137 results about "Barrier function" patented technology

The invention relates to a micro-grid intelligent safety secondary voltage control method based on reinforcement learning, and belongs to the technical field of power grids. The method comprises the following steps: firstly, establishing a micro-grid secondary control model under hybrid network attack; a DoS attack defense network based on deep learning is designed, a self-adaptive FDI attack compensation state observer is designed, compensation for unknown FDI attacks is established, a self-adaptive law of compensation signals is designed, and estimation of the system state under network attacks is achieved. An evaluation-execution structure is constructed based on reinforcement learning, a virtual optimal controller and an actual optimal controller are designed in combination with a backstepping method, unknown parts in a system model, an evaluation network and an execution network are approximated by using a neural network, and a weight adjustment law is given. And introducing a log type barrier function into the performance index function, and constraining the output in a fixed constant interval to meet the constraint condition. The method can cope with DoS attacks and FDI attacks, and ensures stable control and safe operation of the secondary voltage of the micro-grid.

The invention provides a dynamic obstacle avoidance path planning control method for a redundant mechanical arm, which adopts a self-adaptive artificial potential field dynamic obstacle avoidance algorithm based on a second-order dynamic control barrier function. The problems that a traditional artificial potential field method cannot reach a target, is prone to falling into a local minimum value and cannot effectively deal with a dynamic obstacle are solved. The efficient and safe obstacle avoidance of the mechanical arm in a dynamic complex environment is realized by constructing a self-adaptive velocity repulsion field and a virtual obstacle model, combining a high-order dynamic control barrier function as a safety constraint and utilizing a quadratic programming optimization algorithm. Experimental results show that the method can effectively guide the mechanical arm to escape from the local minimum point, the target point can be reached, meanwhile, the safe distance between the mechanical arm and a dynamic obstacle is guaranteed, the movement efficiency and track quality of the mechanical arm are remarkably improved, and the real-time performance and safety requirements of a dynamic obstacle avoidance task are met.

The invention provides a high-speed magnetic levitation suspension system control method and system based on edge calculation and Transform prediction, and the method comprises the steps: constructing a Transform prediction model with a space-time attention mechanism and an autoregression mechanism based on obtained local low-delay calculation resources and train real-time sensing data, and deploying the Transform prediction model in a vehicle-mounted edge calculation unit; performing short-term high-precision prediction on the gap, the acceleration and the disturbance trend at a plurality of sampling moments in the future through a Transform prediction model to obtain a prediction result; processing actuator current saturation and gap safety threshold hard constraints in a limited prediction domain by using a model prediction controller, and solving an optimization control sequence in real time in combination with a prediction result; and overlapping a control barrier function as a safety filter of the model prediction controller, correcting the optimized control sequence to obtain an optimal control sequence, and controlling the high-speed magnetic suspension system. According to the method, the cloud communication delay and jitter are reduced, and the robustness and security of the system under uncertain disturbance are improved.

The invention provides a mechanical arm predefined time trajectory tracking control method and system based on time delay estimation, and relates to the technical field of multi-degree-of-freedom mechanical arm trajectory tracking control. Constructing a predefined time error conversion function to define a system error and conversion error system, designing a barrier function and a feedback control rate based on the conversion error system, and designing a Lyapunov function to prove the stability of the system so as to track an expected trajectory; according to the method, time delay information is deeply fused, dependence on a complex model approximate structure is not needed, stable convergence of the mechanical arm system state within predefined time can be achieved when a mathematical model is not accurate, the system control performance and robustness are effectively improved, and the requirement of modern industry for mechanical arm high-performance control is met.

The invention discloses a coating machine unwinding tension control method which is characterized by comprising the steps that S1, parameters, operation data and tension data of a coating machine are collected, and a state-space equation of an unwinding tension control system is established; s2, constructing an obstacle function; s3, combining the obstacle function and constructing a non-singular sliding mode surface function; s4, solving an equivalent controller for controlling tension according to the non-singular sliding mode surface function; s5, optimizing key parameters of the equivalent controller by using an aurora optimization algorithm; s6, designing a self-adaptive control rate for improving the anti-interference capability of the unwinding tension control system; the adaptive control rate is combined with the equivalent controller, and a total control signal is output; s7, performing unwinding tension control on an unwinding tension control system according to the master control signal; according to the scheme, the speed of dynamic response can be adjusted, finite time convergence is enhanced, steady-state errors are eliminated, singularity is avoided, convergence is accelerated, and therefore high-precision control over an unwinding tension control system is achieved.

