554 results about "Lyapunov stability" patented technology

The invention discloses a self-adaptive event triggering safety control method of a multi-agent system under spoofing attack, and relates to event triggering control of the multi-agent system. A communication topological graph, an adjacent matrix and a Laplacian matrix of the system are defined based on a graph theory, it is assumed that communication topology between multi-agent systems is randomly switched, and the switching process follows a continuous time Markov process; assumptions on the communication topological graphs are relaxed, it is required that the combined graph of all possible communication topological graphs is undirected connected, and it is not required that each switching sub-graph is connected; modeling a spoofing attack, setting an attack signal upper bound, and defining a system security control objective function under the spoofing attack; an event-triggered distributed adaptive security control strategy is designed, the state of a neighbor agent is estimated by using an observer technology, it is verified that the control strategy can ensure the system security under the Markov random switching communication topology, and the effectiveness of the method is verified by using a Lyapunov stability theory, a stochastic analysis and linear matrix inequality method and simulation.

The invention discloses a micro-grid intelligent scheduling method and system based on an AI large model, and the method comprises the steps: collecting and processing the real-time output data of a photovoltaic power station and a wind power station, and obtaining a standardized micro-grid operation data set; a discrete time micro-grid dynamic model is established and a recursive least square method is adopted to carry out system parameter online estimation so as to obtain a robust scheduling scheme oriented to uncertainty interference; and in combination with real-time operation state monitoring, real-time micro-grid intelligent scheduling is carried out by deploying edge computing nodes. According to the method, the Lyapunov stability theory and the control barrier function are combined, a safety reinforcement learning framework oriented to micro-grid dispatching is constructed, and the absolute safety of system operation in the dispatching process is ensured.

The invention discloses a multi-agent spatio-temporal dynamic system intrusion tolerance and fault tolerance cooperative security consistency control method. The method comprises the following steps: constructing mathematical models of a leader and a follower of a multi-agent spatio-temporal dynamic system based on a partial differential equation; constructing a mathematical model of actuator faults and spoofing attacks; defining a consistency condition to obtain an error system; constructing a proper Lyapunov functional, and obtaining a system stability criterion by using a Lyapunov stability theory; and the error of the multi-agent space-time dynamic system tends to zero by using the state feedback controller, so that the system realizes safety consistency control. The invention comprehensively considers that a multi-agent space-time dynamic system is easy to have actuator faults in a complex environment and is easy to suffer from network attacks in network communication, introduces an intrusion tolerance and fault tolerance cooperative security consistency control method, improves the robustness of the system, and improves the reliability of the system. And the multi-agent space-time dynamic system can achieve a safety consistency control target.

The invention discloses a control method applied to self-piercing riveting, and relates to the technical field of self-piercing riveting control. According to the method, riveting material data are collected in real time through a multi-source data fusion interface, the high-dynamic deformation process of a riveting head is captured, deformation rate and rebound value characteristic values are extracted, and a multi-dimensional process parameter set is constructed. And comprehensively analyzing the hardness, thickness and plastic dissipation characteristics of the material, and establishing an anti-deformation capability evaluation function. By introducing the fluctuation characteristics of the punching force, the nonlinear relation between the punching force and the elastic deformation of the riveting rod is analyzed, a hybrid compensation model is constructed, and a riveting dynamic coupling compensation vector is output. And generating a compensation weight matrix through an adaptive optimization algorithm, and dynamically adjusting the stamping force and the displacement of the riveting rod in combination with an anti-saturation control strategy. And dynamically adjusting a gain parameter through multi-scale filtering and a Lyapunov stability criterion, and triggering iterative updating of a compensation weight parameter during out-of-tolerance so as to optimize a control effect. Riveting precision and stability are improved, and the service life of equipment is prolonged.

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 high-precision position detection method for a permanent magnet motor system, and the method comprises the following steps: S1, building a harmonic counter electromotive force model of a permanent magnet motor, and analyzing the influence of the harmonic counter electromotive force on a position estimation error; s2, constructing an arcsine saturation function of which the boundary layer thickness is self-adjusted along with the error of the sliding mode surface as a switching function; s3, establishing a mathematical model of a sliding-mode observer based on an arcsine saturation function, and obtaining a parameter selection range by using a Lyapunov stability criterion; s4, carrying out Park conversion on the output value of the sliding-mode observer to obtain a direct current amount under a dq coordinate system; a first-order low-pass filter is used for filtering, fundamental wave counter electromotive force is reserved, and sine counter electromotive force is extracted from the fundamental wave counter electromotive force through Park inverse transformation; and S5, in combination with an orthogonal phase-locked loop, performing position estimation on the output sine counter electromotive force to obtain an estimated rotor position angle and an estimated rotor rotating speed. The precision and the dynamic performance of sensorless control of the permanent magnet motor can be improved.

