475 results about "Backstepping" 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 self-adaptive switching control method for the unmanned surface vehicle comprises the following steps: establishing a mathematical model of the unmanned surface vehicle, and determining a control target of the unmanned surface vehicle; designing a virtual control law and a first Lyapunov alternative function for controlling the unmanned surface vehicle based on a backstepping method; designing a second Lyapunov alternative function, establishing an adaptive neural network controller model and a robust controller model, and designing control input of the unmanned surface vehicle switching control system; designing updating laws of an adaptive neural network controller and a robust controller, and designing a third Lyapunov function to perform stability analysis on the system; and testing and simulating the unmanned surface vehicle system in a simulation environment by using the adjusted control strategy. According to the control method, on the premise of ensuring the stability of the system, the path tracking error of the unmanned surface vehicle is reduced, and the energy consumption of the controller is reduced.

The invention provides a fixed-time optimal control method and system of a single-connecting-rod mechanical arm, and relates to the technical field of optimal control of the single-connecting-rod mechanical arm. The fixed-time optimal control method comprises the steps that a kinetic model of the single-connecting-rod mechanical arm to be controlled is obtained, and the model is converted into a state model by introducing a state variable; according to the state model, an active-disturbance-rejection control strategy is adopted, and an expansion state observer is established and used for observing disturbance state information of the single-connecting-rod mechanical arm; based on the state model and the extended state observer, a backstepping method technology and a reinforcement learning algorithm are combined, an optimal controller for the single-connecting-rod robot mechanical arm is designed, and self-adaptive optimal backstepping control is conducted on the single-connecting-rod robot mechanical arm; the active disturbance rejection control technology and the fixed time optimal control are combined, a new control strategy and an optimization path are developed, and the control performance and the stability of the mechanical arm system are further improved.

The invention discloses a six-rotor unmanned aerial vehicle control method and system with disturbance and performance constraints. The method comprises the steps that 1, a six-rotor unmanned aerial vehicle power model is built; 2, errors are determined based on the dynamic model, functions are combined, and a finite time scheme is determined; step 3, based on the dynamic model, establishing an observer, and knowing a disturbance state; step 4, a performance control scheme and an unknown disturbance state are stipulated in finite time, an error dynamic model is established, and the error dynamic model is established based on reinforcement learning; 5, setting a base scheme, setting a virtual controller and an actual controller, and setting an adaptive law for the base neural network; 6, the controller follower can track the leader, and tracking control is completed; establishing a controller by adopting a backstepping method, establishing a disturbance observer, and compensating negative effects; a reinforcement learning algorithm and a backstepping method technology are combined, and a controller is arranged to realize control; according to the method, the stability is proved by using a Lyapunov judgment stability theory, and the feasible effectiveness is verified by a simulation example.

The invention discloses a safety cooperative control method of a multi-robot system, and relates to the technical field of robot control. The method comprises the following steps: constructing a multi-mechanical-arm multi-robot system model; setting an end effector of the follower robot in a Cartesian space, and determining a dynamic model of the multi-mechanical-arm multi-robot system; according to the input-driven fuzzy state observer and the kinetic model of the multi-mechanical-arm multi-robot system, a distributed fuzzy observer error is obtained; according to the symbol graph network topology, constructing a bidirectional grouping cooperation error, a bidirectional grouping tracking error and a fuzzy estimation error; a backstepping method is applied to obtain a time-varying Tan type Lyapunov obstacle function; and then determining a distributed formation controller and an adaptive law, and determining a distributed bidirectional cooperative controller of the multi-mechanical-arm multi-robot system. The operation safety of the multi-mechanical-arm multi-robot system in a specific task space range is improved.

The invention relates to the technical field of injection molding pressure control, in particular to a multi-stage pressure closed-loop compensation system and method for high-precision double-color injection molding, and the system comprises an interface gradient positioning module, a flow resistance state recognition module, a fitting function reconstruction module, a time sequence deviation triggering module and a gain reverse correction module. According to the method, the high-density measuring point data is collected, the interface radial pressure gradient change is extracted through the finite difference method, the stably-arranged nodes at the material intersection can be accurately locked, and the flow resistance state of the flow channel is judged according to the slope difference value of the screw speed and the pressure intensity change. A linear boosting or index slow increasing strategy is automatically switched to reconstruct a pressure function form, local high-pressure accumulation characteristics of a product sensitive area are accurately matched, the coincidence degree of a time window drift trend and a slope sudden change window is monitored, historical displacement and pressure errors are converted into backstepping factors through an iterative learning model, and the backstepping factors are calculated. And a driving gain curve is initialized and revised, so that forming deviation caused by thermal drift of equipment is eliminated.

