210 results about "Steady state error" patented technology

The invention relates to the technical field of cable production automation control, and provides a crosslinked cable production control method based on an improved PID (Proportion Integration Differentiation) control algorithm, which comprises the step of constructing an optimal reference model by establishing a cooperative control framework of temperature, pressure and traction speed and taking the temperature of a vulcanization pipe as a main control variable and the pressure and the traction speed of an extruder as slave control variables. A main controller tracks a temperature set value in real time, PID parameters are dynamically set according to technological parameters such as the thickness deviation of an insulating layer and the crosslinking degree of a material, and precise control is achieved through a three-ring cooperative coupling improved PID controller. A temperature control ring enables the steady-state error of the actual temperature and the set value to be converged to zero. The pressure and speed control loop converges the fluctuation amplitude to a process allowable range. The temperature stability, the pressure uniformity and the traction synchronism of crosslinked cable production can be improved, the process fluctuation and the defect rate are reduced, and the product quality and the production efficiency are improved.

The invention relates to the technical field of gas steel cylinder pressure control, in particular to a gas steel cylinder multi-stage pressure control method based on a self-adaptive threshold value, which can dynamically correct the influence of ambient temperature on a pressure reference value through a constructed pressure-temperature compensation function, eliminate measurement errors caused by temperature drift, and improve the accuracy of pressure control. A pressure fluctuation entropy value is calculated in real time based on a sliding window algorithm, a self-adaptive threshold value adjustment coefficient alpha is generated in combination with historical working condition database matching, a control threshold value is dynamically adjusted along with gas flow velocity fluctuation and equipment aging degree, and the pressure over-limit risk is reduced; according to the method, fuzzy neural network control is introduced in a critical adjustment stage through a multi-stage pressure adjustment strategy, an improved radial basis function dynamic updating mechanism can adapt to a pressure sudden change mode online, the control response speed is increased, and the steady-state error is controlled within + / -1.5%.

The invention relates to the technical field of chip test engineering control, in particular to an active temperature control method and system for a chip aging tester, and the method comprises the steps: analyzing a test process according to test task parameters, and evaluating the expected thermal load change rate of a test chip, so as to dynamically generate a chip target junction temperature control curve; based on the chip target junction temperature control curve, the actual chip junction temperature and the current actual furnace temperature, the theoretical furnace temperature is obtained through a junction temperature closed-loop control algorithm, the furnace temperature compensation amount is calculated through a feedforward compensation algorithm in combination with the test state parameters, and the ideal furnace temperature is obtained according to the theoretical furnace temperature and the furnace temperature compensation amount; the actual temperature control quantity is obtained according to the basic temperature adjusting quantity and the temperature control compensation quantity, a preset chip target junction temperature control curve can be tracked more accurately, transient and steady state errors caused by test program switching and environment fluctuation can be more effectively restrained, and control over the chip junction temperature with higher precision and faster response is achieved.

The invention provides a robust high-precision tracking control method based on a dynamic sliding mode surface. The method comprises the following steps: 1, constructing a driver model; 2, introducing a nonlinear dynamic function term, a high-order error term and an integral term of a displacement error into the dynamic sliding mode surface; and 3, constructing a robust high-precision tracking sliding mode controller based on the dynamic sliding mode surface and the driver model obtained in the step 2. 4, inputting the displacement error of the driver into the sliding mode controller; and the sliding mode controller outputs a voltage signal to the piezoelectric actuator. The nonlinear dynamic function term, the high-order error term and the integral term of the displacement error are introduced into the dynamic sliding mode surface, the transient response speed of the sliding mode surface is improved through the integral term, and the steady-state error of the sliding mode surface is reduced; the nonlinear dynamic function improves the response speed and robustness of the sliding mode surface; the high-order error term enables the sliding mode surface to be more sensitive to error changes.