The invention provides a multi-mode control method and system for a brushless doubly-fed motor, and relates to the technical field of motor control, and the method comprises the steps: extracting torque-rotating speed slopes and dynamic response time constants of two points based on a steady-state working point of a current mode and an expected working point of a target switching mode, and constructing a linear correction function; fusing an output value of the linear correction function with an optimization result to generate a modal transition compensation coefficient, and injecting the compensation coefficient into a rotor side voltage control ring based on a feedforward control channel to generate an anti-disturbance dynamic compensation control strategy; and for a non-smooth adjustment item in the dynamic compensation control strategy, performing smoothing processing on a discontinuous boundary of a control quantity by adopting a subgradient projection algorithm, verifying continuous conductivity of a control curved surface in combination with a Lyapunov stability criterion, and generating a final control instruction. According to the invention, adaptive smooth switching control of the brushless doubly-fed motor can be realized.

The invention relates to the technical field of power electronic control, and discloses a voltage and current stabilization and surge suppression cooperative control method for power equipment, and the method comprises the following steps: dynamically segmenting an output current frequency band, and remodeling the output impedance characteristics of a system; predicting and controlling a hierarchical optimization control target based on a multi-time scale model; dynamically switching an energy feedback path; identifying aging parameters of the device on line and adaptively adjusting a control strategy; global stability verification and fault-tolerant protection: verifying the stability of the control system based on the Lyapunov stability theory, and triggering a protection mechanism when the magnetic flux is saturated or the aging of the device exceeds the limit. By means of dynamic impedance frequency domain segmentation, the impedance characteristic of the system is divided into a low-frequency voltage stabilization area and a high-frequency surge suppression area according to frequency bands, the contradiction that in a traditional method, it is difficult to consider steady-state precision and transient surge suppression at the same time due to the single impedance characteristic is solved, and collaborative optimization of voltage stabilization control and surge energy blocking is achieved.

The invention provides a mechanical arm dynamic obstacle avoidance trajectory planning method based on a dynamic motion element and a steering force field, and belongs to the technical field of robots. Secondly, learning the teaching track preprocessed in the step 1 by using an improved dynamic motion primitive (DMP) algorithm, so that the DMP can generate a motion mode similar to the teaching track; through a depth camera installed around a mechanical arm or at a fixed position, obstacle information, shape and motion state are obtained in real time, obstacle avoidance repulsive force is calculated, steering force is quantified, and obstacle avoidance of free motion obstacles is achieved; a Lyapunov equation is constructed, based on the Lyapunov stability theory, the stability of the improved DMP algorithm is proved, and it is ensured that the motion trail of the mechanical arm in the dynamic environment is converged to a target point; and finally, carrying out an obstacle avoidance experiment to verify the dynamic obstacle avoidance capability of the mechanical arm. According to the invention, the trajectory generalization ability of the mechanical arm in a narrow working space is obviously improved.

A preset time robust sliding mode control method for a mechanical arm system comprises the steps that (1) under the condition that parameter perturbation, joint friction and external disturbance are considered, a dynamic model and a tracking error system model of the mechanical arm system are established; (2) a robust preset time controller is designed to ensure that the speed tracking error of the mechanical arm converges to zero within preset time; 3) designing a non-singular fast terminal sliding mode controller based on a preset time reaching law so as to ensure that a position tracking error can also converge to zero within the preset time; 4) the feasibility and effectiveness of the method are verified by using a Lyapunov stability analysis method; aiming at a mechanical arm system with parameter perturbation, joint friction and external disturbance, the invention develops a robust nonsingular fast terminal sliding mode control method in preset time so as to ensure that the mechanical arm system can accurately track an expected trajectory signal in the preset time, so that the tracking speed of a mechanical arm is improved, and the tracking precision is improved. And the tracking accuracy and safety of the mechanical arm are ensured.

