly, the improved binary stochastic mutation operation is performed; fifthly, the binomial crossover operation is performed; an FPRM expression of the target individual and the FPRM expression of a test individual of the target individual are obtained; seventhly, the fitness value of the target individual and the fitness value of the test individual of the target individual are calculated; eighthly, the greedy selection operation and an elitism selection strategy are performed; ninthly, if the current evolution algebra is smaller than the maximum evolution algebra, the fourth to eighth steps are executed in sequence, and otherwise, the best polarity is output.

The invention provides an improved ICCP terrain matching method under a large initial positioning error. According to the method, real-time underwater elevation acquisition is carried out by using a multi-beam sounding system, an underwater terrain matching template is established, isodepth point values are extracted according to a pre-stored underwater digital topographic map, a to-be-matched terrain area is established, and the underwater terrain matching similarity is analyzed, an optimal matching sequence is selected, and rigid transformation is solved, and the matched object is continuously rotated and translated, so that the matched object gradually approaches the target object until a certain index or iteration times are reached, thereby obtaining the position information of the underwater vehicle. Through similarity analysis of the isometric value points, mismatching of the isometric value points is effectively avoided, and the global convergence of the improved algorithm is ensured while the precision and the stability of the algorithm are improved.

The invention aims to provide a full waveform inversion method and system based on a non-monotonic search technology. The method comprises the following steps: firstly, dividing multi-scale inversionfrequencies into N frequency groups according to a set frequency group range; secondly, determining a speed model of next iteration corresponding to the jth frequency group according to the seismic observation data and an initial speed model by adopting a non-monotonic search technology in combination with the initial step length; judging whether j is greater than N or not; if j is greater than N,outputting the speed model of the next iteration corresponding to the jth frequency group as an optimal speed model; and finally, performing seismic imaging based on the optimal speed model. According to the method, the step length is determined by combining the non-monotonic search technology with the initial step length, so that a high-precision inversion result can be obtained on the premise of ensuring the inversion calculation efficiency; and when the initial speed model is poor, the global convergence of inversion can be ensured to a certain extent, and a good inversion result can be obtained.

The invention relates to an identification method of random structural damage based on genetic algorithm and static force measurement data. The identification method of the random structural damage based on the genetic algorithm and the static force measurement data comprises the following steps: (1) preliminarily obtaining statistical properties of random structural damage indexes; (2) defining the probability of unit damage as the random rigidity before occurrence of the damage or the probability that an elastic modulus Kai is larger than Kdi; (3) introducing damage probability indexes, determining several units of the damage probability indexes as non-damage units, and conducting corresponding adjustment on the statistical properties of the damage indexes; (5) going back to the governing equation of the initial damage identification in the step (1), and obtaining the objective function of the structural damage indexes after rearranging; and (6) solving the minimum value of the objective function in the step (5) by means of the genetic algorithm, and obtaining the statistical properties of the damage indexes. The identification method of the random structural damage based on the genetic algorithm and the static force measurement data has the advantages that multiple damage identification of different damage degrees can be carried out due to the fact that the genetic optimized algorithm has little limit on the type, the quantity and the size of the parameter.

The invention discloses a node localization method of a distributed wireless sensor network based on the angle of arrival. In the method, each node does not require to be equipped with a device similar to a compass to unify the orientation of a local coordinate system, so the ability of an algorithm to adapt to the scene is increased, and the method is also applicable in a complex scene where no compass can be equipped. Meanwhile, the method of the invention is linear, the calculation is simple and convenient, and has an exponential convergence property with a high convergence speed. The algorithm can ensure global convergence, which shows that the algorithm will not fall into the local optimum when an initial estimated value is selected randomly. Besides, the invention requires only two anchor nodes, and avoids high cost brought by the installation of a large number of GPSs.

