40 results about "Output feedback" patented technology

The invention discloses a fast output feedback control method for a networked nonlinear system triggered by a dynamic event. By designing the dynamic gain, the dynamic gain is bounded, tracking error coupling is avoided, and meanwhile, the influence of a nonlinear term containing an unknown quantity and external disturbance on the system is compensated. In addition, a dynamic event triggering mechanism is provided based on the dynamic gain, and dynamic compensation of a triggering threshold value is achieved. And constructing a composite Lyapunov function by recursively constructing a virtual control quantity, and enabling the Lyapunov function to meet a fast finite time stability criterion in combination with parameter design of dynamic gain and nonlinear term power. Therefore, tracking errors are converged to a tight set, and the states of all closed-loop systems and observers are globally bounded. According to the method, rapid finite-time dynamic event triggering output feedback actual tracking control of a non-strict feedback system can be realized, and the method has urgent engineering application requirements and is of great significance in promoting the development of a networked nonlinear system control theory and an engineering technology.

The invention discloses an active defense security control method for a hidden false data injection attack, and relates to the field of information physical system security control, in particular to an active defense security control method. The invention aims to solve the problem that the existing detection method can encrypt a transmitted signal, but is limited to detection of a specific type of attack, or dynamic coupling changing the configuration of a control system can increase the burden of a state estimator, and even causes the performance reduction of the system. The method comprises the following steps of: 1, acquiring output feedback gains of the information physical system under different switching signals; and 2, based on the output feedback control gain obtained in the step 1, performing active attack detection and positioning on the FDI attack, and when the attack is detected and positioned, performing active switching by a controller of the information physical system so as to construct an active security controller aiming at the FDI attack.

A batch process model-free output feedback control method with non-repetitive interference belongs to the technical field of industrial process control, and specifically comprises the following steps: step 1, giving a mathematical description for an optimal tracking control problem of a non-repetitive batch process; 2, designing an optimal control law based on output feedback; 3, a game Q function construction method based on two-dimensional output feedback in the intermittent process; 4, solving a two-dimensional output feedback problem of batch operation by utilizing reinforcement learning; 5, solving an optimal control strategy based on output feedback through iteration to enable the optimal control strategy to be finally converged to an ideal optimal value; according to the method, the problem that a traditional method for processing the complex characteristics of a two-dimensional system is too tedious is solved, through the output feedback control method based on reinforcement learning, the method does not depend on a system model, the influence on the control performance is reduced, and the control effect is greatly improved.

The invention discloses a self-adaptive anti-saturation output feedback control method for a complex system based on gain adjustment, belongs to the field of self-adaptive control, and is suitable for the complex system containing nonlinear and unknown time-varying vectors and bounded external interference. The method comprises the following steps: firstly, acquiring a real-time output signal of a controlled object, and then estimating an unmeasurable internal state of the controlled object by a state observer; an initial control signal is generated based on a dynamic model, an actuator saturation limit and the like, and after optimization of a dynamic gain updating law, whether the signal is close to saturation is judged through monitoring conditions; and when approaching, triggering a planning moment, adjusting the signal amplitude to a safety range by a planning function, generating a self-adaptive anti-saturation output feedback control signal, outputting the final output feedback control signal to an actuator, and circularly executing the steps. The method ensures that a controlled object outputs a stable tracking reference signal, the control signal is always in a saturation limit, and stable operation of the system is realized without a sesame phenomenon.

The application discloses an adaptive tracking control method for a random nonlinear system based on output feedback, comprising the following steps of S1, constructing a mathematical model of a random nonlinear system with hysteresis input, unknown control direction, arbitrary switching and output constraint; S2, designing a linear state observer, and reconstructing unmeasurable states of the random nonlinear system by using output and control input of the random nonlinear system; S3, introducing a Nussbaum type function to process the unknown control direction, and constructing a barrier Lyapunov function to ensure that the output constraint is not violated; and S4, designing a framework based on a backstepping method, combining a radial basis neural network to approximate unknown nonlinear functions, designing a virtual control law, an adaptive law and an actual control law, and realizing adaptive tracking control of the output of the random nonlinear system on a reference signal.