The invention discloses an aircraft self-learning optimization control method based on adaptive dynamic programming, and the method comprises the steps: firstly, designing a basic stability controller under a backstepping method frame in order to guarantee the stable control of an aircraft under the uncertain and unknown external disturbance of a model; then, considering that the tracking performance of the system has a weakening trend along with disturbance change, introducing an ADP-based parameter self-adjustment intelligent optimization algorithm to design an auxiliary controller, and obtaining a compensation control signal by executing and evaluating updating adjustment of a network weight along with tracking deviation so as to further reduce the tracking error; and the self-learning and self-adaptive capabilities of the controller to environmental changes are realized. And finally, integrating the basic controller and the auxiliary optimization controller to finally obtain a complete robust self-learning optimization controller. The method is high in universality and high in calculation precision, and has important engineering practice reference significance.

The invention discloses an unmanned helicopter control method based on a fuzzy neural network extended state observer, and belongs to the technical field of transverse unmanned helicopter flight control, and the structure of a transverse unmanned helicopter flight controller comprises an FNNESO-RBSC controller, a control strategy and a transverse unmanned helicopter flight dynamics model; the FNNESO-RBSC controller calculates transverse, longitudinal, course and vertical channel control quantities according to system input and output information, the control quantities are distributed to all control surfaces of the transverse unmanned helicopter through a control strategy and act on a controlled object of the transverse unmanned helicopter, object state response of the transverse unmanned helicopter is changed under the action of aerodynamic force, and the control surfaces of the transverse unmanned helicopter are controlled. And the target flight state is controlled. According to the method, the fuzzy neural network is adopted to transform the extended state observer, and the adaptability of the extended state observer is improved, so that the unknown total disturbance needing to be compensated in the backstepping control law is more effectively obtained.

The invention relates to a non-linear multi-unmanned vehicle system self-triggering time-varying formation control method, which comprises the following steps: constructing a second-order dynamic model for a non-linear multi-unmanned vehicle system, and describing communication between unmanned systems through a directed topological structure; a backstepping method is adopted, a virtual controller is introduced, and a leader-follower formation error system under the triggering condition is constructed; designing influence weights of communication resources and control performance, and respectively constructing an optimization model based on virtual input and formation distributed position errors and an optimization model based on real input and tracking formation speed errors; solving an approximate solution of a corresponding Hamiltonian-Jacobi-Bellman equation through a simplified reinforcement learning method, and sequentially obtaining an optimal virtual control protocol and an optimal reality control protocol; similarly, determining a self-triggering prediction condition; and finally, under the condition of self-triggering prediction, the multi-unmanned vehicle system not only realizes time-varying formation, but also balances communication resources and control performance.

The invention belongs to the technical field of unmanned aerial vehicle trajectory tracking control, and particularly discloses an unmanned aerial vehicle preset performance control method and system considering time-varying position constraints. According to the method, under the condition that the unmanned aerial vehicle has time-varying position constraint, model uncertainty, actuator fault and wind disturbance, a nonlinear mapping method is firstly adopted to convert a system with position constraint into an unconstrained system, then a preset performance control method and a self-adaptive backstepping technology are adopted to carry out controller design on the converted system, and therefore the control precision of the unmanned aerial vehicle is improved. The system error is kept in a preset boundary function range; the invention also designs a fuzzy logic system (FLS) and a nonlinear disturbance observer to estimate the uncertainty and wind disturbance of the model, thereby providing a trajectory tracking control scheme combining model mapping and preset performance. Under the control of the method, the quad-rotor unmanned aerial vehicle has good robustness, and even if an actuator breaks down, the tracking error can still be kept within a preset interval.