The invention discloses a distant-relative pointer genetic algorithm-based cross section size optimization method of a steel truss structure, which is used for cross section size optimization design and calculation of the steel truss structure so that the structure conforms to the economical and safe requirements. The method comprises the steps of building a mathematical model of a structural optimization design; mapping a target function and a constraint condition to an adaptive value function so as to further randomly generate an initial population; calculating an adaptive value, and judging whether the adaptive value conforms to a convergence criterion or not; arranging a distant-relative pointer to sequentially complete genetic operator operation and immunity operator operation if the adaptive value does not conform to the convergence criterion, and further completing population substitution; repeatedly calculating the adaptive value until the convergence criterion is satisfied; and outputting an optimization result. The distant-relative pointer can be used for effectively avoiding individual repeated occurrence, meanwhile, the distant-relative pointer has the capability of generating a new mode under the condition of no damage on excellent individual, the global convergence of the algorithm is ensured, and the method is simple to operate; and the local search capability of the algorithm can be improved by vaccination on an immunity operator, individual degeneration can be prevented, and the complete effect of an adaptive technology is ensured.

The invention relates to an identification method of random structural damage based on genetic algorithm and static force measurement data. The identification method of the random structural damage based on the genetic algorithm and the static force measurement data comprises the following steps: (1) preliminarily obtaining statistical properties of random structural damage indexes; (2) defining the probability of unit damage as the random rigidity before occurrence of the damage or the probability that an elastic modulus Kai is larger than Kdi; (3) introducing damage probability indexes, determining several units of the damage probability indexes as non-damage units, and conducting corresponding adjustment on the statistical properties of the damage indexes; (5) going back to the governing equation of the initial damage identification in the step (1), and obtaining the objective function of the structural damage indexes after rearranging; and (6) solving the minimum value of the objective function in the step (5) by means of the genetic algorithm, and obtaining the statistical properties of the damage indexes. The identification method of the random structural damage based on the genetic algorithm and the static force measurement data has the advantages that multiple damage identification of different damage degrees can be carried out due to the fact that the genetic optimized algorithm has little limit on the type, the quantity and the size of the parameter.

The invention discloses a topology optimization method, system and storage medium considering boundary optimization, by determining the initial finite element structure of the topology and the type of moving boundary points according to engineering needs; according to the type of moving boundary points and the initial finite element structure of the topology to construct A topology optimization model with moving boundary points and density as variables; solving two variables in the topology optimization model at the same time to obtain the optimal topology structure of the topology and its corresponding optimal boundary conditions. Compared with the prior art, the present invention combines the boundary conditions of the structure It is also considered that in the optimization, the obtained results can not only obtain the optimal topology, but also obtain the optimal boundary conditions, which can further improve the optimization effect of the topology.

Disclosed is a population dynamics optimization method with vertical-structure nutrition chains. The population dynamics optimization method with the vertical-structure nutrition chains is a PDO-NCVS algorithm. A solution space of an optimization problem is regarded as an ecosystem which has three vertical-structure nutrition chain types of an opened nutrition chain, a closed nutrition chain and a branch nutrition chain, the ecosystem is divided into a plurality of different sub-systems, and each sub-system has a specific vertical-structure nutrition chain type. For each sub-system, a plurality of populations live in each sub-system. The populations can not be transmitted among the sub-systems, and information transfer exists in the kindred populations among the sub-systems which have the same vertical-structure nutrition chain types. The populations living in a sub-system are connected in a predator-prey circulating mode or in a resource-consumption circulating mode. Behaviors during a population acts in a sub-system are constructed into evolution operators which are used for constructing evolution strategies of the populations. The algorithm has the advantages of being strong in search capability and having global convergence, and a solution scheme is provided for the solution of a complex function optimization problem.

The invention provides a polarity searching method of a fixed-polarity Reed-Muller logic circuit. The best polarity of an FPRM logic circuit is searched by using the new binary differential evolution algorithm, compared with a polarity searching method of the FPRM logic circuit based on the genetic algorithm, the capacity of local optimum and the capacity of premature convergence are avoided, and the convergence rate and the polarity searching efficiency are improved. The method comprises the following steps that firstly, a Boolean logic circuit is read; secondly, an evolution parameter is input; thirdly, an initial population is generated randomly, wherein the polarity is encoded into a binary individual; fourthly, the improved binary stochastic mutation operation is performed; fifthly, the binomial crossover operation is performed; an FPRM expression of the target individual and the FPRM expression of a test individual of the target individual are obtained; seventhly, the fitness value of the target individual and the fitness value of the test individual of the target individual are calculated; eighthly, the greedy selection operation and an elitism selection strategy are performed; ninthly, if the current evolution algebra is smaller than the maximum evolution algebra, the fourth to eighth steps are executed in sequence, and otherwise, the best polarity is output.

