48 results about "Backstepping" patented technology

The application relates to the technical field of multi-spacecraft cooperative control, and discloses a multi-spacecraft grouping consistency restraint control method and device under optimized FLS, which comprises the following steps: acquiring the network topology of a heterogeneous multi-spacecraft system and constructing a multi-subgroup directed communication topology network, constructing a spacecraft attitude control system model based on modified Rodrigues parameters; approximating unknown nonlinear terms in the model through a fuzzy logic system, constructing a fuzzy logic system based on a gradient descent algorithm and Armijo line search criterion; constructing a restraint control term and using a backstepping method to construct a virtual control law, combining the output of the fuzzy logic system based on the gradient descent algorithm and the Armijo line search criterion to construct an actual control input law, so that grouping consistency restraint control of the heterogeneous multi-spacecraft system is realized. The application can improve real-time approximation precision and adaptive step length adjustment capability while guaranteeing grouping consistency, and can enhance topology applicability and task cooperation flexibility.

This invention addresses the common failure problems encountered by actuators during the flight of heavy-lift launch vehicles in disturbed environments. It studies a method for achieving rocket attitude stabilization and tracking under complex conditions, proposing an adaptive fault-tolerant backstepping attitude control design method. First, to avoid attitude singularities, a quaternion-based kinematic and dynamic model of the launch vehicle oriented towards actuator failures is established. Based on this model, an adaptive fault-tolerant backstepping attitude controller is designed to adaptively estimate and compensate for uncertainties and external disturbances, handling potential failures and ensuring the stability of the control system. The algorithm proposed in this invention is more effective and superior than traditional adaptive backstepping methods.

A recursive RISE control method and system for maglev train levitation systems is disclosed. The control method includes: constructing the dynamic equations and voltage balance equations of the electromagnets based on a single-point levitation system, and then constructing a dynamic model of the single-point levitation system accordingly; designing a virtual control law, and then designing a recursive RISE controller based on the backstepping method by combining the dynamic model of the single-point levitation system and the virtual control law; performing stability analysis on the RISE controller to obtain its stability conditions; and, based on satisfying the stability conditions, using the RISE controller to perform real-time control of the levitation gap of the maglev train levitation system. This invention has excellent anti-interference capabilities, reducing the impact of external interference or model uncertainty on levitation accuracy. Furthermore, by handling the nonlinear relationship between voltage and current through the backstepping method, this invention avoids the model mismatch problem caused by linearization.

This invention discloses a method for improving the synchronization stability of a grid-connected inverter based on backstepping, comprising: setting a low-pass filter in the active power-frequency droop control loop of the grid-connected inverter; establishing a corresponding state model based on the dynamic characteristics of the low-pass filter; constructing intermediate state variables according to the state model; and recursively deriving the control input expression based on the intermediate state variables using the backstepping control method to obtain a control law that satisfies the Lyapunov stability condition; introducing the control law into the active power-frequency droop control loop to adjust the output frequency of the grid-connected inverter, thereby optimizing the active power-frequency droop control strategy of the grid-connected inverter. This invention only requires superimposing the designed controller on the synchronization loop to effectively enhance the dynamic stability of the system and avoid instability and oscillations caused by factors such as temperature drift and component aging.

This invention discloses a distributed optimization and safe formation control method for multiple robotic arms with nonlinear uncertainty. By establishing a dynamic model of the multi-robotic arm system with nonlinear uncertainty, a dynamic event triggering method with internal system information is constructed. Each robotic arm broadcasts its position information to its neighboring nodes only when the triggering condition is met. A predetermined reference trajectory is generated for each robotic arm system, converging to the global optimum and satisfying formation constraints. The position tracking error between the actual output of the robotic arm system and the predetermined reference trajectory at each moment is calculated. The virtual control law designed in the first step of the back-reasoning is used as the target to be optimized. A quadratic programming problem is constructed by combining the safety constraint boundary inequality built with the control obstacle function to obtain the safe virtual control law. In the second step of the back-reasoning, the velocity tracking error between the actual state of the robotic arm and the safe virtual control signal is calculated, constructing a back-reasoning controller based on the obstacle Lyapunov function. This invention overcomes the control instability risk caused by strong nonlinearity and achieves high-precision trajectory tracking.