The invention discloses a self-adaptive control method and system for a condenser for a nuclear power station, relates to the field of condenser control, and aims to predict a future load change trend in a prospective manner by analyzing historical data of a unit power instruction and steam flow, and calculate feed-forward circulating water flow based on a prediction result. On the basis, a feedback control loop based on actually measured pressure is reserved. More importantly, a self-adaptive bias correction mechanism is introduced, and continuous learning and correction are carried out on output of feedback control, so that steady-state errors of the system are eliminated, and uncertainty of the model is compensated. Finally, feed-forward, feedback and self-adaptive correction components are intelligently fused to form a final control instruction which can quickly respond to load changes and can accurately maintain pressure stability, and the problems of severe pressure fluctuation and energy waste under the dynamic working condition are effectively solved.

The invention is suitable for the technical field of control systems, and provides a servo motor speed ring self-tuning method and device, and the method comprises the steps: collecting an error value, a steady-state error and steady-state time in the speed adjustment process of a servo motor in real time, and calculating a fluctuation index, thereby achieving the self-tuning of the speed ring of the servo motor. And the adjustment effect of the PID controller parameters can be comprehensively evaluated. When the volatility index is smaller than a first threshold value, a linear function is adopted to adjust the initial PID parameter, a first target control parameter is obtained, and therefore the adjusting performance is optimized; and when the volatility index does not reach the standard, performing adaptive adjustment by using a fuzzy strategy to obtain a second target control parameter. By dynamically adjusting PID parameters, external interference or load change can be effectively handled, the problems of over-adjustment, under-adjustment and the like are reduced, and the stability and response speed of the system are improved. The manual debugging workload is reduced, especially in a complex application scene, the system efficiency and stability are remarkably improved, the debugging cost is reduced, and the deployment efficiency is improved.

The invention belongs to the technical field of motors, and relates to a vertical magnetic suspension motor. On the basis of the magnetic bearing control module, the cooling control module and the control evaluation module, closed-loop control of the rotor stably suspending at the target position is achieved, oscillation and steady-state errors are reduced, and the control precision is effectively improved; by dynamically calculating the temperature gradient difference and the cooling power regulation and control value and combining the PID controller and the liquid cooling / air cooling cooperation strategy, on-demand cooling is achieved, the cooling energy consumption is reduced, the temperature balance is improved, and the problems of local overheating or too high energy consumption are avoided; by performing normalized weighted analysis on key parameters such as magnetic bearing energy efficiency and cooling efficiency, feeding back control evaluation coefficients in real time and guiding system parameter optimization, the stability and reliability of long-term operation of the motor are ensured, the failure rate of the motor is reduced, the functional integrity is ensured, and system-level optimization is facilitated.

The invention relates to an intelligent valve control method based on a double-delay depth deterministic strategy gradient. The method comprises the steps that firstly, a regulating valve mathematical model is established, a valve transfer function is determined, and a valve simulation environment is established; then, on the basis of the valve simulation environment, parameters of the regulating valve mathematical model are optimized and adjusted through a double-delay depth deterministic strategy gradient algorithm; and finally, PID valve control is realized based on the optimized and adjusted parameters. Compared with the traditional method, the PID controller optimized and set by using the TD3 algorithm has the advantages that the steady-state error of the system is remarkably reduced, and the target tracking precision is remarkably improved. Through self-adaptive adjustment of reinforcement learning, the system can be matched with a reference target more accurately, error accumulation is reduced, and the stability of precision control is improved. Compared with manual adjustment, the TD3 algorithm can continuously optimize controller parameters according to real-time feedback of the system, so that the control system has higher adaptability and self-adjustment capability.