The invention discloses a robust controller of a permanent magnet synchronous motor based on a fuzzy-neural network generalized inverse and a construction method thereof. The construction method of the invention comprises the following steps of: combining an internal model controller and a fuzzy-neural network generalized inverse to form a compound controlled object; serially connecting two linear transfer functions and one integrator with the fuzzy-neural network with determined parameters and weight coefficients to form the fuzzy-neural network generalized inverse, serially connecting the fuzzy-neural network generalized inverse and the compound controlled object to form a generalized pseudo-linear system, linearizing a PMSM (permanent magnet synchronous motor), and decoupling and equalizing the linearized PMSM into a second-order speed pseudo-linear subsystem and a first-order current pseudo-linear subsystem; and respectively introducing an internal-model control method in the two pseudo-linear subsystems to construct the internal model controller. The robust controller of the invention has the advantages of overcoming the dependence and local convergence of the optimal gradient method on initial values and solving the problems of randomness and probability caused by using the simple genetic algorithm, obtaining the high performance control, anti-disturbance performance andadaptability of the motor and simplifying the control difficulty, along with simple structure and high system robustness.

An information fusion and adaptive matching method in a pan-correlation sparse matrix space belongs to the technical field of artificial intelligence. Firstly, the present invention 1) constructs a pan-correlation sparse matrix information space, and realizes the fusion representation of complex heterogeneous information; 2) provides a construction method of pan-correlation sparse matrix, and designs a multi-module sparse coding; 3) in The pan-correlation sparse matrix space defines the algorithm of heterogeneous elements; 4) On this basis, a system of multi-dimensional attribute intelligent matching and service discovery is realized. Solve the problem that the existing similar methods are difficult to integrate global elements due to the lack of a unified system architecture and heterogeneous information fusion method; the application layer has too much time and resource overhead, and it is difficult to realize the mobile terminal environment application. Problem; it is difficult to guarantee global stability and convergence at the technical level.

The purpose of the present invention is to provide a full waveform inversion method and system based on non-monotonic search technology. The method includes: firstly divide the multi-scale inversion frequency into N frequency groups according to the set frequency group range; secondly, use non-monotonic search technology In combination with the initial step size, determine the velocity model of the next iteration corresponding to the jth frequency group according to the seismic observation data and the initial velocity model; then judge whether j is greater than N; if j is greater than N, then set the next iteration corresponding to the jth frequency group The velocity model of the second iteration is output as the optimal velocity model; finally, seismic imaging is performed based on the optimal velocity model. The invention combines the non-monotonic search technology with the initial step size to determine the step size. This method can not only obtain high-precision inversion results under the premise of ensuring the inversion calculation efficiency; To a certain extent, the global convergence of the inversion is guaranteed, and better inversion results are obtained.

The invention relates to a super-large-scale integrated circuit layout method considering atomization and proximity effects. The method comprises the following steps: (1) establishing an energy distribution model for the atomization and proximity effects in electron beam lithography (EBL) and describing changes of the energy distribution model; (2) optimizing a model and a function in the global layout, and determining a smooth target function; (3) introducing a new variable to convert an unconstrained minimization problem into a separable minimization problem CSMP with linear constraints; (4)determining an adjacent group ADMM algorithm and an iterative formula of the separable minimization problem; (5) solving two sub-problems in adjacent group iteration; and (6) performing convergence analysis on the ADMM algorithm of the adjacent group. The method is beneficial to reducing adverse effects caused by atomization and proximity effect and improving the layout efficiency.

The invention discloses an attitude control method of an underwater robot based on BP neural network S-plane control, establishes a dynamic model for the underwater robot, models and analyzes the disturbance forces of the umbilical cable and the manipulator, and establishes an overall The kinematics control loop and the disturbance-compensating item-based coordinated controller for disturbance-resistant control use the S-plane control based on BP neural network to ensure the global convergence of the coordinated controller for underwater robots; in the design of the dynamic control law, the underwater During the operation, the robot is subjected to the disturbance force of the manipulator and the umbilical cable, and the movement of the propeller is controlled to compensate, so as to realize the stability and precise control of the underwater robot during the operation.