This invention discloses an output feedback control method and system for a flexible joint manipulator. The method includes: constructing a general state-space model of the flexible joint manipulator; converting the state-space model of the flexible joint manipulator into a linear state observer, which is used to output the auxiliary estimation states of the link angular velocity and the motor angular velocity; defining trajectory tracking error variables step by step; based on the trajectory tracking error variables, link angular position, motor angular position, auxiliary estimation states of link angular velocity and motor angular velocity, deriving and constructing the first to third virtual control laws step by step using the backstepping method; and solving the final control input command according to the first to third virtual control laws to form a linear feedback closed-loop controller. This invention only requires conventional position sensors to achieve output feedback control, which can significantly improve the trajectory tracking accuracy and engineering robustness of the flexible joint servo system.

The present application relates to a kind of discrete multi-motor servo system fuzzy wavelet neural network control method with event triggering mechanism, belong to multi-motor servo system control field, comprising the following steps: S1: the discrete time system model of multi-motor servo system is established;S2: design two type fuzzy wavelet neural network to estimate unknown nonlinear function caused by external interference and internal parameter perturbation;S3: based on the backstepping control framework, introduce event triggering mechanism with dead zone operator, design discrete time fuzzy wavelet neural network controller;S4: using the discrete time fuzzy wavelet neural network controller, control multi-motor servo system is carried out.

An adaptive control method for cyber-physical systems (CPS) subjected to injection and deception attacks includes the following steps: S1: Constructing a control system model under a cyber-physical framework; S2: Defining actuator attacks when the system's sensors and actuators are subjected to adversary injection and deception attacks; S3: Defining sensor attacks when the system is subjected to sensor attacks; S4: Rewriting the system model under cyberattacks; S5: Based on the rewritten system model, obtaining the adaptive control law u using the BackStepping method; S6: Further deriving that all signals in the closed-loop system are globally bounded based on the adaptive control law u in S5, and designing a controller by introducing Nussbaum even functions and their variable derivatives. When the system is subjected to injection and deception attacks, the control parameters are adjusted to make the adjustment error arbitrarily small. This invention can ensure that the adjustment error can be arbitrarily small under injection and deception attacks by adjusting the control parameters.

The application relates to a permanent magnet synchronous motor multi-source nonlinear disturbance suppression method and system, and belongs to the technical field of permanent magnet synchronous motor control. The application comprises the following steps: establishing an electromechanical coupling discrete model of a permanent magnet synchronous motor actuator; designing a linear extended state observer based on a super-local model to observe system total disturbance in real time; decomposing the system total disturbance into multiple nonlinear disturbance factor components based on the electromechanical coupling discrete model, and calculating the contribution degrees of the components; designing a backstepping adaptive controller based on the contribution degrees, and combining the linear extended state observer to perform feedforward compensation; designing a deadbeat predictive current controller, and performing weighted fusion of the backstepping adaptive controller through a dynamic weight optimization strategy to generate a control voltage; and applying the finally generated d-axis and q-axis voltage control quantities to the permanent magnet synchronous motor actuator. The application can precisely suppress multi-source nonlinear disturbance, and improve the tracking accuracy, dynamic response speed and robustness of the system.

The application provides a kind of unmanned aerial vehicle path planning algorithm based on particle swarm optimization and reverse learning, belongs to artificial intelligence optimization algorithm and intelligent control technical field.The technical scheme is: including the following steps: step 1, the success rate parameter is introduced into the traditional linear decreasing adaptive mechanism;Step 2, a particle is randomly generated, and the next particle is initialized based on the particle parameters using Logistic chaotic mapping, and so on, the particle swarm is initialized;Step 3, in the process of algorithm optimization, the particles that converge rapidly in a short time are disturbed using Levy flight;Step 4, the algorithm is optimized using reverse learning.The application reduces the operation time of the algorithm and improves the stability of the unmanned aerial vehicle in space operation.

This invention proposes a tracking control method and system for tower cranes with unknown disturbances, belonging to the technical field of anti-swaying for tower cranes. The method includes: acquiring the state information of the tower crane's boom, trolley, and load; establishing a dynamic model of the tower crane and decoupling the unknown disturbance and its nonlinear unknown coefficients in the dynamic model using Young's inequality; designing a coordinate transformation to eliminate the pure feedback structure in the system; introducing coordinate transformation into a virtual controller designed based on the backstepping method, constructing the virtual controller, and decoupling it from the disturbance; and, based on the virtual controller and the actual controller, controlling the position of the trolley on the tower crane to maintain synchronization with the designed desired trajectory under unknown external disturbances using a near-disturbance decoupling control method. This invention avoids the swaying of the tower crane load due to uncertain disturbances, which is of great significance for improving the working efficiency and safe construction of tower cranes.