The invention discloses a pouring machine flow control system based on multi-parameter feedback and PID dynamic adjustment, and relates to the technical field of automatic control of mold pouring, the system comprises a tilting parameter module, a liquid level dynamic modeling module, a flow adaptive control module and an execution mechanism module; the tilting parameter module collects the angular velocity and angle data of a casting ladle in real time, the liquid level dynamic modeling module calculates the liquid level height according to the angular velocity, the angle data and the geometrical shape of the casting ladle, and the flow self-adaptive control module dynamically calculates the current flow value. And the preset flow target and the real-time flow difference value are input into a PID controller to generate a tilting adjustment signal to drive an execution mechanism module to adjust the flow of the casting machine. The liquid level height and the flow are calculated through mechanical motion parameters, the problem that the sensor is in direct contact with liquid in the high-temperature and high-pressure environment and fails is solved, and stable operation in the severe environment is guaranteed; and adaptive PID control and a multi-parameter fusion model are used for cooperative work, so that the steady-state error is reduced, and the response speed is increased.

The invention discloses a pumped storage unit speed regulation control optimization method based on PID mu control of a tracking differentiator. The method comprises the steps that 1, a simulation model is built based on parameters of a pumped storage power station; 2, popularizing a PID control differential item from an integer order to a fractional order, and introducing a tracking differentiator to carry out smooth transition on a control signal; and step 3, optimizing the control parameters by adopting an intelligent algorithm. Through a good point set and a Levy flight improved dung beetle algorithm, an objective function is established through linear weighting of an ITAE index, an overshoot and a steady-state error. And 4, performing simulation verification. And performing simulation verification on each working condition of the pumped storage unit, and verifying a control effect through no-load and on-load rotating speed disturbance simulation under different water heads. The method provided by the invention is suitable for improving the control capability of the pumped storage unit under the background of a novel power system with new energy access, and has important significance for ensuring safe and stable operation of the power system.

The invention discloses an ultrahigh-pressure jet cleaning system with an intelligent adjusting function. According to the system, the control algorithm sub-module is integrated with the PID controller, the fuzzy logic controller and the machine learning model, so that the control algorithm sub-module can realize accurate monitoring and intelligent adjustment of the system operation state, and the efficiency and the stability of the cleaning process are ensured. The PID controller quickly responds to errors, eliminates steady-state errors, reduces system overshoot and oscillation, and improves real-time performance and accuracy of control; the fuzzy logic controller is used for processing nonlinearity and uncertainty of the system, so that the adaptability of the system to complex working conditions is enhanced; and the fault rate and the maintenance cost of the equipment are reduced through predictive maintenance, so that the operation convenience, the operation reliability and the economical efficiency of the system are integrally improved, and efficient and intelligent cleaning experience is brought to users.

The invention provides an electric vehicle wireless charging control system optimization method based on a three-stage particle swarm algorithm, which considers transient performance and steady-state performance in the charging process of an electric vehicle, and divides the charging process into three stages: a starting stage, a tracking stage and a stabilizing stage. The first stage is a starting stage, and overshoot and voltage rising speed are mainly considered; the second stage is a tracking stage, and voltage tracking time and switching loss are mainly considered; and the third stage is a stable stage, and mainly considers steady-state errors and switching loss. Through a particle swarm optimization algorithm, parameters and duration of an optimization controller in each stage are optimized, and voltage fluctuation at a stage switching node is minimized, so that the system can achieve optimal performance in each stage. Compared with a traditional overall control scheme, the performance of the wireless charging system of the electric vehicle can be remarkably improved, the system loss is effectively reduced, and the purposes of high efficiency, economical efficiency and safety are achieved.

The invention relates to a mobile robot control method and system based on fault prediction, and the system comprises a multi-mode inverse kinematics solver which dynamically selects a solving mode according to the condition number of a Jacobian matrix, and carries out the solving of the Jacobian matrix according to the condition number from small to large; respectively selecting a transpose matrix method, a damping least square method and constrained quadratic programming to calculate the joint angle adjustment amount to adapt to bad working conditions such as singular configuration; the self-adaptive integral controller is used for independently adjusting position / attitude ring gain through a decoupling gain matrix, dynamically inhibiting vibration and reducing steady-state errors in combination with errors and fault indexes; and the fault prediction and reconstruction subsystem calculates a joint fault index in real time, and the index integrates torque deviation, temperature, harmonic distortion, vibration and wear data and implements hierarchical response. According to the system, the singular region stability is remarkably improved, the decoupling performance and the fault response speed are controlled, and the secondary fault risk is reduced.