The invention discloses a stable control method for a deformation process of an unmanned aerial vehicle in a complex environment, and relates to the technical field of unmanned aerial vehicle flight control. The state-space equation is used for describing flight dynamics parameters in the deformation process of the unmanned aerial vehicle in the complex environment; constructing a disturbance observer based on the state-space equation; the disturbance observer is used for observing a lumped disturbance quantity caused by a complex environment and wing tip folding deformation; constructing a sliding mode surface equation according to the attitude angle tracking error; constructing an attitude stability controller according to the disturbance observer and the sliding mode surface equation; and based on the Lyapunov stability theory, performing stable control on the unmanned aerial vehicle by using an attitude stability controller. The lumped disturbance quantity caused by the complex environment and wing tip folding deformation is considered during flight control of the unmanned aerial vehicle, and the control precision of the deformable unmanned aerial vehicle under the complex environment condition can be improved.

The invention discloses a time-delay load frequency control stability analysis method considering electric vehicle access, and relates to the technical field of load frequency control stability analysis, and the method comprises the steps: building a nominal load frequency control model of a load frequency control system through the power generation characteristics of an electric vehicle and a distributed power supply through model reconstruction, establishing a determined load frequency control model of the load frequency control system according to the electric vehicle charge state fluctuation; introducing an auxiliary polynomial, constructing two types of improved relaxation matrix functions, combining multiple integrals, and respectively obtaining an asymptotic stability criterion of a nominal load frequency control system and a robust stability criterion of an uncertain load frequency control system by using a Lyapunov stability analysis method and a Bessel-Legendre inequality; time-delay load frequency control stability analysis is completed; the reliability and the accuracy of load frequency control analysis of the power system and the applicability of the analysis method are remarkably improved.

The invention discloses a cascaded auto-disturbance rejection rotating speed stability control method with a quasi-resonance link based on propeller load, and the method comprises the following steps: S1, building a speed ring mathematical equation containing disturbance according to the basic structure and working principle of a surface-mounted permanent magnet synchronous motor; s2, according to aerodynamic characteristics of the propeller, establishing a mathematical model of tension and load torque of the propeller; s3, establishing a quasi-resonance cascade active-disturbance-rejection speed controller containing disturbance compensation according to the S1 and the S2, and performing stability analysis on a cascade expansion observer equation according to a Lyapunov stability theorem; and S4, a cascade active disturbance rejection control method with a quasi-resonance link is used to realize suppression of periodic and non-periodic disturbance of the rotating speed so as to realize high-performance speed control. According to the method, the motor and the propeller are integrally modeled, the influence of periodic disturbance and non-periodic disturbance on the rotating speed is considered at the same time, and the method has better dynamic performance and steady-state performance.

The invention relates to a networked nonlinear system control method based on fuzzy adaptive event triggering. The method comprises the following steps: S1, carrying out equivalent T-S fuzzy modeling on a networked nonlinear system based on a T-S fuzzy theory; s2, constructing a fuzzy adaptive event triggering mechanism based on T-S fuzzy model structured characteristics and fuzzy membership function information; s3, based on the fuzzy adaptive event trigger mechanism and the sampling control strategy, constructing a T-S fuzzy networked closed-loop system based on the fuzzy adaptive event trigger mechanism under sampling input; s4, based on a Lyapunov stability theory, establishing a sufficiency condition of asymptotic stability of the T-S fuzzy networked closed-loop system based on a fuzzy adaptive event trigger mechanism; and S5, based on a linear matrix inequality theory, calculating a state feedback gain matrix of the T-S fuzzy networked closed-loop system, and realizing control of the networked nonlinear system based on the T-S fuzzy model. According to the method, unnecessary communication transmission is reduced and the control cost is saved while the stability of the system is ensured.

The invention discloses a security boundary consistency control method for a multi-agent spatio-temporal dynamic system. The method specifically comprises the following technical steps: firstly, constructing a leader-follower spatio-temporal dynamic mathematical model of the multi-agent system based on a partial differential equation; then establishing an abnormal working condition mathematical model containing an actuator fault and a network spoofing attack; deducing to obtain an error dynamic system equation by defining a system consistency control target; constructing an energy function by using a Lyapunov stability theory, and deducing to obtain a system stability criterion; finally, a security boundary consistency control method is designed, and multi-agent space-time dynamic consistency control is achieved. The method effectively solves the problem of high cost caused by arrangement of actuators in a whole spatial domain in a traditional scheme and the problem that a multi-agent system is easily interfered by network attacks in a communication process, and has remarkable application value in engineering practice.