The invention discloses a hydraulic mechanical arm motion control method based on low-pressure volume elastic modulus compensation. The method comprises the steps that a dynamic linearization model of the multi-degree-of-freedom hydraulic mechanical arm containing the effective volume elastic modulus is established, a backstepping method is used for designing a self-adaptive robust controller, an expected joint angle is input into the self-adaptive robust controller, meanwhile, online self-adaptive parameter updating is carried out, and the optimal joint angle is obtained. The self-adaptive robust controller outputs virtual control flow of the multi-degree-of-freedom hydraulic mechanical arm after processing, and valve port control voltage is obtained after static mapping so as to control a hydraulic valve; and the multi-degree-of-freedom hydraulic mechanical arm outputs the actual control flow, the actual joint control torque and the actual joint angle to the self-adaptive robust controller in real time, and motion closed-loop control over the multi-degree-of-freedom hydraulic mechanical arm is achieved. According to the method, the high-precision motion tracking performance can be achieved under the condition that the hydraulic mechanical arm keeps stable low pressure.

The invention discloses a grid-connected inverter sliding mode control method based on a reduced-order generalized proportional-integral observer, and the method is applied to closed-loop control of a grid-connected inverter. The specific implementation method comprises the steps that a given power reference value is subjected to PQ control to obtain a current reference value, and the form of a reduced-order generalized proportional-integral observer is designed according to a state-space equation of the grid-connected inverter and used for observing the output current value and the total disturbance of the system. A backstepping method is combined with a super-spiral sliding mode to serve as a controller of a current loop, a control law output by the controller is subjected to an SPWM modulation method to obtain a trigger pulse signal of a switch tube, and closed-loop control of the grid-connected inverter is formed. According to the control method, the change of external disturbance can be more effectively resisted, the coupling influence between the d axis and the q axis is reduced, higher robustness and adaptivity to load disturbance are achieved, and stable operation of the grid-connected inverter is facilitated.

The invention discloses a reinforcement learning-based multi-unmanned ship formation hunting control method, which comprises the following steps of: constructing a multi-unmanned ship motion system influenced by environmental interference, and constructing a hunting formation error variable; constructing a virtual controller of the unmanned ship by adopting a backstepping method based on the encircling formation error variable; constructing a leader ship formation hunting controller based on a reinforcement learning method in combination with a neural network; constructing a following ship formation hunting controller based on an adaptive backstepping method in combination with the Lyapunov theory and a neural network; and according to the leading ship formation hunting controller and the following ship formation hunting controller, formation hunting control of the target ship is realized. The problem that the control performance of the multiple unmanned ships is insufficient due to the interference of complex marine environments such as wind, waves and flow in the process of efficiently executing a hunting task in the formation of the multiple unmanned ships, the cooperative relationship between the chasing unmanned ships cannot be fully reflected, the formation of the multiple unmanned ships is difficult to control, and the hunting efficiency of the multiple unmanned ships is improved is solved. And the control is difficult to realize at the cost as low as possible.

The invention discloses a non-linear multi-agent system output feedback consistency control method, and belongs to the technical field of non-linear multi-agent system control. The method comprises the following steps: acquiring a multi-agent system comprising a leader and a plurality of followers; constructing a state observer according to the estimated value of the state variable of the follower; acquiring an auxiliary function and a dynamic surface with a nonlinear filter; on the basis of the state observer, the auxiliary function and the dynamic surface with the nonlinear filter, constructing and updating an actual controller of the multi-agent system by using a backstepping method; wherein the event triggering technology is adopted in the construction process of the actual controller and is used for controlling the updating frequency of the actual controller. According to the invention, the preset precision of the tracking error is set through the auxiliary function; eliminating boundary layer errors in each step of the backstepping method through a dynamic surface with a nonlinear filter; an unmeasurable state is observed through a state observer; the actual controller updating frequency is reduced through the event triggering technology, and resource consumption is reduced.

A system, computer readable storage medium and method for controlling a trajectory of coordinated time-varying maneuvers of a geometric formation of unmanned vehicles is disclosed. The system includes unmanned vehicles, each configured with communication circuitry to communicate between the vehicles. A subset of the unmanned vehicles function as leader vehicles, with the remaining vehicles functioning as follower vehicles for leader-follower maneuvering. The system further includes an actuator suite configured to adjust the direction and orientation of each vehicle, a sensor suite for stabilization and navigation, and a flight controller for maintaining stable maneuvering, even in the presence of actuator faults and sensor deception attacks. Processing circuitry is configured with a reinforcement learning neural network that includes identifier, actor, and critic radial basis function neural networks to estimate movement, adjust control actions, and assess vehicle performance based on feedback signals, including corrupted signals from the sensor suite due to deception attacks.