This invention discloses an output compensation control method for multi-agent systems under aperiodic DoS attacks. First, a follower fuzzy approximation system model and an aperiodic DoS attack model are constructed. When the attack activation causes output loss, an attack compensation mechanism is triggered to maintain the last output and provide continuous compensation signals. A switching fuzzy state observer is constructed to estimate the agent states, and an adaptive law for the attack activation and dormancy phases is designed using a Lyapunov function. A globally preset performance function is constructed to establish dynamic performance boundaries, and the consistency tracking error and normalization transformation error are calculated to construct a barrier function. A distributed adaptive safety control law is derived by combining the backstepping method. This invention overcomes the shortcomings of output loss during the attack phase leading to decreased observation performance and the dependence of traditional performance functions on initial error values, ensuring globally preset performance and high-precision state estimation, and solving the cooperative control problem under aperiodic DoS attacks.

The application discloses a second-order terminal sliding mode control method based on time delay estimation fuzzy observer backstepping method, and aims at unknown disturbance and trajectory tracking problems of a mechanical arm system in many practical problems, estimates kinematics and dynamics parameters of the system through time delay estimation, estimates total disturbance existing in the system through a novel fuzzy observer, combines the second-order sliding mode control to increase instantaneous response and reduce steady-state error, suppresses chattering phenomenon while guaranteeing finite time convergence of tracking error, and finally proves stability of the system by adopting a Lyapunov method, and according to comparison of different methods, fast and accurate tracking of a desired trajectory in the method is shown.

The application provides a hydraulic wind-driven reverse osmosis seawater desalination control method based on double-layer cooperative optimization, relates to the technical field of renewable energy utilization and seawater desalination equipment control, and comprises the following steps: S1, acquiring hydraulic wind turbine unit parameters and historical wind speed data to obtain the optimal wind wheel rotating speed of the hydraulic wind turbine; S2, determining the real-time energy supply performance of the hydraulic transmission chain and outputting dynamic equivalent energy parameters; S3, generating an energy supply relationship matched with dynamic characteristics and solving an optimal water production steady-state characteristic vector; S4, constructing an outer ring wind wheel rotating speed backstepping sliding mode controller and outputting a motor displacement control signal; and S5, establishing an inner ring motor rotating speed backstepping sliding mode controller and outputting a feed water pressure regulation signal. Through the double-layer cooperative architecture of the upper-layer dynamic target programming and the lower-layer double backstepping sliding mode tracking, the application solves the energy matching and dynamic tracking problems of wind power seawater desalination and improves the wind energy utilization rate and water production stability.

This invention relates to a predetermined-time fuzzy backstepping control method for a fractional-order decoupled dual-mass MEMS gyroscope with delay constraints and actuator faults, belonging to the field of MEMS gyroscope control technology. The method includes: S2: establishing a fractional-order motion equation for the decoupled dual-mass MEMS gyroscope with actuator faults based on the Lagrange equations to eliminate the influence of linear acceleration and vibration at the same frequency; S3: constructing a β-cut 2 type fuzzy logic system to address the uncertainties and nonlinear terms of the system model; superimposing a delay error function on a specified time function to ensure that the error does not violate the constraints after a finite time; using a fractional-order hyperbolic tangent tracking differentiator in the controller design to handle direct fractional derivatives and repeated derivatives; and finally, combining the β-cut 2 type fuzzy logic system to establish a control law with an adaptive law for fault parameters.

The application provides a skull drilling self-adaptive control method and system based on force-position feedback control, relates to the technical field of skull drilling control, and realizes accurate classification of the skull type of a patient and individualized setting of drilling parameters by constructing a mapping model among feedback parameters, skull parameters and drilling parameters; the automatic drilling process in stages is adopted, so that dynamic adjustment of drilling pressure and rotation speed can be realized, and the mechanical properties of different bone layers can be effectively adapted; through acquisition of real-time feedback parameters and backstepping of the mapping model, combined with closed-loop control, continuous self-adaptive adjustment of drilling parameters is realized. The scheme not only improves the operation efficiency of skull drilling, reduces the risk of damage to the skull and brain tissue, but also enhances the adaptability of the system to individual differences of patients, and improves the safety and accuracy of the operation.

