60 results about "Adaptive observer" patented technology

The invention discloses a VSG-DFIG subsynchronous oscillation adaptive suppression method based on a Hamiltonian model, and belongs to the technical field of subsynchronous oscillation suppression of a power system. The method aims at solving the problem that an existing method for suppressing subsynchronous oscillation existing in grid-connected operation of the doubly-fed induction generator is poor in suppression effect. Comprising the following steps: constructing a port controlled Hamiltonian dissipation model; constructing a closed-loop system dynamic equation based on interconnection and damping distribution under strict passivity; an expected energy function in a unit closed-loop form is constructed, a DFIG unit feedback control law based on a Hamiltonian model is obtained, and further deformation is carried out to obtain a deformed DFIG unit feedback control law; the port controlled Hamiltonian dissipation model is corrected, and a corrected port controlled Hamiltonian dissipation model is obtained; constructing a dynamic equation of the anti-interference adaptive observer, and establishing an error dynamic equation; and constructing a Lyapunov function, and designing an estimation error feedback control law according to the stability condition of the Lyapunov function. The subsynchronous oscillation suppression circuit is used for suppressing subsynchronous oscillation.

The invention relates to an anti-interference speed control method for a permanent magnet synchronous motor, in particular to the technical field of electrical engineering, and effectively solves the problem that the control performance of the permanent magnet synchronous motor is degraded due to model parameter drift and external load sudden change under complex working conditions. According to the method, the high robustness of sliding mode control and the rolling optimization characteristic of model prediction control are creatively fused, and an adaptive observer is introduced to estimate the system state and lumped disturbance online, so that the real-time feedforward compensation and active suppression of parameter uncertainty and disturbance are realized; according to the method, the dynamic response speed and the steady-state precision of the system are remarkably improved, the rotating speed fluctuation and the torque ripple are effectively inhibited, meanwhile, controller parameters are adaptively adjusted through an intelligent learning mechanism, the stability and the adaptability in long-term operation are ensured, and the method is suitable for large-scale popularization and application. The limitation that a traditional control strategy depends on an accurate model and the disturbance boundary is unknown is overcome fundamentally, and high-performance and high-robustness speed control is achieved.

The invention relates to the technical field of servo drivers, in particular to a servo driver control method, and the technical scheme comprises the steps: carrying out the online identification of load inertia and external disturbance through a self-adaptive observer, generating a dynamic compensation parameter, constructing a multi-model parallel self-adaptive observer group, and carrying out the online identification of the load inertia and external disturbance through the self-adaptive observer group, a dynamic confidence evaluation mechanism is designed, disturbance dynamic compensation is realized, parameter sudden change and slow change scenes can be taken into consideration, the real-time performance of dynamic response can be ensured, compensation parameters are input into a fuzzy neural network controller, an optimal control quantity is generated in combination with a preset control target, a multi-source compensation parameter input channel is constructed, and the optimal control quantity is generated. A dynamic input weighting module is designed, fuzzy rule confidence is updated through an online strategy gradient algorithm to generate an optimized control quantity, the working condition adaptability can be enhanced, a dynamic target can be dealt with, finally, a sliding mode variable structure algorithm is adopted to carry out high-frequency buffeting suppression on the control quantity, and a final driving signal is output to a power module. And the control precision of the servo driver is improved.

The invention relates to the technical field of unmanned aerial vehicle charging control, and discloses an unmanned aerial vehicle charging control method and control system. The method comprises the following steps: after responding to an unmanned aerial vehicle charging request, starting a charging control flow comprising an environmental parameter acquisition stage and a battery state initialization stage; and after the charging connection is established, the adaptive observer executes system state estimation and external disturbance estimation in parallel based on the acquired environmental parameters and the initialized battery state. And dynamically adjusting a charging strategy according to the estimated real-time result, including refreshing a power control signal and activating a compensation mechanism, and cooperatively controlling the energy output of the charging equipment according to the power control signal and activating the compensation mechanism until the battery reaches a predetermined charging target. According to the method, through dual consideration of the environment and the battery state and in combination with a parallel estimation and dynamic adjustment mechanism, the charging process can accurately match the battery requirement of the unmanned aerial vehicle, various interference factors are effectively coped, the reliability and the adaptability of the charging process are improved, and a brand new solution is provided for efficient charging of the unmanned aerial vehicle.