The invention discloses a ring main unit intelligent regulation and control method based on deep reinforcement learning, and the method comprises the following steps: building an electrical topological graph model through collecting the real-time operation data of a ring main unit and a power distribution network, extracting the characteristics of nodes and branches, and generating an initial state vector; a comparison strategy embedding layer is arranged in front of the strategy network, and state-action representation is optimized through comparison learning; constructing a reinforcement learning decision structure by adopting a trust region strategy optimization algorithm, and outputting a ring main unit control action sequence; before execution, an out-of-limit action is judged and corrected by a control barrier function convoy layer; and closing and opening control is completed according to the corrected action, rewards are recorded, and incremental learning and parameter fine adjustment are performed by using an experience playback buffer area. According to the invention, by fusing the graph neural network and deep reinforcement learning and introducing a comparison strategy embedding layer and a control barrier function escorting layer, safe, adaptive and efficient intelligent regulation and control of the ring main unit in a complex power distribution network are realized.

The invention provides a four-rotor unmanned aerial vehicle optimal attitude synchronous control method based on reinforcement learning. The method comprises the following steps: firstly, establishing a kinetic model based on an Euler angle, and converting a constraint control problem into an unconstrained problem by constructing a global consistent barrier function; and then, designing a distributed observer by utilizing the state of an adjacent machine so as to estimate the state of a leading machine, and converting a complex attitude synchronization task into a trajectory tracking task. Thirdly, combining the estimated pilot state with the four-rotor state, constructing an augmentation equation, designing a performance index function, and obtaining optimal control input through iteration of a Hamilton equation and a data-driven reinforcement learning algorithm; and finally, adopting a neural network to approximate an optimal performance index function and control input, updating a neural network weight by using a least square method, and performing tracking control on the unmanned aerial vehicle through an updating strategy. According to the method, safe flight of the quad-rotor unmanned aerial vehicle under different state constraints is ensured, and tracking failure caused by difficult collection of dynamic information is avoided.

The invention relates to the technical field of control and information, and particularly discloses a distributed optimization control method for a security constraint uncertain multi-agent system, which comprises the following steps of: constructing a topological graph according to a network structure of the multi-agent system, and determining an adjacent matrix of the topological graph; determining a state equation; determining a target function needing to be optimized and inequality constraints needing to be met; according to the state equation, the target function needing to be optimized and the inequality constraint needing to be met, based on a Lyapunov function control method, an optimal solution search condition is established, and based on a barrier function control method, a system safety maintenance condition is established; determining an input constraint condition according to the multi-agent system; calculating by using a quadratic programming method to obtain optimal control input; and controlling the multi-agent system according to the optimal control input. According to the method, the long-term reliability of the system can be remarkably improved, the communication and calculation overhead is remarkably reduced, and the calculation efficiency is remarkably improved.

The invention discloses a hybrid robot adaptive motion control method and system, and relates to the technical field of robot motion control, and the method comprises the steps: obtaining the initial state of a robot system; establishing a nominal dynamics prediction model, and defining a safety set and a control barrier function set; statistical characteristics of model prediction residuals are obtained through online learning; correcting the dynamic model and generating a feed-forward compensation amount, and adaptively adjusting a safety set and a barrier function according to uncertainty; constructing a rolling optimization problem integrating the closed chain constraint and the barrier function hard constraint; solving the optimization problem to obtain an expected control quantity, and adjusting a safety parameter to obtain a rollback control quantity when solving fails; and synthesizing the feed-forward compensation quantity and the control quantity to generate a driving instruction. According to the method, the problem of model mismatching is solved through online learning and residual compensation, safety self-adaption is achieved through uncertainty, performance and safety are considered through a unified optimization framework, and the adaptability, precision and robustness of the system under dynamic disturbance are remarkably improved.

The invention relates to a practical fixed time control method for a state constraint nonlinear system. The method comprises the following steps: step 1) establishing a dynamic equation and state constraint of a pure feedback constraint nonlinear system; 2) aiming at the state constraint, constructing an improved general barrier function; 3) based on the obstacle function, converting the constrained nonlinear system into an unconstrained system; 4) performing approximation to obtain an approximation form; 5) obtaining a coordinate transformation result; step 6) based on error dynamics, constructing an adaptive fixed time tracking control scheme and a parameter updating law control system; and 7) proving the stability of the system. Compared with the prior art, the method has the advantages of overcoming singularity in fixed time control, reducing calculation burden and the like.