The present invention belongs to the technical field of parameter identification for permanent-magnet synchronous motors. Disclosed are a parameter identification method and apparatus for a permanent-magnet synchronous motor, and a storage medium and a device. The method comprises: using a variable step-size feedback neural network algorithm to solve a voltage equation and a d-q axis current discrete state equation, which take cross-coupled inductance into consideration, of a permanent-magnet synchronous motor in a dq coordinate system, so as to obtain intermediate parameters generated in the process of constructing the d-q axis current discrete state equation; and performing calculation on the basis of the intermediate parameters, so as to obtain the stator resistance, the d-axis inductance, the q-axis inductance, the cross-coupled inductance and the permanent-magnet flux linkage of the permanent-magnet synchronous motor, and thereby completing parameter identification. The present invention takes cross-coupled inductance into consideration and uses the variable step-size feedback neural network algorithm, such that the computational load is extremely small, the degree of complexity is reduced, rapid and effective identification can be realized, and errors can be reduced; in different convergence time periods, the algorithm adjusts the step size on the basis of the magnitude of steady-state errors, such that identification results approach stable values more quickly, thereby improving the speed and stability of convergence.

The invention discloses a self-adaptive control method based on a large-inertia fan, and belongs to the technical field of PID control optimization, and the method specifically comprises the steps: S1, constructing a large-inertia fan control system; s2, improving a mathematical model in a standard tornado optimization algorithm; s3, using an improved tornado optimization algorithm to optimize control parameters of a speed loop PID controller in the large-inertia fan control system, and obtaining a group of optimal PID control parameters Kp, Ki and Kd through optimization of the algorithm; and S4, applying the group of optimal PID control parameters obtained in the S3 to a speed loop PID controller in the large-inertia fan control system, and outputting a corresponding control quantity to realize speed control of the large-inertia fan control system. The speed loop PID controller in the large-inertia fan control system is optimized through the improved tornado optimization algorithm, the adaptive capacity of the PID controller is enhanced, the steady-state error in the fan operation process is effectively reduced, the control precision is improved, and the robustness of the large-inertia fan control system is improved.

The invention discloses an integral adaptive admittance control method and system, a computing control device and a storage medium. The method comprises the steps that 1, a target admittance model is established and initialized; step 2, establishing an adaptive admittance controller according to an adaptive control strategy based on environmental information identification, and deriving a reference trajectory corresponding to the target admittance model; step 3, establishing an integral admittance controller, performing steady-state error optimization processing on the reference trajectory, and generating an optimized reference trajectory; and step 4, generating a robot motion instruction according to the optimized reference trajectory. According to the method, firstly, an integral controller and online environment parameter identification are deeply fused, steady-state errors are eliminated through integral terms, dynamic environment changes are compensated through parameter identification, and stability collaborative optimization is carried out. And secondly, a reference trajectory is dynamically adjusted by introducing a force error integration link, so that the stability contradiction between integration control and an adaptive law is solved, and the unification of high-frequency response and lightweight control is realized.

The invention discloses a cooperative robot joint adaptive control system and method based on multi-sensor fusion, and relates to the technical field of robot joint control. According to the invention, through an improved extended Kalman filtering and deep neural network compensation mechanism integrated by the multi-source data fusion unit, sensor noise is effectively suppressed, and multi-source data conflicts are solved; the sensor reliability evaluation unit is based on a dynamic weight distribution mechanism of Bayesian reasoning, and can reduce the fusion weight of the sensor in real time when the sensor is detected to be abnormal, and improve the contribution degree of reliable sensor data. The adaptive control unit is combined with the self-learning optimization unit to form a closed-loop optimization architecture, so that the performance limitation caused by fixed traditional PID parameters is thoroughly solved; through adaptive weighted fusion of multi-source sensor data, the joint state estimation precision is improved; and a deep neural network residual error compensation mechanism is utilized to reduce a torque control steady-state error.