The invention relates to a cooperative robot control method based on a self-adaptive observer and a neural network. The cooperative robot control method comprises the steps of system modeling and signal collection, operator identity recognition and feature extraction, super-spiral disturbance observer, frequency domain self-adaptive admittance filter and composite control law synthesis. A kinetic model is constructed and disturbance terms are defined by collecting man-machine interaction torque and joint states in real time; extracting the characteristics of an operator, carrying out identity recognition by adopting an incremental DBSCAN algorithm, and associating personalized admittance parameters; designing a super-spiral disturbance observer to dynamically estimate disturbance; adaptively adjusting admittance parameters according to the identity of the operator and the frequency band characteristics to generate an expected trajectory; and the sliding mode control and the double-loop neural network compensation item are combined to synthesize the control torque, so that high-precision and smooth man-machine interaction control is realized. The method has the advantages of being high in anti-interference performance, good in personalized adaptability, high in tracking precision and the like.

The invention provides a self-adaptive dynamic programming self-triggering fault-tolerant control method for a flexible mechanical arm, and particularly relates to a flexible joint mechanical arm with a non-affine fault and an unknown control direction. The method comprises the following steps: firstly, designing a self-adaptive observer, and estimating a non-affine actuator fault and an unknown item caused by an unknown control input direction; constructing an evaluation neural network considering unknown terms by using the approximation capability of the neural network so as to approach a system cost function; then solving a Hamiltonian-Jacobian-Bellman equation to obtain approximate optimal control input for realizing ideal trajectory tracking and vibration suppression; meanwhile, a self-triggering mechanism is adopted, the next triggering moment is calculated according to the current system state, hardware requirements are reduced, and communication resources are saved. According to the method, control performance, energy consumption and information transmission constraint are considered, resource consumption can be reduced while quick response is achieved, and trajectory tracking and vibration suppression tasks of the flexible joint mechanical arm are safely and reliably achieved.

The invention discloses an electric energy metering method and device based on harmonic self-adaptive observation and zero point tracking type SOGI-FLL. The electric energy metering method comprises the following steps: step 1, carrying out ADC sampling on voltage and current signals of a power grid; 2, performing frequency rapid estimation on the voltage signal based on a zero point tracking frequency-locked loop, and obtaining a fundamental wave angular frequency of a power grid in real time; step 3, constructing adaptive observers of voltage and current by using the fundamental wave angular frequency, and estimating parameters of fundamental waves, harmonic waves and direct current components in voltage and current signals in real time through an adaptive law based on Lyapunov stability; 4, according to the parameter estimation value, the amplitude and phase of the voltage and current are calculated; and step 5, calculating active power and reactive power of each harmonic based on the voltage and current amplitudes and phases obtained in the step 4, and summing to obtain total active power and total reactive power so as to realize metering of active electric energy and reactive electric energy. According to the invention, rapid and accurate metering of fundamental wave and harmonic wave electric energy under complex working conditions is realized.

The invention relates to the technical field of motor control in the intelligent power grid industry, and discloses a space vector control method for a twelve-phase permanent magnet synchronous motor flywheel energy storage system, a control circuit of the flywheel energy storage system relates to power electronic components such as various semiconductor field-effect tubes, and the space vector control method is used for high-precision control of a motor. Comprising the steps of S1, twelve-phase space vector decoupling fault-tolerant control, S2, high-order harmonic energy recovery closed loop, S3, quantum annealing parameter optimization layer, S4, flux linkage adaptive observer network construction and S5, 18-dimensional switching vector table construction. According to the scheme, through multi-reference coordinate system transformation and a redundant phase reconstruction algorithm, rapid fault-tolerant response of the twelve-phase motor during a single-phase or two-phase fault is realized, a current path is dynamically recombined during the fault, a negative-sequence current compensation ring is combined, torque ripple is effectively suppressed, and the reliability of the motor is improved under a high-speed charging and discharging working condition. The harmonic distortion rate is detected in real time through the sliding window FFT, the bandwidth of the controller is adjusted, and the problem that the dynamic response of a traditional fault-tolerant strategy is insufficient is solved.