The invention relates to the technical field of digital control switching power supplies, and particularly discloses a switching power supply dynamic response adjusting system based on digital control, which comprises a digital controller, a multi-channel analog-to-digital conversion module, a parameter identification module, a self-adaptive control law adjusting module, a pulse width modulation driving module and a stability monitoring module. On-line identification of key parameters of a system is realized by constructing a nonlinear state space model and combining an extended Kalman filtering algorithm, dynamic compensation is carried out for non-minimum phase characteristics such as a right half plane zero point, and a self-adaptive control law adjustment module optimizes a control strategy in real time according to an updated model. A Lyapunov stability criterion and a predictive control mechanism are introduced, the robustness and response speed of the system are improved, and a stability monitoring module identifies an abnormal state through sliding window variance analysis and peak detection and triggers a fault-tolerant mechanism to guarantee safe operation of the system.

The invention relates to the technical field of attitude control, and discloses a method for realizing self-adaptive active leveling compensation, which comprises the following steps: acquiring real-time data of a vehicle, the data being provided by a plurality of sensors; processing the real-time data through a data fusion algorithm; according to the inclination state and the motion parameters of the vehicle, hydraulic supporting leg adjusting parameters are calculated through a multi-objective optimization algorithm; according to the calculated hydraulic supporting leg adjusting parameters, the action of the hydraulic supporting leg is adjusted through a PI D controller for vehicle balance recovery; the stability of the hydraulic supporting leg adjusting system is verified through the Lyapunov stability theory, and the hydraulic supporting leg adjusting system is used for enabling the adjusting process of the hydraulic supporting leg to be completed in all operation states; and the vehicle inclination state is fed back in real time. By fusing multi-sensor data and adopting a Kalman filtering or particle filtering algorithm, high-precision perception of the real-time inclination state and dynamic parameters of the vehicle is realized, and a more accurate and reliable vehicle attitude judgment effect is obtained.

The invention provides an optical storage multi-mode control method. The optical storage multi-mode control method comprises the following steps of 1, collecting multi-source data of photovoltaic output, energy storage SOC, load requirements and electricity price signals in real time; 2, dividing operation modes based on fuzzy logic, such as an economic mode, an emergency power supply mode and a peak value adjustment mode; 3, generating an optimal power distribution strategy in each mode through model predictive control (MPC); and step 4, designing a mode switching logic based on Lyapunov stability to realize non-impact transition, and relates to the technical field of photovoltaic power generation and energy storage system cooperative control, and through multi-source data fusion, fuzzy logic mode division, model prediction control and the Lyapunov stability switching logic, the mode switching logic based on the Lyapunov stability is realized. Self-adaptive efficient operation of the optical storage system under complex working conditions is achieved, the problems that a traditional control method is single in mode, lagged in response and low in energy utilization rate are solved, and the method is suitable for improving the economical efficiency and stability of the optical storage system.

The invention relates to a multi-machine cooperative dynamic balance driving method, system and device for a spiral lifting machine, and the method comprises the steps: carrying out the compensation of the time lag of an actuator through employing a non-linear state observation and time lag compensation combined method, and obtaining a feedforward control quantity; performing Lyapunov stability feedback processing on the vibration acceleration signal to obtain a vibration suppression control quantity; designing a multi-section S-shaped speed curve, and calculating a closed-loop feedback control quantity by adopting PID (Proportion Integration Differentiation) combined with a robust control algorithm; and according to the feedforward control quantity, the vibration suppression control quantity and the closed-loop feedback control quantity, multi-machine cooperative dynamic balance driving of the spiral lifting machine is carried out. According to the invention, a non-linear state observation and time delay compensation technology is adopted, so that the synchronization error is reduced to a millisecond level; the low-frequency vibration amplitude is reduced through dynamic feedback control designed based on the Lyapunov theory; the hydraulic impact is obviously reduced by adopting a multi-section S-shaped speed curve; external disturbance is observed in real time through Kalman filtering, feed-forward compensation is carried out, and the robustness of the system is greatly enhanced.