The invention provides a self-adaptive cross damping flutter suppression method for blades of an offshore wind turbine, and belongs to the technical field of wind power generation based on computer data processing. The method comprises the following steps: firstly, establishing an aeroelastic model containing a non-diagonal damping item, and coupling sinking and floating motion and pitching motion through a cross damping coefficient; secondly, designing a self-adaptive control law based on a differential Riccati equation, and dynamically adjusting a damping parameter to compensate the time-varying aerodynamic force; and finally, proving closed-loop stability through a Lyapunov theory, and verifying a control effect by utilizing numerical simulation. An external pneumatic executing mechanism is not needed, the structure is simple, response is fast, the vibration convergence time can be shortened to be within 4 seconds at the wind speed of 8 m / s, the steady-state error approaches zero, and the method is suitable for flutter suppression of the blades of the large offshore wind turbine.

The invention relates to a clamping force constraint backstepping sliding mode control method for an electronic mechanical braking system, and belongs to the related technical field of new energy automobile control. The method comprises the following steps of: establishing a state space model of the system by considering the nonlinear characteristic of the electronic mechanical braking system, and reasonably simplifying the state space model into a strict feedback form; a time-varying tangent barrier Lyapunov function (BLF) containing an error integral term is constructed, an exponentially decayed time-varying function is used as a constraint boundary of a clamping force tracking error, the integral term is introduced to eliminate a steady-state error caused by unmodeled dynamics, and the output constraint performance and tracking precision of a system are ensured; on the basis, a control law is designed by combining a backstepping method and non-singular fast terminal sliding mode control, and fast and accurate tracking of the braking clamping force is achieved; and proving the system stability based on the Lyapunov stability theorem. According to the invention, the braking response time and the clamping force control precision of the electronic mechanical braking system can be effectively improved.

The invention relates to the technical field of electronic component reliability engineering and environmental engineering, and discloses an electronic component environmental adaptability reliability prediction and life evaluation system and method, and the system comprises an environmental parameter collection module, a reliability prediction module, a life evaluation module, and a data storage and interaction module. According to the invention, a multi-dimensional sensor array including temperature, humidity, vibration, electromagnetic radiation and salt mist concentration is adopted by an environmental parameter acquisition module, multi-physical field stress parameters are synchronously acquired, and common environmental interference factors of electronic components are covered; a Kalman filtering algorithm is built in the data preprocessing unit to suppress environmental noise, the noise suppression ratio is increased by 40%, then abnormal values are eliminated through the 3 sigma criterion, and the data integrity reaches 99% or above; besides, the stress simulation sub-module realizes single / multi-stress coupling simulation through a temperature control cabin, a vibration table and the like, and ensures that the simulation stress steady-state error is less than or equal to 1% in combination with PID feedback control, so that extreme environment data of a laboratory can be supplemented, and the problem of insufficient field data is solved.

The invention provides a PID self-tuning vacuum pressure adjusting device. The PID self-tuning vacuum pressure adjusting device comprises a human-computer interface, an external interface, a control unit, a pressure acquisition module, a PID self-tuning unit, a power execution unit and a valve, the PID self-tuning unit realizes a pressure adjustment function, is different from a conventional PID adjustment algorithm, has the capability of automatically correcting PID parameters, outputs a valve position adjustment value, controls the pressure to quickly approach a target position, and reduces steady-state errors. The power execution unit receives the target valve position, and a driving motor drives a speed reducer to run to the target position; the valve department controls the through-flow area of the pipeline so as to adjust the pressure value of the pipeline. By the adoption of the device, for the vacuum pressure adjusting technology with hysteresis quality, the pressure of the cavity can be adjusted more accurately, steady-state errors are smaller, fluid waste caused by fluctuation is reduced, equipment can be separated from a PLC to operate independently according to a preset target value, flexibility is higher, and the equipment cost can be reduced.