The invention discloses a method and device for estimating residual waste gas in a cylinder of a hydrogen combustion range extender, a vehicle and a medium, and relates to the technical field of vehicle engines, and the method comprises the steps: building a waste gas flow model at an exhaust valve based on engine structure parameters, a pressure signal and a temperature signal of an exhaust manifold, and exhaust efficiency; on the basis of the inlet waste gas flow of the exhaust manifold and the outlet waste gas flow of the exhaust manifold, according to the mass conservation relation and the ideal gas state equation, an exhaust pressure average value discrete dynamic model of the exhaust manifold is built; constructing a self-adaptive observer based on an exhaust pressure average discrete dynamic model, and estimating the exhaust efficiency; according to the online estimation result of the exhaust efficiency, the mass of exhaust gas exhausted in each cycle is obtained, and the amount of residual exhaust gas in the cylinder is calculated. By adopting the method, the mass of exhaust gas in each cycle and the amount of residual exhaust gas in the cylinder can be accurately estimated in real time in the actual vehicle running process, so that reliable reference is provided for combustion control and emission optimization.

The present application relates to a kind of optimal covert attack detection method, device and medium based on double adaptive observer in power grid system, for detecting optimal covert attack based on the vulnerability of adaptive unknown input observer in direct current micro grid system, for the direct current micro grid system with ZIP load, the dynamic model of distributed power generation unit is established;Adaptive unknown input observer and adaptive distributed observer are deployed for each distributed power generation unit, monitor its state when decoupling with neighbor distributed power generation unit;Adaptive distributed observer accepts the output of neighbor adaptive unknown input observer and predicts the output of distributed power generation unit;Based on the detection residual between actual output and estimated system output of system through adaptive unknown input observer and adaptive distributed observer, it is judged whether distributed power generation unit is attacked.Compared with prior art, the reliability and security of direct current micro grid system are effectively improved.

The invention discloses a permanent magnet synchronous motor multi-source disturbance suppression method based on an adaptive observer. Firstly, a multi-source disturbance model is reconstructed by designing non-singular coordinate transformation, and coupling between an unknown disturbance frequency and a to-be-estimated state is eliminated. On the basis, a self-adaptive observer is designed in combination with a virtual invariant manifold technology, and constant disturbance and multi-frequency unknown period disturbance components in the system can be estimated online. And finally, injecting a composite disturbance signal estimated by the observer in real time into a controller of the system as a feed-forward compensation item, thereby realizing suppression of multi-frequency disturbance. According to the method, accurate frequency and amplitude information of disturbance does not need to be known in advance, multiple periodic disturbances and constant disturbances can be effectively processed at the same time, and the application range of a permanent magnet synchronous motor driving system in a multi-source disturbance environment is effectively expanded.

The application relates to the technical field of multi-agent systems, and provides a multi-agent sensor fault diagnosis method and system based on adaptive learning, which comprises the following steps: acquiring control signals and outputs of each multi-agent subsystem; based on the control signals and the outputs, an adaptive observer is used to obtain a state vector of the adaptive observer; by comparing the state vector of the adaptive observer with a state vector of the multi-agent subsystem, fault information of a sensor corresponding to the multi-agent subsystem is obtained; wherein the adaptive observer ensures that a tracking error and a weight vector error are bounded, the tracking error is a difference value between an output estimation of the multi-agent subsystem and an output of the multi-agent subsystem, and the weight vector error is a difference value between an estimated weight of a neural network in the adaptive observer and an ideal value; and the final consistent boundedness of identification errors is ensured.