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

The application discloses an output feedback control method for a mechanical arm against random jump interference, firstly establishes a discrete-time mechanical arm Markov jump linear system model, analyzes the discrete-time mechanical arm system model based on the Markov jump process, then designs a state observer and a measured disturbance observer, constructs a compound output feedback controller, estimates the system state to realize state feedback control, and finally guarantees the finite time boundedness and performance of the system through a Lyapunov method and linear matrix inequality technology. The application solves the problem of not considering the influence of the measured interference on the mechanical arm system in the prior art, thereby causing poor stability and control performance of the designed scheme.

The application discloses a complex system adaptive anti-saturation output feedback control method based on gain adjustment, belongs to the field of adaptive control, and is suitable for a complex system containing nonlinear, unknown time-varying vectors and bounded external disturbances. The method first acquires real-time output signals of a controlled object, then estimates unobservable internal states of the controlled object by a state observer; initial control signals are generated based on a dynamic model, actuator saturation limits and the like, are optimized through a dynamic gain updating law, and whether the signals are close to saturation is judged through a monitoring condition; when close to saturation, a planning time is triggered, the signal amplitude is adjusted to a safe range through a planning function, adaptive anti-saturation output feedback control signals are generated, the final output feedback control signals are output to actuators, and the steps are executed in cycles. The method ensures that the output of the controlled object stably tracks a reference signal, the control signals are always within saturation limits, system stable operation is realized, and no Zeno phenomenon occurs.

The application relates to the technical field of inverter control, and proposes an HJI inverter robust control method based on a disturbance observer. The method can effectively control a second-order system in the case of external disturbance, uncertainty and unknown disturbance. In order to eliminate the time delay influence of a remote signal on output feedback during transmission, a second-order disturbance observer is used to identify external disturbance. In addition, an SMC control law is designed according to HJI theory to improve the robustness of the system, and the stability of the system is analyzed by using the second Lyapunov method. Compared with the prior art, the application can effectively improve the control of the second-order system, and verifies the feasibility of the theory on an auxiliary power inverter.

The invention provides a chaotic encryption and control method for an aircraft instruction, and relates to the technical field of information security, and the method comprises the steps: receiving plaintext data of an aircraft control instruction, respectively injecting the plaintext data into state equations of a plurality of chaotic systems, and generating a plurality of paths of key streams; performing internal permutation operation on the multiple paths of key streams by using predefined permutation vectors respectively, and performing external interleaving operation on the multiple paths of key streams subjected to internal permutation by using an interleaving sequence specified by predefined interleaving vectors so as to combine and generate a path of composite key stream; encrypting the plaintext data by using the composite key stream to obtain a ciphertext instruction, and sending the ciphertext instruction to an aircraft control end; and at the control end, decrypting the ciphertext instruction based on a preset permutation vector and an interleaving vector, restoring the plaintext data, and generating an aircraft control signal through an output feedback control algorithm according to the decrypted plaintext data.

The invention discloses an anti-DoS attack and actuator saturation power system memory event trigger insurance cost control method and system, and relates to the technical field of network control systems. Establishing a state-space equation based on the dynamic characteristics of the power system; key influence factors in operation of the power system and external influence factors in a network environment are identified; constructing an output feedback controller based on a dynamic memory event triggering mechanism; a convex hull method is adopted to process saturation nonlinear characteristics of the actuator, a closed-loop system is obtained in combination with the state-space equation, and a guaranteed cost function is designed; and judging whether the closed-loop system meets the requirements of local asymptotic stability and a preset weighted disturbance attenuation level, solving a linear matrix inequality, and obtaining an output feedback controller gain and an event trigger weight matrix. The objective of the invention is to solve the problem that the system stability is difficult to guarantee under the complex conditions that a power system suffers from DoS attacks, actuator saturation exists and external disturbance faces.