The invention discloses an automatic regulation and control method and device for a valve of a heat supply pipeline and a medium, and relates to the field of automatic control over a heat supply system.The method comprises the steps that the opening value of the valve of a target heat supply pipeline is obtained in real time, and according to the opening value, the heat difference and the heat difference change rate of the target heat supply pipeline are predicted through a fitted polynomial regression model; converting the predicted heat difference and the predicted heat difference change rate into corresponding membership values through a membership function, and obtaining a total membership value of a preset fuzzy set; performing matching in a fuzzy rule base based on the membership value, and determining a fuzzy value; performing defuzzification on the fuzzy value according to the total contribution and the total membership value of a preset fuzzy set to obtain a specific value of the valve adjustment amount; and adjusting the control parameters of the automatic controller based on the specific value. And by continuously optimizing automatic control parameters, the steady-state error of the system is reduced, the control precision of the system is improved, and it is ensured that the heat supply effect is more stable.

The invention belongs to the technical field of direct-current synchronous motor control, and particularly relates to a PMSM improved sliding mode control system and method based on a two-section type dynamic boundary layer, in the aspect of rotating speed control, a rotating speed ring terminal sliding mode controller is introduced, related items of load torque and mechanical angular speed are combined into total disturbance, a nonlinear terminal sliding mode surface is selected, and the total disturbance is calculated. The problem that nonlinear disturbance cannot be effectively processed through traditional PI rotating speed control can be solved, finite time convergence of PMSM speed tracking errors is achieved, and meanwhile system buffeting is effectively reduced. In the aspect of current control, an improved sliding-mode observer is obtained according to a two-section quasi sliding-mode surface and an exponential reaching law design based on a terminal saturation function, so that the observation steady-state error of dq-axis back electromotive force is effectively eliminated, high-frequency buffeting in a sliding-mode reaching stage is effectively reduced, and the steady-state and dynamic performance of a system under electrical parameter mismatch is improved.

The invention belongs to the technical field of active magnetic suspension bearings, and discloses a single-cycle delay compensation method and device for a magnetic suspension bearing switch power amplifier, and the method comprises the steps: building a unipolar single-cycle control mathematical model, and calculating the duty ratio of a single cycle; setting a state switching criterion of a charging and discharging period, and judging a charging and discharging state of a single period; and establishing a linear duty ratio prediction model, calculating a linear derivation duty ratio dt and a calculation duty ratio dj to obtain a total duty ratio of a corresponding switch tube in a period, and outputting the total duty ratio to the corresponding switch tube. According to the method, target tracking can be achieved under the static condition, steady-state errors do not exist, and the problem that time delay exists in a traditional digital single-cycle control algorithm can be effectively solved under the dynamic condition.

According to the flow control system and method for the filter element flow resistance performance test provided by the invention, the self-adaptive PID control closed loop taking the real-time flow resistance coefficient as the core feedback quantity is constructed, and the high-precision sensing network and the intelligent decision algorithm are integrated; the defect that traditional manual or simple PID control is difficult to adapt to filter element characteristic change and environmental interference is successfully overcome; specifically, the system dynamically collects flow, pressure difference and temperature signals and calculates the flow resistance coefficient of a filter element in real time, PID control parameters (proportion, integration and differential terms) are adjusted in a self-adaptive mode according to the flow resistance coefficient, meanwhile, the reference value of a sensor is periodically calibrated so as to eliminate drift errors, and an electric control valve is driven to rapidly and accurately respond; according to the method, the dynamic stability and static precision of flow control are remarkably improved, multi-source interference such as pipeline pressure pulsation and fluid temperature change is effectively compensated, the steady-state error of the test flow is greatly reduced, and finally the efficient and high-precision automatic test of the flow resistance performance of the filter element is achieved.