The cross-process simulation integrated circuit behavior modeling method based on transfer learning and self-enhancement optimization belongs to the technical field of electronic design automation and artificial intelligence, and first, the circuit simulation data of a source process and a target process are subjected to feature engineering processing and alignment based on physical characteristics; a forward performance prediction model is constructed, a transfer learning strategy of source domain pre-training combined with target domain partial layer fine-tuning is adopted, and a mapping from circuit parameters to performance indicators is established; secondly, a reverse parameter backstepping model is constructed, and self-enhancement closed-loop optimization is performed, the forward model is used as a verifier to perform performance evaluation on the samples generated by the reverse model, high-confidence samples are selected to iteratively update the reverse model, and process rule hard constraints are introduced in the output layer to perform parameter boundary clipping; finally, the circuit design parameters meeting the requirements of the target process are output. The present application significantly improves the design efficiency and physical reliability of the simulation IP cross-process migration, and effectively solves the model generalization problem in the small sample scene.

The invention discloses a two-degree-of-freedom helicopter preset performance tracking control method based on reinforcement learning. The method comprises the following steps: constructing a two-degree-of-freedom helicopter dynamic model; a preset performance function is introduced, a constrained tracking error is converted into an unconstrained form through error conversion, and therefore it is guaranteed that the system reaches a steady state within preset time; designing a preset time nonlinear disturbance observer; based on a backstepping control architecture, combining with a predefined time filter, and decomposing the system into two stages of subsystems through coordinate transformation; an optimal performance index function is constructed for each stage of subsystem, an optimal control strategy is approached online by using a reinforcement learning algorithm, and an optimal virtual controller and a self-adaptive optimal actual control law are designed. According to the method, all signals in a two-degree-of-freedom helicopter closed-loop system can be converged and bounded within predefined time, transient and steady state response indexes of the system are improved, and it is ensured that the helicopter can still rapidly and accurately track a reference trajectory under complex working conditions and quantized limited conditions.

The application discloses a kind of data physical drive reinforcement learning underactuated ship parallel control methods, it is related to ship motion control technical field, including, for the three degrees of freedom underactuated ship of dynamic characteristics and disturbance unknown, establish its mathematical model, fusion offline data and recurrent neural network are constructed data physical drive ship model;Based on this model and online ship state information, a virtual ship system is established to map the real ship;Using backstepping method and dynamic surface control technology, a path tracking parallel feedforward controller is designed on the virtual system;Combined with the optimal control method of reinforcement learning, an approximate parallel optimal controller is designed;Integrate the outputs of the two controllers, generate the actual control command after constraint processing, realize the parallel tracking control of ship path;The application can dynamically adapt to ship loading changes and complex navigation conditions, improve the generalization ability and operation reliability, reduce equipment overconsumption and energy waste, solve the inherent limitations of single target optimization of traditional control method.

The application discloses an event-triggered multi-robot arm system random pre-defined time control method, belongs to the technical field of control, and is used for time control of a multi-robot arm system. The method comprises a dynamic equation of the multi-robot arm system, introduces a trajectory error tracking system, and obtains consensus error of the multi-robot arm system. Unknown functions in the dynamic equation of the multi-robot arm system are estimated, a virtual control signal, an adaptive rate and an event-triggered mechanism are designed according to an adaptive backstepping method, and stability analysis is performed in combination with Lyapunov stability theory. The application simultaneously adopts the event-triggered mechanism, reduces the update frequency of the controller, and in combination with experiments, the consensus error of the multi-robot arm system can converge into a small neighborhood within a pre-defined time of 1s.

The application discloses a rope-driven flexible robot based on neural network adaptive backstepping fault-tolerant control method, comprising the following steps: S1, obtaining a dynamic model and giving a desired reference trajectory; S2, introducing a quaternion to eliminate error and obtaining tracking error of a robot working end; S3, designing a backstepping fault-tolerant control law, according to a robot model and a control target of trajectory tracking, designing a control law of a robot trajectory tracking controller and determining control parameters; S4, introducing an RBF neural network model to approximate unknown quantities which are difficult to directly obtain in the control law; S5, analyzing stability and convergence of the backstepping fault-tolerant system, adjusting control parameters of the controller and outputting trajectory tracking results. The application designs an RBF neural network adaptive backstepping fault-tolerant controller for a rope-driven flexible continuous robot, on the basis of guaranteeing stable tracking of the system by using the backstepping control scheme, a fault-tolerant control scheme is added, so that safety of the robot is ensured when a fault occurs in an actuator.