A multi-dimensional system contour error estimation method based on simplified Newton method, related to the field of numerical control machining technology, aimed at the large amount of calculation, long solution time and singularity of trajectory peak in the existing contour error estimation method based on Newton extremum search algorithm etc. Compared with the classical Newton extremum search algorithm, the simplified Newton contour error estimation algorithm proposed by the applicant ensures higher accuracy and does not need to calculate derivatives. In the process of contour error estimation, it avoids singularity and reduces amount of calculation. According to the specific experimental results, the estimation accuracy and efficiency of the multi-dimensional system contour error are about 30%-50% higher than those of the traditional scheme. The traditional contour estimation method based on the Newton extremum search method is a local convergence method, and the selection of its initial value will determine the convergence and accuracy of the algorithm. The present invention guarantees the overall Convergence.

The invention provides an information fusion and adaptive coupling method of an extensively-related sparse matrix space and belongs to the technical field of artificial intelligence. The invention comprises steps of 1) establishing an extensively-related sparse matrix information space and achieving a fusion expression of complex heterogeneous information; 2) providing an establishing method of the extensively-related sparse matrix and designing multi-module sparse coding; 3) defining operation rules of heterogeneous elements in the extensively-related sparse matrix information space; and 4) accordingly achieving a multi-attribute intelligent matching and service discovery system. The problem that global elements cannot be fused because existing methods of the same kind do not have a unified system configuration and heterogeneous information fusing method and adopt classification and layered optimization is solved. The problem that the time resource cost in application level is too much and the movable end environmental application is difficult to achieve is solved. The problem that the global stability and convergence in technology level is hard to ensure is solved.

The invention relates to the technical field of underwater positioning, in particular to a single-beacon positioning method for an underwater vehicle. A single-beacon positioning method for underwater vehicles with global convergence. Underwater vehicles are equipped with hydrophones, Doppler velocimeters, depth gauges, attitude and heading reference systems, and GPS; underwater acoustic beacons broadcast periodically Underwater acoustic signal; the present invention converts the nonlinear single-beacon positioning model in a discrete state into a linear time-varying model through state augmentation; when the underwater acoustic signal is not received, the underwater navigation is obtained through the underwater vehicle's own equipment. dead reckoning based on the relative speed and attitude of the vehicle and the water; after receiving the underwater acoustic signal, the transmission time of the underwater acoustic signal is obtained through the known transmission time of the underwater acoustic signal, which is used as an observation variable, and the dead reckoning data and various The sensor observation data is used to predict and update single beacon positioning based on Kalman filtering. Under the premise that the localization model is observable, this method has global exponential convergence.

The invention discloses a multi-dimensional system contour error estimation method based on a simplified Newton method, relates to the technical field of numerical control machining and aims at solving problems that an existing contour error estimation method based on a Newton extremum search algorithm is large in calculated amount, long in solving time, singular in track peak and the like. Compared with a classical Newton extremum search algorithm, the simplified Newton contour error estimation algorithm provided by the applicant ensures high precision and does not need to calculate derivatives, singularity is avoided in the contour error estimation process, and meanwhile the calculated amount is reduced. According to the specific experimental effect, estimation precision and efficiency of a contour error of the multi-dimensional system are improved by about 30%-50% compared with those of a traditional scheme; a traditional contour estimation method based on a Newton extremum search method is a local convergence method, selection of an initial value of the contour estimation method determines convergence and accuracy of the algorithm, and global convergence is guaranteed by adopting the estimation value as an initial value of subsequent iteration.

The invention belongs to the field of robot control and nonlinear systems, and specifically relates to a method for adaptive impedance control of robots based on biologically inspired neural networks, aiming to solve the problem that the existing technology cannot realize real-time precise control of robots in complex nonlinear systems . The present invention includes: acquiring the initial control torque, expected impedance, and motion trajectory of the system; constructing the dynamic equation and expected impedance model of an n-degree-of-freedom manipulator system containing impedance to obtain the real state and expected state of the system at time t of the robot; based on full state feedback Build an adaptive controller with the bio-inspired network and obtain the control torque at time t+1; cycle through state acquisition, adaptive impedance control, and motion control until the robot arm completes the motion trajectory. The invention combines the biological heuristic network structure and time-delay feedback, adopts the sea-bian algorithm for adjusting the reward value, and the structure combining network estimation and full state feedback, so the system is stable and the control precision is high.