This invention discloses a vehicle state estimation method based on dynamic event triggering. The method first designs an output estimator based on dynamic event triggering to reduce sampling of the actual output and obtain a continuous output estimation signal. Then, it designs an adaptive observer based on the triggered sampling signal to estimate the vehicle state and avoid Zeno behavior. When applied to road vehicles, this method effectively estimates the vehicle state and avoids Zeno behavior.

The application discloses a high-speed train basic resistance online estimation method, device, equipment and medium, relates to the field of track traffic control and high-speed train dynamics control, and the method comprises the following steps: constructing a multi-particle longitudinal dynamics equation of a high-speed train based on the internal coupling force of the high-speed train; designing an adaptive observer based on the multi-particle longitudinal dynamics equation; the adaptive observer comprises a state estimation equation, a parameter adaptive law, an auxiliary variable dynamic equation, a gain matrix updating law and a basic resistance equation; and the basic resistance of the high-speed train is estimated online based on the adaptive observer, so that the basic resistance estimation values of each carriage of the high-speed train are obtained. The application can effectively estimate the basic resistance online and asymptotically, significantly reduces the test cost and improves the robust performance, does not need to rely on complex offline tests, and has both high precision and strong robustness.

The invention provides a signal quantization and event-triggered unmanned ship trajectory tracking multi-constraint control method based on a neural network observer, and the method comprises the steps: carrying out the linear description of an input quantization process in a controller through a linear analysis model, and enabling the controller to not need the prior information of any quantization parameter; aiming at the problem that the ship speed cannot be directly measured in navigation practice, a neural network adaptive observer is designed, and the motion state of the unmanned ship is estimated; a constraint processing mechanism based on a logarithm barrier function is adopted, and the state variable is kept within a preset safety boundary; a neural network approximates unknown model dynamics in a system, a low-frequency gain learning method is introduced to suppress control signal high-frequency oscillation caused by external disturbance, consistent final boundaries of all closed-loop signals are proved, and boundaries of internal signals and quantization errors are clarified; the effectiveness of the signal quantization and event-triggered unmanned ship trajectory tracking multi-constraint control method based on the neural network observer is verified through a simulation experiment.

The application relates to the technical field of unmanned aerial vehicle (UAV) charging control, and discloses a UAV charging control method and a control system. The method comprises the following steps: after responding to a UAV charging request, starting a charging control process comprising an environment parameter collection and battery state initialization stage. After establishing a charging connection, a self-adaptive observer performs system state estimation and external disturbance estimation in parallel based on the collected environment parameters and the initialized battery state. According to the real-time results of the above estimation, the charging strategy is dynamically adjusted, including refreshing the power control signal and activating the compensation mechanism, and the energy output of the charging equipment is cooperatively controlled accordingly until the battery reaches the predetermined charging target. Through the double consideration of the environment and the battery state, combined with the parallel estimation and dynamic adjustment mechanism, the charging process can accurately match the battery demand of the UAV, effectively cope with various interference factors, improve the reliability and adaptability of the charging process, and provide a new solution for efficient charging of the UAV.

The invention relates to the technical field of chemical process equipment monitoring, in particular to a cascade heat exchange reactor fault diagnosis method, system and equipment based on a distributed observer and a storage medium. Structural parameters of the cascade heat exchange reactor are obtained, unitized segmentation is carried out according to the arrangement position of a temperature sensor, and the temperature transfer relation between units and a heat exchange mass exchange model are established; fault sensitive parameters are identified based on model analysis, and a distributed observer assembly comprising a plurality of adaptive observers is constructed for each subsystem; a state estimation value and a fault parameter estimation value are obtained in real time through an observer assembly, a residual sequence is constructed for fault detection, residual signal convergence characteristics and a parameter change mode are analyzed, and local fault types and global fault types are effectively distinguished; and determining the specific position and severity of the fault according to the residual convergence criterion and the fault parameter variation amplitude, and completing fault isolation and quantitative evaluation. Accurate fault diagnosis of a complex cascade system is realized, and fault detection precision and diagnosis efficiency are improved.