The invention discloses an unmanned agricultural machine path tracking preset performance control method and system, and the method comprises the steps: collecting the pose information of an unmanned agricultural machine, building an unmanned agricultural machine path tracking error kinematic model, and designing a finite time preset performance function based on the unmanned agricultural machine path tracking error kinematic model. The constraint is converted through nonlinear mapping; based on the Lyapunov stability theory, adopting a backstepping method to recursively design an actual control law, and according to the actual control law, performing inverse transformation on the input of the unmanned agricultural machine tracking control system in the error kinematics model of the unmanned agricultural machine tracking control system to obtain the front wheel steering angle of the unmanned agricultural machine. According to the method, convergence is completed within finite time, and transient control performance and steady control performance of a path tracking control system are considered.

The invention relates to a system inertia boundary backstepping method based on frequency dynamic indexes and distributed node equivalent inertia analysis, and belongs to the field of power optimization. The method comprises the following steps: dividing a frequency response process into an inertia response stage, a primary frequency modulation stage and a secondary frequency modulation stage; constructing a multi-source frequency response unified equivalent model; an equivalent inertia boundary inversion method based on RoCoF and Nadr dual constraints is provided, a frequency safety boundary is converted into a constraint expression for the equivalent total inertia of a system, and an inertia boundary required under given power disturbance is reversely deduced; the method further comprises the steps of introducing static load voltage characteristic correction during inertia boundary inversion; the method further comprises the step of constructing a frequency support-oriented regionalization control strategy. According to the method, the safety support boundary of the low-inertia emergency power supply system is researched from the overall level and the regional level, and a theoretical basis is provided for refined frequency control and resource optimization layout.

The invention provides a self-adaptive elastic formation control method for a quad-rotor unmanned aerial vehicle under DoS attack, and relates to the technical field of unmanned aerial vehicle safety control, and the method is based on a multi-aerial vehicle formation system in a leader-follower mode under an undirected network topological graph. Establishing a mathematical model for the DoS attack blocking the communication channel, and establishing a changed multi-machine system network topology according to the DoS attack; constructing a distributed elastic observer to obtain unknown leader information under DoS attack, constructing a tracking formation error containing a time-varying offset signal according to the output of the observer, and taking a neighborhood in which the formation error can be converged to zero as a control target; and designing a self-adaptive elastic formation controller by using a backstepping method to realize self-adaptive elastic formation control of the four-rotor unmanned aerial vehicle. According to the control method provided by the invention, the condition that at least one communication channel is subjected to DoS attack is considered, and the constructed adaptive elastic formation controller can ensure that the tracking formation error of the multi-machine formation system is converged to a neighborhood of zero under the DoS attack, so that an expected formation is achieved.

The invention relates to the technical field of robot control, and discloses a mechanical arm specified performance fault-tolerant control method based on a fuzzy logic system, and the method comprises the steps: building a kinematics and dynamics model and a fault model of an agricultural robot; establishing a relative error dynamic equation between the actual position and the expected position; designing a specified performance function, and constraining the tracking error within an expected range by using the specified performance function; on the basis, designing a virtual control law by using a backstepping method under the condition of meeting error constraints; designing an adaptive control law by adopting a fuzzy logic system based on an uncertain item, unknown environment interference and a fault model in the kinetic model; a specified performance controller is designed based on a virtual control law, fuzzy logic adaptive control and full-state feedback agricultural robot, and specified performance fault-tolerant control of the agricultural robot is realized. The stability proves that the tracking error of the agricultural robot converges to any small neighborhood near zero within fixed time.

The invention provides an ocean current interference resistant fault-tolerant control method and system for a robot multi-motor propulsion system, and the method comprises the steps: inputting the actual and expected position error and speed error of an underwater robot into a self-adaptive backstepping sliding mode path tracking controller, and obtaining the thrust needed by tracking motion; introducing a propeller weight matrix representing the fault degree of the propeller into a weighted pseudo-inverse method, optimizing the propeller weight matrix by adopting an intelligent algorithm to obtain an optimal propeller weight matrix, and performing thrust distribution on thrust required by tracking motion through the weighted pseudo-inverse method to obtain a thrust distribution result; according to the operation and thrust distribution results of the multiple motors, the parameters of all the motors are adjusted in a self-adaptive mode through a multi-motor fault-tolerant control strategy; and each motor remotely controls the underwater robot based on the parameters after self-adaptive adjustment, and obtains the actual position and speed of the underwater robot at the current moment for loop closing.