The invention relates to the technical field of dynamic system obstacle avoidance, in particular to a distributed multi-agent safety control method based on a non-smooth graph barrier function. Comprising the following steps: defining a node set, and constructing a communication graph topology and a neighbor interaction relationship according to the node set; boundary conditions required for constructing a non-smooth graph barrier function are given, and the non-smooth graph barrier function is constructed according to the boundary conditions; formalizing a multi-agent system kinetic equation with uncertain parameters according to a non-smooth graph barrier function; according to the multi-agent system kinetic equation, designing a reference model and an adaptive law, and constructing a stable reference trajectory; constructing and improving a filter according to the reference trajectory; and inputting the motion data of the unmanned aerial vehicle group to a filter to obtain a motion control result of the unmanned aerial vehicle. According to the method, an absolute continuity modeling tool is introduced, description of non-smooth safety areas such as polygonal obstacles is directly supported, and the dependence of a traditional method on a differentiable boundary is broken through.

The invention discloses a predefined space-time pitch angle control method of a variable-speed wind generating set, and belongs to the technical field of variable-speed wind generating set control. The method is characterized by comprising the following steps of 1, obtaining a dynamic model of the variable-speed wind turbine according to the Betz theory; step 2, obtaining a predefined space-time reaching law with a buffer area according to the improved barrier function; 3, constructing a sliding mode variable, and converting a non-affine model of the variable speed wind turbine into an affine model based on an unknown smooth nonlinear function; and 4, compensating the unknown smooth nonlinear function by using a neural network, and obtaining a pitch angle controller according to the neural network. According to the predefined space-time pitch angle control method of the variable-speed wind generating set, the state space is divided into the multiple parts including the buffer areas, dependence on model parameters is reduced, the buffeting phenomenon is weakened through the improved obstacle function, and then the power generation efficiency is improved, and the operation cost is reduced.

The invention discloses an intelligent anti-interference control method and device for full-state dynamic constraint of a nonlinear servo system, and the method specifically comprises the steps: firstly constructing a nonlinear obstacle function, and converting a state constraint problem of the servo system into a stability control problem; then designing a multi-layer feedforward neural network and an uncertain observer, and estimating endogenous disturbance and external disturbance in the system; an intelligent anti-interference controller is designed, and feedforward compensation is carried out on internal source disturbance and external disturbance in the system; and finally, designing a neural network weight adaptive law, and selecting controller design parameters to realize a predetermined control target. According to the method, the problems of parameter uncertainty, structural uncertainty, unmodeled dynamics and external disturbance uncertainty existing in the nonlinear servo system can be solved at the same time, it is ensured that the full state of the system meets the preset dynamic constraint requirement, and the influence of differential explosion in the design process of a high-order nonlinear servo system controller is avoided; and large-scale application in industry and engineering is facilitated.

The invention relates to the technical field of power system communication, and particularly discloses an optimization method for an operation and maintenance strategy of a power communication network, which performs space-time alignment and feature reconstruction on multi-source heterogeneous telemetry data of the power communication network by using a sliding window and a graph neural network, and constructs a state tensor reflecting network physical and logic panorama. Generating a candidate strategy set through a deep reinforcement learning action network, and mapping the candidate strategy set to a digital twin environment synchronized with the current network in real time for parallel simulation rehearsal; a dynamic scoring mechanism including network pressure sensing and marginal utility analysis is introduced, fusing is carried out on a high-risk strategy in combination with a logarithmic barrier function, and an optimal strategy giving consideration to performance and safety is selected from a candidate set. And finally, a final execution instruction is generated through verification of a power service hard rule filter, the executed real network is fed back and stored in an experience playback buffer area to update model parameters, and the problems that a traditional static strategy cannot dynamically adapt to the network pressure and the direct trial and error risk is high are solved.

The invention relates to the technical field of multi-agent control, in particular to a method and a system for realizing preset performance to control multiple agents based on a barrier function. The non-linear state observer is designed to estimate the unmeasurable state in the system in real time, the dependence of a traditional control strategy on full-state information is broken through, and the adaptability and robustness of the system are improved. Besides, the inclusion error is constrained through the obstacle function, and the inclusion error is limited in a limit range, so that a preset performance constraint problem is converted into a bounded problem of the obstacle function. Explicit constraint is carried out on convergence time and range including errors through a preset performance function, and the dual requirements for rapidity and precision in an actual task are met. And a filter is introduced to replace a high-order virtual controller for derivation, so that the design complexity of the controller is effectively reduced, and the engineering implementability of the algorithm is improved.