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 discloses a doubly-fed induction generator fault detection method combining a sliding-mode observer and a bilateral cumulative sum algorithm, and the method comprises the following steps: designing a doubly-fed induction generator rotor current sliding-mode observer based on the combination of a novel reaching law and a doubly-fed induction generator state equation; carrying out stability and convergence analysis on the designed novel reaching law by adopting a Lyapunov stability theory; and the fault detection method of the doubly-fed induction generator is designed in combination with the optimized bilateral accumulation sum algorithm. A rotor current sensor fault, an input interference voltage fault, a voltage drop fault and a stator turn-to-turn short circuit fault are introduced. The rotor current tracking performance of the designed sliding-mode observer under different doubly-fed induction generator faults is verified by using a residual value obtained by comparing a doubly-fed induction generator rotor current value estimated by the sliding-mode observer with an actual rotor current output value; and meanwhile, the reliability of the designed doubly-fed induction generator fault detection method based on the optimized bilateral cumulative sum algorithm is verified. The doubly-fed induction generator fault detection method combining the sliding-mode observer and the bilateral cumulative sum algorithm has the advantages of being high in tracking precision and accurate in fault state characterization, meanwhile, weak fault features can be effectively converted into digital signals to be output, and faults of the doubly-fed induction generator are detected.

The invention relates to the field of mine geological disaster monitoring and early warning, and discloses a mine geological disaster early warning method based on artificial intelligence, and the method comprises the steps: describing the dynamic change of a state variable and a multi-physics coupling effect in a disaster evolution process through constructing a kinetic model; carrying out discrete solution on the model by adopting a numerical method to obtain a disaster evolution path; a disaster instability critical point is accurately identified in combination with a bifurcation theory and Lyapunov stability analysis; designing an optimal early warning strategy by using a reinforcement learning algorithm, and dynamically selecting an alarm signal triggering opportunity; fusing monitoring data and a model in real time, and dynamically updating a disaster state and a risk level; and outputting an alarm signal and providing emergency suggestions. According to the method, real-time response of accurate disaster prediction and early warning is realized, and the mine geological disaster prevention and control capability is improved.

The invention relates to a heterogeneous neural network acceleration device based on cooperation of a DSP (Digital Signal Processor) and a memristor, which adopts a hybrid computing architecture design of a DSP and memristor cross array, such as dynamic precision switching, a real-time closed-loop feedback mechanism based on a Lyapunov stability theory, a self-adaptive cross-domain quantization module, hardware instruction set optimization and the like. The hybrid architecture has good balance performance and universality, stability is remarkably improved through conductivity monitoring, self-adaptive PWM pulse generation and quantization table updating, a higher signal-to-noise ratio is achieved at lower cost through combination of a self-adaptive cross-domain quantization module and noise reduction, hardware level optimization is achieved through customization of an instruction set, the hybrid architecture is more suitable for an HNN calculation task, and the method is suitable for the HNN calculation task. The energy efficiency ratio and the anti-drifting capability of the HNN acceleration circuit are improved, and the acceleration performance is improved.

The invention relates to an unmanned aerial vehicle trajectory tracking control method based on model prediction and preset performance constraint, and the method proposes to introduce Lyapunov stability constraint into a model prediction control framework and integrate a preset performance control mechanism, thereby achieving the unification of performance constraint and system stability analysis. Comprising the following steps: establishing a nonlinear system model based on unmanned aerial vehicle dynamics; position errors and attitude errors are defined, a preset performance function is constructed, and errors with performance constraints are converted into unconstrained errors through error normalization and nonlinear transformation; establishing a model prediction optimization problem on the premise of considering input saturation and stability constraints; designing an auxiliary control law based on the transformation error to construct a stability constraint; it is proved that the control strategy can ensure that errors meet preset performance constraints and system local asymptotic stability. According to the invention, stable and reliable trajectory tracking control of the unmanned aerial vehicle system can be realized, and the method has high tracking precision and good dynamic performance.

The invention discloses a sensorless control method and system based on fast and slow time scale characteristics, and belongs to the technical field of permanent magnet synchronous motor control. Firstly, a basic dynamic equation of the surface-mounted permanent magnet synchronous motor on a two-phase synchronous rotation orthogonal coordinate system is deduced, and a simplified mathematical model based on fast and slow time scale characteristics is obtained. Then, a novel sliding-mode observer is constructed and used for estimating the position and speed information of the servo system in real time, and therefore non-sensing control is achieved. And finally, based on a singular perturbation theory, respectively carrying out Lyapunov stability analysis in a fast time scale and a slow time scale so as to obtain a gain determination method of the sliding-mode observer. Based on the fast and slow time scale characteristics of the permanent magnet synchronous motor, a novel modeling method and a sensorless control technology are provided, and estimated values of the position and the speed are used in a servo driving system.