The invention relates to the technical field of electric power simulation, and discloses a hardware-in-the-loop simulation-oriented new energy equipment model verification method and system, and the method comprises the steps: constructing a new energy equipment basic model based on a physical equation, carrying out the compensation of a dynamic working condition error through combining with an LSTM neural network, and generating a hybrid parameterized model; fitting voltage and current data through a particle swarm optimization algorithm, and dynamically adjusting impedance parameters to complete model calibration; a time domain step response test, frequency domain characteristic analysis and a voltage sag fault experiment are executed on a hardware-in-loop platform, and the performance of the model is verified in a multi-dimensional mode; and generating a standardized verification report containing a steady-state error and a simulation step length index based on the deviation between the dynamic response curve and the measured data. According to the method, a mechanism model and a data driving technology are fused, the problems that dynamic characteristic adaptation is insufficient and parameter calibration depends on experience in traditional verification are solved, and the simulation precision and verification efficiency of the model under complex working conditions are improved.

The invention provides an anti-integral saturation permanent magnet synchronous motor dead-beat predictive current control system and method, and relates to the technical field of motor control. The integral separation type PID controller is introduced to replace a traditional PID controller, integral accumulation is restrained when errors are large, so that the integral saturation phenomenon is avoided, the integral adjustment effect is achieved to eliminate steady-state errors when the errors are small, and the dynamic performance and the response speed of the system are remarkably improved. Meanwhile, the current control effect of the motor is further optimized in combination with the fast dynamic response characteristic of dead-beat predictive current control. The system comprises key components such as an integral separation PID controller, a dead-beat prediction current controller, a delay module, a coordinate transformation module and a driving module. The objective of the invention is to solve the problems of steady-state errors and integral saturation of traditional dead-beat predictive current control under the influence of factors such as motor parameter mismatch, time delay and inverter nonlinearity.

The invention discloses a permanent magnet synchronous motor model-free sliding mode control method based on a novel reaching law, belongs to the field of power electronics, and aims at solving the problems that traditional sliding mode control is large in buffeting and high in steady-state error and depends on an accurate model. According to the method, based on a model-free theory, a permanent magnet synchronous motor rotating speed ring super-local model is constructed, then a novel reaching law containing a dynamic parameter delta = e-x beta is designed, and the contradiction between buffeting suppression and quick response is solved; meanwhile, designing a novel sliding-mode observer, and estimating an unknown part g of the system to realize disturbance feed-forward compensation; and finally, a q-axis current reference value control law is obtained through derivation in combination with a sliding mode surface and a mechanical motion equation. The method can weaken the buffeting of the sliding mode, improves the anti-interference performance and the steady-state precision of the system, guarantees the finite time convergence of the state, and is suitable for the control of the permanent magnet synchronous motor in the fields of aerospace and new energy automobiles.

The invention discloses a zero-sum game-based two-degree-of-freedom helicopter safety controller design method, which relates to the technical field of two-degree-of-freedom helicopter control, and comprises the steps of constructing a system equation, introducing an attack signal and a preset performance function, decomposing into two stages of subsystems, and designing a virtual controller, an adaptive law and a self-triggering mechanism. And solving a Hamiltonian-Jacobian-Exophone equation by using a zero-sum game theory, and finally verifying the stability of the system through a Lyapunov function. According to the invention, it can be ensured that the tracking error converges to the steady-state error interval within the predetermined time, and the transient and steady-state performance of the system is significantly improved; and meanwhile, a Nussbaum function and a zero-sum game strategy are introduced to improve the attack resistance, the communication burden and actuator abrasion are reduced through a self-triggering mechanism, and the sesame effect is avoided.

The invention discloses a mechanical arm self-adaptive variable impedance constant force control system which comprises an inverse kinematics unit, a fractional order active disturbance rejection controller unit, a mechanical arm and a self-adaptive variable impedance controller unit, and the input of the system is a tail end expected position and expected contact force. According to the method, the force overshoot is remarkably reduced, the steady-state error approaches zero, and the force tracking precision and robustness are improved.