The invention discloses a self-adaptive neural network distributed control system and method for a mechanical arm, and belongs to the technical field of automatic control. Comprising a multi-mechanical-arm system which has position and attitude constraints and is provided with a sensor, a three-dimensional rigid body dynamic model for receiving sensor data from the multi-mechanical-arm system, and a universal time-varying asymmetric barrier function integrated with the three-dimensional rigid body dynamic model, the adaptive neural network is used for estimating unknown kinetic parameters in the system online; and the distributed cooperative tracking controller is designed based on estimation of the adaptive neural network. According to the method, a new universal time-varying asymmetric barrier function of an output constraint multi-mechanical-arm system containing unknown dynamics is constructed, and an online learning module based on a feedforward neural network is established, so that the problem of asymmetric time-varying output constraint of insensitive parameter online estimation is solved; the dynamic estimation problem insensitive to an initial value and limited by asymmetric time-varying output can be solved at the same time.

The invention discloses a crane position control method and system based on MPC and a control obstacle function. The method comprises the steps that S101, all to-be-controlled parameter values are calculated according to given positions; s102, solving an optimal control sequence u at the moment according to a predictive control algorithm; s103, designing a state observer according to the output y, the input u and the state at the moment, observing the state at the next moment, designing by adopting a full-dimensional linear state observer method, and adjusting parameters by adopting a DLQR method to obtain state information at the next moment for the step S102 at the next moment; and S104, the output of the real system is measured and sent to the step S103 at the next moment, and the predictive control algorithm is a model predictive control algorithm fused with the control barrier function. According to the crane position control method and system based on the MPC and the control barrier function provided by the invention, the model prediction control is combined with the control barrier function, so that the crane can reach a specified position.

The invention relates to the technical field of automobile engineering, and discloses a vehicle suspension control method and system based on a control barrier function and a storage medium, and the method comprises the following steps: obtaining suspension control data, and determining a state quantity and a control quantity according to the suspension control data; constructing a vehicle seven-degree-of-freedom mathematical model based on the state quantity; according to a control barrier function generation algorithm, a control barrier function is generated in combination with the vehicle seven-degree-of-freedom mathematical model; generating a reward function by using a reward generation algorithm according to the suspension control data; obtaining a control strategy by using a reinforcement learning algorithm based on the reward function; inputting the control strategy into the control barrier function, and outputting the latest control strategy after minimum intrusion security constraint; in order to avoid control behaviors affecting system safety in the reinforcement learning training process, the safety reinforcement learning method based on the control barrier function is used for controlling the vehicle active suspension, and the purpose of controlling the stability of the vehicle suspension is achieved.

The invention discloses an Internet of Vehicles cooperative driving control method and system based on deep reinforcement learning, and belongs to the field of automatic drive.The method comprises the steps that state data of a main vehicle and state data of at least one adjacent vehicle received through Internet of Vehicles communication are received, generating smooth state estimation and uncertainty characteristics used for representing data credibility; constructing the main vehicle and the adjacent vehicles into a dynamic vehicle interaction diagram; inputting the dynamic vehicle interaction graph into a graph attention feature extraction model to extract space-time interaction features; inputting the space-time interaction characteristics into a deep reinforcement learning strategy network to generate a preliminary reference control instruction; a quadratic programming problem is constructed, and the quadratic programming problem takes minimization of deviation from a reference control instruction as an optimization target and takes security constraints derived from a control barrier function as constraint conditions; and solving the quadratic programming problem to obtain a final control instruction. According to the invention, efficient and intelligent cooperative driving can be realized on the premise of ensuring safety.