The present application relates to a kind of spacecraft attitude fault-tolerant tracking control method based on predefined time disturbance observer, belong to spacecraft attitude control technical field, method includes: establishing spacecraft attitude model and introducing actuator failure;Predefined time nonlinear disturbance observer is constructed, the collective disturbance including external disturbance, inertia uncertainty and actuator failure is accurately estimated within preset time;Predefined time attitude tracking controller is designed based on backstepping method, and the output of observer is used for feedforward compensation, so that attitude and angular velocity tracking error converges to the original point small neighborhood within preset time.The present application can be preset on the upper bound of convergence time and independent of initial state, effectively suppress the influence of disturbance and failure, with high precision and strong robustness.

This invention discloses a self-leveling air gap equalization and multi-cavity pressure-stabilized ultra-low disturbance air cavity system and control method, belonging to the field of aircraft control and ground simulation. The system includes an outer axis air-bearing sleeve, an inner axis moving sleeve, a lifting device, a surrounding air gap monitoring device, a distributed air pressure sensor array, and an air path control board. A surrounding interconnected air path is used to automatically eliminate air gap pressure difference. Combined with zoned air supply and feedforward pressure field shaping, a synchronous lifting device maintains a constant air cavity volume to reduce hysteresis effects. The control method relies on offline training of a neural network to construct a compensation model and uses feedforward neural backstepping control to achieve closed-loop pressure stabilization and disturbance suppression. This invention can effectively improve the accuracy, stability, and dynamic response of vertical microgravity simulation, solving the multi-source disturbance problems caused by air gap imbalance, supply and exhaust coupling, and hysteresis feedback in traditional aerodynamic compensation systems.

The application discloses a kind of saturated optimization model-free adaptive control methods, systems, equipment and storage medium for MIMO system, obtain the input data and output data of MIMO system, establish the dynamic linearization data model containing pseudo-gradient matrix;Using projection algorithm with penalty mechanism and reset mechanism to update the pseudo-gradient matrix online;According to discrete quasi-sliding surface and backstepping method, combine the dynamic linearization data model, solve the unconstrained ideal control increment by reverse algebra;According to the amplitude constraint and rate constraint of MIMO system actuator, establish a linear inequality constraint set about control increment, use the unconstrained control increment as reference quantity, construct the quadratic objective function containing tracking error term and control increment weighting term;Using Hildreth algorithm to solve iteratively, obtain the actual control increment that satisfies constraint;According to actual control increment, update control input and apply to MIMO system.

The application provides a limited time cross-domain aircraft cluster formation control method based on a neural network, relates to the field of aircraft control, and specifically comprises the following steps: a unified nonlinear non-strict feedback system is established according to the dynamic characteristics of cross-domain aircraft in different domains; real-time flight states and position information of a plurality of followers are collected; an improved Tan-type nonlinear mapping function is designed, the initial output constraint type of the system is identified, and new variables without constraints are obtained; a double neural network combining a shared network and a dedicated network is constructed, a backstepping method combined with a switching function and a non-singular fast limited time control strategy are designed, and the output of the double neural network structure is controlled; and a switching threshold event triggering mechanism is designed, so that the UAV cluster has different responses in different environments. The technical scheme of the application overcomes the problem that the formation control method in the prior art does not consider mode switching, dynamic coupling and collaborative consistency in cross-domain cooperation.

The application relates to the field of automatic control of intelligent agricultural equipment, and particularly discloses a constraint control method for an intelligent mower based on fixed-time convergence. First, a kinematic model and a heading subsystem of a differential steering intelligent mower are established; a fractional time conversion function is constructed to complete time dimension conversion scheduling of the heading subsystem; a nonlinear mapping function is used to convert the constrained heading system into an unconstrained system; and finally, a nonlinear heading constraint controller based on the backstepping method is designed to realize fixed-time convergence. The application can guarantee that the heading angle converges to the expected value within a fixed time, and the constraint boundary is not exceeded throughout the whole process, thereby improving the response speed, control accuracy and operation safety of the heading control of the intelligent mower.

The present application belongs to the technical field of fault-tolerant control of multi-motor servo system, and specifically discloses a four-motor synchronous drive servo system partial failure fault-tolerant control method and system based on a current observer. The present application designs a current observer for the fault-tolerant control problem of the four-motor servo system under the condition of actuator partial failure, which is used to observe the effective coefficient and current of each motor to determine the working state of the motor. A command filter is used to process the calculation complexity and reduce the calculation burden in the backstepping design process. A speed synchronization and torque balance signal is designed to ensure that each motor can maintain high synchronization performance after failure. The four-motor synchronous drive servo system partial failure fault-tolerant control method disclosed in the present application can ensure that even if there is a failure, the four-motor system can accurately track the given angular position under the premise of ensuring synchronization performance.