The invention relates to a passive fault-tolerant control method and system for a tilt-rotor unmanned aerial vehicle based on a nonlinear variable parameter model. The method comprises the following steps: S1, establishing an actuator fault model; s2, constructing a longitudinal dynamic model of the tilt-rotor aircraft, and converting the longitudinal dynamic model of the tilt-rotor aircraft into a nonlinear variable parameter model of the tilt-rotor aircraft; s3, introducing an actuator fault model into the nonlinear variable parameter model to obtain a nonlinear variable parameter model containing an actuator fault; s4, designing a fault-tolerant controller by adopting a quadratic sum convex programming method; s5, based on the Lyapunov stability theory, establishing a fault-tolerant controller solvability condition of the nonlinear variable parameter system model containing the actuator fault, enabling the nonlinear variable parameter model to have exponential stability, and solving the solvability condition to obtain the fault-tolerant controller; and S6, applying the fault-tolerant controller to a flight control system of the tilt-rotor aircraft.

The invention discloses a driving information security management system and method based on a memristor neural network, and relates to the technical field of information security, and the method comprises the steps: S1, monitoring the synchronism of a response system and a driving system in real time, and collecting feedback signal data; s2, analyzing and designing a synchronous controller through a Lyapunov stability theory; s3, dynamically selecting an information encryption mode by using the memristor neural network according to the synchronization error, the feedback signal and the signal encryption level; s4, ensuring that the probability that the encrypted information is decrypted in the driving feedback interval is lower than a set threshold value; and S5, extracting and analyzing the historical encryption signal, and updating an information encryption mode and a network security level. Based on the Lyapunov stability theory, the synchronous controller is constructed, the signal encryption level is dynamically adjusted by using the memristor neural network, and high-level safety is ensured to be kept in the dynamic change process.

The invention discloses a steer-by-wire system self-adaptive steering angle tracking strategy based on a high-order all-drive theory, which comprises the following steps of: 1, establishing a kinetic model of a steer-by-wire system according to the steer-by-wire system of a passenger vehicle, and determining a state variable and a control variable; 2, designing an extended disturbance observer according to the kinetic model established in the step 1, and observing the internal and external uncertainty and disturbance of the system, and compensating the uncertainty and disturbance to a controller; 3, converting the steering-by-wire system dynamics model in the step 1 into an all-wheel-drive system model, and designing a self-adaptive high-order all-wheel-drive controller; and 4, verifying the stability of the high-order all-wheel-drive controller designed in the step 3 according to a Lyapunov stability method. According to the method, uncertainty interferences inside and outside the system are considered in the built steer-by-wire system model, the interferences are lumped, the extended disturbance observer is used for observation and compensation to the controller, and the accuracy and robustness of the control system are improved.

The invention discloses an air-ground cooperative system model prediction formation control method in an underground pipe gallery environment, and the method comprises the steps: constructing an air-ground cooperative system nonlinear model, and constructing an underground pipe gallery model; designing a high-order interference observer to estimate current unknown external disturbance, and establishing an interference prediction model to estimate future unknown external disturbance; a rolling optimization control strategy based on state prediction is designed, control signals of all followers are optimized according to the current state of the system and the predicted future state in each control period, and the tracking precision is ensured while the tracking precision is improved. Collision between followers, collision between the followers and the wall of the pipe gallery and obstacle avoidance between the followers and static and dynamic obstacles in the pipe gallery are achieved; and solving is carried out based on a distributed model prediction formation control algorithm combined with a Lyapunov stability theory, so that each follower keeps an expected formation configuration while tracking a leader trajectory. And the driving safety of the formation in the underground pipe gallery environment is ensured.

The invention discloses a method for controlling intelligent equipment of an electric smelting furnace for magnesia based on neural network self-adaptive PID, and relates to the field of industrial control of equipment with great energy consumption. According to the method, modeling is carried out on the electric smelting magnesium furnace, the current working condition and the production boundary condition are analyzed to obtain an ideal current set value, and a corresponding PID controller is designed with the purpose of eliminating the error between the current three-phase electrode current and the set value. And the three-phase electrode current can rapidly track the changing set current value, so that the safe and stable operation of the production process is ensured. Designing a self-adaptive control method based on a radial basis function neural network; designing a self-adaptive law aiming at meeting Lyapunov stability conditions, and approaching key variables of the self-adaptive law by the radial basis function neural network; and finally, in combination with PID parameters and an adaptive law, obtaining the dynamic PID controller of the electrical smelting furnace for magnesia, which is embedded with the neural network and has self-tuning and self-adaptive gains. Compared with the prior art, the neural network adaptive PID controller has the advantages that the control effect is obviously improved, and the transient and steady-state performance of the system can be effectively improved.