The invention discloses a mechanical arm dynamic constraint control method and system oriented to model uncertainty compensation, and the method specifically comprises the steps: building a mathematical model of a mechanical arm system, and introducing a barrier function to convert a state constraint control problem of the system into a stability control problem; designing a neural network estimator based on the multilayer feedforward neural network, and estimating matching and non-matching endogenous disturbances suffered by the system; designing a disturbance observer based on a neural network estimator, and estimating other matching and non-matching disturbances of the system; designing a mechanical arm dynamic constraint intelligent controller and a neural network adaptive law; and selecting an initial value of a neural network weight parameter, the adaptive law matrix and a controller parameter, and carrying out dynamic constraint control on the mechanical arm. According to the method, the uncertainty of the model can be effectively compensated, meanwhile, high-precision trajectory tracking is achieved, it is strictly guaranteed that the system state does not exceed the dynamic constraint range, and the control precision, adaptability and reliability of the mechanical arm system under the complex working condition are improved.

The invention provides a method and device suitable for safe motion planning of a quadruped robot in a complex environment, and the method comprises the steps: sensing the environment through a visual sensor and a laser radar, and constructing an environment sensing and spatial modeling system; environment point cloud information collected by the laser radar is sampled and clustered; parameterizing the enveloping boundary of the point cloud obstacle into an elliptic model, wherein the long axis direction of the elliptic model is determined according to the maximum extension dimension of the obstacle; performing motion prediction and trajectory planning, establishing a target optimization function of the model prediction function, and adding a control barrier function constraint condition; introducing an expected value of a CBF attenuation rate into a cost function of model prediction control; and solving an optimization problem of model prediction control, and sending an optimal solution of the optimization problem as control input to a motion control module of the quadruped robot to realize navigation of the quadruped robot. According to the method, the robustness and scene generalization ability of the quadruped robot in a complex dynamic environment are improved.

This invention relates to the fields of control and information technology, specifically disclosing a distributed optimization control method for multi-agent systems with uncertain security constraints. The method includes the following steps: constructing a topology graph based on the network structure of the multi-agent system and determining the adjacency matrix of the topology graph; determining the state equation; determining the objective function to be optimized and the inequality constraints to be satisfied; establishing optimal solution search conditions based on the control Lyapunov function method and the control barrier function method based on the state equation, the objective function to be optimized, and the inequality constraints to be satisfied; determining the system security maintenance conditions based on the multi-agent system; calculating the optimal control input using a quadratic programming method; and controlling the multi-agent system based on the optimal control input. This invention can significantly improve the long-term reliability of the system and significantly reduce communication and computational overhead, resulting in a significant improvement in computational efficiency.

The present application relates to the technical field of smart water affairs and urban public safety, and particularly relates to a kind of artificial intelligence driven urban pipe network flood resilience evaluation method and system, comprising: constructing Riemann metric tensor by using metric matrix containing potential barrier function, and establishing the Riemann manifold embedding space of pipe network physical state;Then, based on fluid energy, a Hamilton function is constructed, and a dynamics prediction model with physical conservation is obtained by using symplectic neural network and symplectic discrete integral format training;Subsequently, a second-order Hamilton-Jacobi-Aleksandrov partial differential equation describing stochastic differential game is constructed, and a physical perception neural network is used to solve and extract the resilience safety boundary;Finally, real-time data is mapped to the manifold space, the Riemann gradient is calculated, and a quadratic programming problem is solved to generate control instructions. The problem of lack of physical constraints and safety bottom line in pipe network control under extreme random working conditions is solved, and real-time closed-loop control with physical consistency is realized.

This invention relates to the field of robot motion control technology, and more particularly to an obstacle avoidance adaptive dynamic control system, comprising a multimodal environment and physiological temporal perception module with communication connections, a photodynamic Riemannian metric tensor mapping module, a neural constant differential cognitive evolution prediction module, a robust forward invariant tube synthesis module, a Riemannian space barrier constraint solving module, and a differential manifold impedance execution module. The perception module extracts circadian rhythm representation data; the mapping module reconstructs Euclidean space into a Riemannian manifold space based on the photodynamic cost scalar field; the neural constant differential module calculates the cognitive delay evolution trajectory by combining multi-source features; the synthesis module synthesizes a robust forward invariant tube for envelope prediction error; a Riemannian control barrier function constraint proposition is constructed by combining geodesic distance; and the execution module solves for the optimal safe control vector and inversely maps it to impedance torque distribution. This invention achieves deep coupling between physiological rhythms and non-Euclidean geometric control, constructing a physically compliant obstacle avoidance safety defense line.