The invention relates to the technical field of cooperative control, in particular to an adaptive event triggering fixed time consistency control method for a multi-agent system, which comprises the following steps of: S1, constructing a nonlinear high-order multi-agent system mathematical model with uncertain dynamics; s2, constructing a Lyapunov function containing an uncertain parameter lower bound based on coordinate transformation; s3, designing an adaptive event trigger controller with a positive bounded function by using a backstepping control method, wherein the adaptive event trigger controller comprises a virtual controller, an adaptive law and an event trigger mechanism; and S4, through stability analysis, it is proved that the high-order nonlinear multi-agent with the uncertain dynamic state can achieve actual fixed time output consistency, Zeno behaviors are avoided, and the self-adaptive event triggering fixed time consistency control method of the multi-agent system has the advantages of being high in practicability and easy to popularize. The problem of actual fixed time output consistency of a high-order nonlinear multi-agent system is solved, and an existing actual fixed time output consistency result is popularized.

The invention provides a heterogeneous motorcade prediction control method based on a path information protection mechanism, and relates to the technical field of network connection unmanned vehicle cooperative control and intelligent traffic. Firstly, a path information protection mechanism is provided for preventing an alien invading vehicle from tracking a motorcade and ensuring the safety of motorcade path information. In addition, a predictor model is established to improve the estimation performance of the adaptive law, and a variable gain nonlinear expansion state observer is designed to approach and compensate the total disturbance. Then, by introducing a variable-gain hyperbolic tangent tracking differentiator, the problem of'complexity explosion 'caused by repeated derivation of a virtual controller in a backstepping method is avoided; and finally, a distributed prediction cooperative controller is constructed, and stable and safe driving of the whole heterogeneous network connection fleet is ensured.

The invention discloses an electro-hydraulic system adaptive neural network preset time control method, which integrates a neural network real-time learning technology, innovatively introduces a preset time performance function, and designs an adaptive neural network preset time performance controller. For the electro-hydraulic system position tracking control problem, it can be ensured that system output transient performance and steady-state performance converge to a specified performance index range within a preset time, safe and reliable operation of the system is ensured, the unknown dynamic state of the system can be learned in real time through a neural network, high-precision motion control performance is achieved, and the control precision is improved. And the problem of differential explosion in traditional backstepping control of the electro-hydraulic system can be avoided, and the influence of measurement noise on the control precision is reduced.

The invention discloses an event-triggered adaptive fixed time control method for a random nonlinear switching system. The method comprises the following steps: constructing a model of the random nonlinear switching system; introducing an event triggering mechanism; designing a fixed time control strategy; and designing an event-triggered fixed-time adaptive controller based on a backstepping method design framework. According to the event-triggered adaptive fixed time control method for the random nonlinear switching system, an event-triggered control strategy is adopted to reduce the communication burden of the system. A new controller is stably constructed by using fixed time, and the upper bound of convergence time is irrelevant to initial conditions; meanwhile, by introducing nonlinear mapping, a random nonlinear system with symmetric and asymmetric output constraints is converted into a new unconstrained system, and random switching of the control system is realized by using a switching signal.

The invention discloses an under-actuated AUV and USV formation trajectory tracking control method, and relates to the technical field of under-actuated AUV and USV three-dimensional trajectory tracking control, and the method comprises the steps: building a dynamic model of an under-actuated underwater robot-unmanned ship heterogeneous multi-agent system; establishing a specified time control system; designing a distributed specified time state observer for each follower agent; defining a system trajectory tracking error; lOS error conversion is carried out on the under-actuated underwater robot; designing a Lyapunov function, and designing a specified time sliding mode surface of the system by using a backstepping method; designing a specified time interference observer for each follower agent; and redesigning a Lyapunov function, and designing a system control rate by using a backstepping method. The problems that the formation control precision is limited and the convergence time for reaching the stable state cannot be accurately estimated when an existing control method is applied are solved.