This invention proposes a speed control method based on a composite observer and adaptive super-torsional sliding mode, comprising the following steps: First, an extended dynamic model of the PMLSM system is established by introducing extended variables related to speed measurement; second, based on unknown motor parameters, internal disturbances, and external load changes, a composite observer consisting of a model reference adaptive observer and a generalized proportional-integral observer is designed according to the extended dynamic model of the PMLSM; next, for the extended dynamic model of the PMLSM after compensating for lumped disturbances, an outer-loop AST speed control law based on the composite observer is designed to enhance the robustness of the system; finally, a PI-based inner-loop d-q axis current vector control method is adopted to achieve the motor stator current tracking its given value. This invention can comprehensively consider complex actual operating conditions such as unknown mover mass, internal and external disturbances, and speed measurement noise, enabling the speed control of the PMLSM drive system to have good dynamic and steady-state performance.

The application relates to a predictive control method and device based on an unknown parameter model, wherein the method comprises the following steps: parameter setting is performed on an unknown parameter model used for simulating a controlled real system to obtain model parameters; the model parameters of the unknown parameter model are updated through an adaptive observer, and a terminal domain of the unknown parameter model is calculated; based on the model parameters of the unknown parameter model, the adaptive observer and the terminal domain of the unknown parameter model, iterative calculation of a control quantity of the controlled real system is repeatedly performed, and when the number of times of the iterative calculation reaches a preset total iteration number, an optimal control quantity of the controlled real system is obtained. Through the application, the problem of how to perform predictive control based on an unknown parameter model is solved, the model parameters are updated based on the adaptive observer and are input into the unknown parameter model, the best control quantity is calculated by the model using the terminal domain, the robustness of the system is improved, and good control performance is achieved under the condition that the model parameters are unknown.

The application provides a self-adaptive dynamic programming self-triggered fault-tolerant control method for a flexible mechanical arm, and in particular to a flexible joint mechanical arm with non-affine faults and unknown control directions. First, an adaptive observer is designed to estimate the unknown terms caused by non-affine actuator faults and unknown control input directions. Then, the evaluation neural network considering the unknown terms is constructed by using the neural network approximation capability to approximate the system cost function. Subsequently, the approximate optimal control input for realizing ideal trajectory tracking and vibration suppression is obtained by solving the Hamilton-Jacobi-Bellman equation. At the same time, the self-triggered mechanism is adopted to calculate the next trigger time according to the current system state, thereby reducing the hardware demand and saving the communication resources. The application takes into account the control performance, energy consumption and information transmission constraints, can quickly respond while reducing resource consumption, and safely and reliably realizes the trajectory tracking and vibration suppression task of the flexible joint mechanical arm.

The application discloses a kind of adaptive observer-based closed-loop system anti-interference joint estimation method, it belongs to adaptive estimation field.The application solves the problem that it is difficult to obtain the parameters, state and disturbance of controlled object in real time accurately in closed-loop feedback structure.The estimation method of the application does not need prior information of controlled system parameters and disturbance, only utilizes the input and output measurement data and controller information of system, decouples control input and noise, solves the biased estimation problem caused by noise correlation in closed-loop structure;It has robustness to unknown disturbance and noise, and the adaptive observer-based closed-loop system anti-interference joint estimation problem is constructed by using the technology of relatively prime decomposition, and the signal unrelated to measurement noise is used as the reference input described by the stable image of controlled system, then a closed-loop robust adaptive estimation mechanism is built, so as to realize unbiased estimation of unknown parameters, state and disturbance of system.The method of the application can be applied to closed-loop system anti-interference joint estimation.