This invention discloses a safe navigation and localization method for bipedal robots based on discrete control barrier functions and a hierarchical architecture, belonging to the field of robot control technology. The method employs the Relative Dynamics Test (RRT) algorithm to generate a global path and combines it with a Linear Inverted Pendulum Model (LIPM) to generate a stable gait. A discrete control barrier function (D-CBF) is used to ensure obstacle avoidance in dynamic environments, and the safe control input is solved through an optimization problem. Sensor fusion technology is used for real-time localization, and moving average filtering is applied to eliminate oscillation errors caused by gait. This invention, by combining discrete control barrier functions (D-CBF) with hybrid zero dynamics (HZD), achieves high-precision navigation and safe obstacle avoidance for bipedal robots in complex environments, effectively solving the problems of insufficient stability and lack of safety in traditional methods.

The invention provides a power distribution network reactive voltage optimization method based on mathematical analysis, and relates to the technical field of power distribution network control, and the method comprises the steps: S1, building a control model of a distributed active power distribution network reactive power compensation device according to a physical model and control characteristics of the distributed active power distribution network reactive power compensation device; s2, establishing a multi-optimization objective function which takes the minimum network loss as a core and takes voltage quality into account, and establishing operation constraint conditions; s3, based on the obstacle function conversion constraint and gradient / Hessian matrix optimization, designing a solving algorithm model; and S4, integrating the control model, the multi-objective optimization function and the operation constraint condition into an active power distribution network reactive voltage coordination control optimization model, calling a solution algorithm model for solution, and outputting an optimization control scheme of the reactive power compensation device. According to the scheme, the problems that a traditional distribution network optimization model does not fully fuse the characteristics of the distributed active reactive power compensation device, and a multi-dimensional target and complete constraint are not designed for the active power distribution network can be solved.

The present disclosure proposes an actuator failure compensation and safety control method based on a double-layer evaluation architecture, a storage medium and a system. The bottom layer uses an adaptive model predictive control to output a nominal instruction, a reinforcement learning model is built thereon to output a high-frequency residual compensation instruction to eliminate model mismatch, and a second evaluation network is independently built to evaluate the future global loss-of-control risk of the vehicle. The Lagrange dual optimization is introduced to convert the macro risk evaluation into a mathematical constraint to guide the first strategy network correction; the global risk index is used to feed forward the target cost weight of the predictive control to realize continuous flexible degradation, and the control barrier function is triggered at the limit boundary to realize discrete quadratic programming instruction projection. The present application overcomes the conservative strategy degradation defect caused by the traditional mixed reward, establishes an absolute safety line, and improves the control accuracy and safety robustness of the vehicle in complex nonlinear working conditions.

The application discloses a heavy vehicle anti-rollover safety control law design method based on a control barrier function, utilizes Newton's second law of motion and the law of conservation of angular momentum to analyze a vehicle lateral-yaw subsystem and a roll subsystem respectively, and constructs a three-degree-of-freedom state space model; the traditional lateral load transfer rate LTR for measuring vehicle rollover is re-stated by using state variables in the three-degree-of-freedom state space model, and a vehicle anti-rollover safety certificate and a barrier function are constructed; a control Lyapunov function and a control barrier function are constructed for the three-degree-of-freedom state space model, and a quadratic optimization framework is used to solve the anti-rollover control law which meets the requirements of heavy vehicle driving control tasks and the non-rollover safety constraint condition. The application does not depend on a large number of sensors and complex calculation algorithms, strives to achieve a more efficient control effect with fewer resources, and reduces the dependence on expensive hardware, so that a balance between cost and performance is found.

A nonlinear optimal control method is provided. The nonlinear optimal control method comprises performing a policy iteration algorithm using a Lyapunov barrier function made by applying a barrier function to a control Lyapunov function and Sontag's formula.

The invention provides a risk-adaptive safe navigation method and device, a terminal and a storage medium, and belongs to the technical field of robot control, and the method comprises the following steps: generating an environment state vector at a current moment; obtaining a risk assessment value; adjusting a security constraint area of the control barrier function and weight distribution of a risk weighted cost function in the model prediction control module; performing optimization solution based on the adjusted risk weighted cost function and the safety constraint formed by the adjusted safety constraint region by using a model prediction control module to obtain a target control instruction meeting the safety constraint and the optimal cost; the robot is driven to move. According to the method, the environment risk is dynamically modeled, the weights of the safety constraint condition and the risk cost function are adaptively adjusted, the adjusted safety constraint condition and the risk cost function are introduced into the model prediction control module to generate the target control instruction, the method can dynamically adapt to the change of the environment, and then the accuracy of navigation is ensured.