The invention relates to a precise-control unmanned aerial vehicle cluster adaptive tracking control method, system and device under communication limitation, and the method comprises the steps: employing a quantizer to quantize an obtained control input instruction related to an expected flight path, including a speed, a pitch angle and a yaw angle, obtaining a control input quantized signal, and enabling the control input quantized signal to be transmitted to an unmanned aerial vehicle cluster; obtaining current error data according to a control input quantization signal and current flight real-time data, and obtaining a linear velocity quantization instruction and an angular velocity quantization instruction according to the current error data by adopting a self-adaptive controller designed and obtained based on error dynamics and a self-adaptive backstepping method; and an unmanned aerial vehicle autopilot model is adopted to adjust the attitude and speed of the unmanned aerial vehicle according to the linear velocity quantification instruction and the angular velocity quantification instruction so as to realize adaptive control of the trajectory of the unmanned aerial vehicle. By adopting the method, effective and accurate tracking control of each unmanned aerial vehicle can be realized under the background of a large number of unmanned aerial vehicle clusters with massive interaction data.

The invention discloses a control method for ultra-low-altitude flight of a large unmanned aerial vehicle, and the method comprises the following steps: building a kinetic model of the unmanned aerial vehicle, the kinetic model comprising dynamic equations of position, speed, attitude control and control input; performing error tracking control on the attitude and the speed of the unmanned aerial vehicle by adopting a Backstepping nonlinear control method based on the dynamic model; estimating external disturbance and dynamic parameters of the unmanned aerial vehicle in real time by combining an L1 adaptive control method, and adaptively adjusting control input to compensate wind speed change; predicting a future flight state by using a model prediction control algorithm, optimizing a flight path and compensating wind speed disturbance; a safe trajectory is planned in real time through a dynamic window method, and obstacles are avoided in combination with dynamic constraints. According to the invention, through organic combination of Backstepping control, L1 adaptive control, MPC and DWA algorithms, the complex problems of wind speed disturbance, obstacle avoidance, high-precision operation and the like in ultra-low-altitude flight can be solved.

The invention relates to the technical field of data processing, and discloses a motor controller research and development method and system based on an intelligent algorithm. The method comprises the steps that after parameter identification is carried out on a motor, PID parameters are optimized through an adaptive differential evolution algorithm; constructing a double-loop control structure according to the optimal PID parameters; designing a virtual sensor based on the optimized double-loop structure and obtaining a health index; fusing the multi-source signal to obtain a fusion speed signal; therefore, a backstepping sliding mode controller is realized and system verification is completed. The motor control system with high fault-tolerant capability is constructed by fusing various intelligent algorithms and virtual sensor technologies, the basic operation performance of the motor can be kept even if the sensor is abnormal or fails, and the reliability and the safety of the system are improved.

According to the cooperative control method and system of the non-linear multi-agent system, under the condition that communication link faults are considered, a non-linear dynamic model of each single-connecting-rod mechanical arm is established, and unknown uncertain non-linear dynamic states in the single-connecting-rod mechanical arms are fuzzified through a fuzzy logic system; a mixed event triggering mechanism is introduced, so that state information of a leader is estimated, and the communication frequency is effectively reduced; and constructing a virtual controller and an actual controller by utilizing a backstepping method based on the estimated state information, so as to control the position of the following mechanical arm to be synchronous with the expected trajectory of the leading mechanical arm under the condition of being influenced by the communication link fault. According to the method, the state of the multi-single-connecting-rod mechanical arm system can be kept consistent and stable tracking control can be achieved under the condition that communication link faults and nonlinear uncertainty exist without global information.

The invention discloses a hydraulic system preset time dynamic surface position control method, which is based on a dynamic surface control framework, innovatively introduces a preset time-varying function, and designs a nonlinear robust position controller with preset time. Aiming at the position control problem of the hydraulic cylinder of the hydraulic system, the differential explosion problem frequently occurring in the classic backstepping control design of the hydraulic system is avoided, the influence of measurement noise on the control precision is reduced, the controller design is simplified, and the excellent transient performance of the system within the preset time is guaranteed; and the convergence time can be clearly determined and does not depend on initial conditions of the system, so that the method has high-precision tracking performance.