The application provides a kind of offshore aerospace launch and recovery ship power positioning fault-tolerant anti-interference method, belongs to offshore aerospace launch and recovery ship automatic control technical field;The method of the application is mainly oriented to the research of offshore aerospace launch and recovery ship power positioning control considering unknown external marine environment interference and mixed faults of thrusters, uses interference observer and bias fault observer and multiplicative fault adaptive observer, solves the online estimation and inhibition of unknown external marine environment interference on offshore aerospace launch and recovery ship power positioning and the estimation and compensation problem of mixed faults of offshore aerospace launch and recovery ship thrusters;By using a composite fault-tolerant anti-interference controller to correct the tracking feedback control error problem, effectively enhance the anti-interference ability of offshore aerospace launch and recovery ship, improve the reliability of offshore aerospace launch and recovery ship control, so that the expected position of offshore aerospace launch and recovery ship reaches the expected effect.

The invention discloses a sliding mode control method based on a neural network adaptive observer, and belongs to the technical field of hydraulic position servo system control. The method comprises the following steps: establishing a nonlinear mathematical model of the hydraulic servo system; designing a state observer based on a radial basis function neural network, estimating each state in the system, and designing a system sliding mode control law by using observed parameters; designing a neural network adaptive law by using a Lyapunov stability theory; smoothing the output of the controller by using a filter; and proper controller hyper-parameters are selected, so that the system can realize an expected control effect and accurately track an expected signal. The control strategy designed by the invention can perform online estimation on nonlinearity which is difficult to model of the system, can accurately observe the state of the system, is good in steady-state performance, and is easy for engineering application.

This invention specifically relates to an EMB torque distribution control method based on a road surface adhesion coefficient estimation algorithm, belonging to the field of vehicle braking control technology. It includes: rolling time-domain adhesion coefficient estimation based on multi-source data fusion; and optimized braking force distribution based on yaw moment balance. In this invention, the rolling time-domain adhesion coefficient estimation method utilizes historical data from cameras, IMUs, GNSS, and wheel speed sensors to construct a nonlinear adaptive observer, overcoming the limitations of traditional single-sensor or current-moment data estimation. On one hand, the camera uses UNet semantic segmentation and a ShuffleNet network to identify road surface types, pre-defining the adhesion coefficient range and reducing initial estimation errors. On the other hand, the rolling time-domain observer combines the vehicle dynamics model with historical data, achieving exponential stable convergence only when the tires enter the nonlinear region at a historical moment, demonstrating strong robustness to model uncertainties.

The invention belongs to the technical field of man-machine cooperative control, and particularly relates to a man-machine sharing control method for an exogenous input system under Nash equilibrium, which comprises the following steps of: constructing a man-machine sharing control system model with exogenous input items, and setting a cost function of man-machine parties; a man-machine non-cooperative differential game model is considered, an information structure of man-machine interaction meets a closed-loop perfect state mode, a control target is Nash equilibrium between cost functions of the two parties, and a Nash equilibrium solution form of human and machine control input and an algorithm of control gain and additional items are provided; based on fuzziness and uncertainty of human input, constructing an adaptive observer, and combining a Lyapunov equation to obtain a human input estimation gain and an updating law of an additional item; the real-time Nash equilibrium solution of man-machine sharing control in an exogenous input system is provided while the influence of exogenous input on the system state is restrained, and the method has high scientific significance and application value.

The invention discloses a cylindrical lithium battery internal temperature field real-time estimation method and system, and the method comprises the steps: building a temperature space distribution model based on a heat conduction equation, normalizing the space-time quantity through scaling transformation, and converting a non-homogeneous boundary condition into a homogeneous boundary condition through variable replacement; decoupling a space-time relationship between the temperature distribution quantity and a heat production item by adopting a double decomposition technology, and regarding a heat production time variable as a virtual source; and establishing a finite-dimensional ordinary differential equation model by using a Galerkin method. Designing a self-adaptive observer, and estimating a low-order state variable and a virtual source time sequence on line by combining a self-adaptive learning algorithm so as to measure and estimate a temperature error driving update rate; and reconstructing a temperature distribution quantity and a virtual source estimation value through space-time synthesis of the estimation sequence and a preset space basis function. Real-time estimation of the internal temperature field and the heat production amount of the cylindrical lithium battery can be effectively realized by means of a small number of thermocouples only through temperature measurement data without voltage and current sensors.

The invention discloses a high-speed motor continuous terminal sliding mode current decoupling method based on an adaptive observer, and belongs to the technical field of motor control. The method comprises the following steps: constructing a rapid continuous terminal sliding mode surface based on a hyperbolic tangent function for permanent magnet synchronous motor current loop control so as to improve the convergence speed and suppress buffeting; a linear expansion state observer based on adaptive parameters is designed, and d-axis and q-axis currents and total disturbance are accurately observed in real time; and performing feedforward compensation on the observed disturbance value to a voltage control law derived from the sliding mode surface. The method effectively solves the problems that a traditional control method is slow in convergence, large in buffeting, weak in anti-interference capacity and incomplete in decoupling, and the dynamic response speed, control precision and robustness of the system under parameter change and external disturbance are remarkably improved.

The invention provides an energy storage converter active-disturbance-rejection control method based on observer bandwidth adjustment, and belongs to the technical field of micro-grid control, and the method comprises the steps: obtaining a transfer function of a second-order linear active-disturbance-rejection controller from input to output; determining a bandwidth range of a linear expansion state observer in a second-order linear active disturbance rejection controller in a system stable working domain based on the transfer function; obtaining an estimation error based on the estimated and actual voltage values, and dynamically adjusting the bandwidth parameter of the observer according to the estimation error and the bandwidth range; setting the high-frequency gain of a second-order linear active-disturbance-rejection controller according to the operating parameters of the converter; and the controller for dynamically adjusting the bandwidth is used for controlling the energy storage converter. The method has the advantages that the bandwidth of the observer is dynamically increased to be close to the maximum safety value in the photovoltaic power fluctuation or load sudden change stage through a self-adaptive observer bandwidth adjusting mechanism, and the dynamic convergence process of the system is accelerated; and the bandwidth is automatically reduced to the minimum safety value in the steady-state operation stage, and the steady-state ripple amplitude is greatly reduced.

The invention discloses an automobile transverse auxiliary control method considering driver fatigue. The method comprises the steps that inherent parameters and real-time parameters of an automobile are collected; establishing an automobile transverse dynamics and kinematics model and a driver steering model by using the collected parameters; establishing a global human-vehicle-road system fuzzy model by using the established model; according to the global human-vehicle-road system fuzzy model, designing a fast adaptive observer, and obtaining an estimated value of driver torque; according to the estimated value of the driver torque and the ideal value of the driver torque, representing a driver fatigue parameter; designing a self-adaptive auxiliary controller according to the fatigue parameters of the driver; and performing auxiliary control on the automobile according to the self-adaptive auxiliary controller. According to the method, the interval type-2 T-S fuzzy model method is adopted, the uncertainty of the membership function is described, the observer is adopted to represent the driver fatigue, auxiliary control over the driver fatigue vehicle is achieved, and the driving safety and stability are improved.

A computer-implemented method for robust adaptive control and / or a robust adaptive observer of an indefinitely time-delayed system is disclosed, in particular wherein the indefinitely time-delayed system comprises a controller, optionally an observer, and a technical system to be controlled, wherein the controller and / or the observer communicate at least partially with the technical system to be controlled via a communication network, the method comprising: receiving a time delay determined for a first time point for the indefinitely time-delayed system and a time delay determined for a second time point for the indefinitely time-delayed system, wherein the second time point is later than the first time point;Determining one or more transition probabilities for transitions from time delays at the first time to time delays at the second time based on the time delay determined for the first time for the indefinitely time-delayed system and the time delay determined for the second time for the indefinitely time-delayed system; and determining one or more ambiguity sets around the one or more determined transition probabilities.