The application provides an injection molding system optimal set point tracking control method based on output feedback, relates to the automatic control technical field of injection molding systems, and comprises the following steps: establishing a discrete time state space equation of the injection molding system, which at least comprises a state vector and an output vector; subtracting the output vector from the corresponding set point in a reference trajectory to obtain a tracking error, and constructing an augmented system according to the state vector and the tracking error; constructing an infinite domain cost function based on the augmented system; obtaining a non-minimal state vector based on the augmented system by using a state parameterization method, and defining an output feedback Q function based on the vector; collecting input and output data of the injection molding system to design a zero-error controller, and taking the controller as an initial stable control strategy to execute an output feedback off-policy Q learning algorithm; and through iterative solving, the output feedback Q function is converged to obtain an optimal output feedback control strategy that minimizes the infinite domain cost function, thereby improving control precision.

The invention provides an H infinite dynamic output feedback control method of an SS type IPT system. The method comprises the following steps: constructing an extended description function state space model of the SS type IPT system; an integral error between a reference signal and actual output is introduced on the basis, and an augmented system model is established in combination with external disturbance and measurement noise; based on a Lyapunov function and an H infinity performance index, sufficient conditions of asymptotic stability and disturbance suppression performance of a closed-loop system are deduced and converted into a linear matrix inequality to be solved, so that gain parameters of a dynamic output feedback controller are directly obtained. Under the condition that external disturbance and measurement noise exist, accurate tracking of the reference signal by the system load voltage can still be guaranteed, the robustness and the fault-tolerant capability of the system are improved, meanwhile, dependence on alternating current side variable measurement is avoided, and the method has high engineering application value.

The invention discloses a control method based on a variable gain superspiral sliding mode energy storage converter, which is used for controlling a post-stage converter of the energy storage converter, and specifically comprises the following steps: obtaining a reference value of current through PQ control of a given power reference value, establishing a disturbed state space averaging model of the post-stage converter by adopting a state space averaging method, and obtaining a disturbed state space averaging model of the post-stage converter; a high-order terminal sliding mode observer is designed, a switching function in the observer is improved, a boundary layer variable switching function is introduced, output of the observer serves as feed-forward compensation and is combined with an output feedback control method, a variable-gain super-spiral sliding mode controller is designed, the variable-gain super-spiral sliding mode controller serves as current inner loop control, and a high-order terminal sliding mode controller is designed. SPWM modulation is carried out on the control rate output by the controller to obtain a control signal of a switching tube, and control over the output current of the rear-stage converter is achieved. According to the invention, the buffeting problem which is difficult to avoid by a traditional super-spiral sliding mode controller is solved.

The invention discloses a two-phase flow drying control method and system adaptive to boiler load change, and the method comprises the following steps: collecting boiler load parameters and two-phase flow drying system operation parameters in real time, and constructing an input vector; inputting the input vector to a least square support vector machine prediction model, and outputting an optimal set value adaptive to the current boiler load by establishing a nonlinear mapping relationship between the boiler working condition and the optimal control parameter; according to a preset process security constraint, performing boundary verification and correction on the optimal set value to generate a final control instruction; and the final control instruction is controlled and executed, two-phase flow parameters are adjusted, closed-loop control is formed based on system output feedback, and self-adaptive optimization of the two-phase flow drying process is achieved. According to the method, the least square support vector machine is adopted as a core algorithm, so that the adaptability, the prediction precision and the automation degree of the system are improved, and stable and efficient operation of the two-phase flow drying process under various working conditions is guaranteed.

The invention belongs to the technical field of automatic control and fault tolerance, and particularly relates to a sensor fault compensation control method based on a preset time adaptive neural network dynamic gain observer. According to the method, an integrated framework integrating observer design, fault compensation and preset time control is provided for an uncertain nonlinear system which is unmeasurable in state and has unknown nonlinearity and sensor faults. The method comprises the following steps: constructing an uncertain nonlinear system model with unknown sensor sensitivity, and approaching unknown nonlinear dynamics by using a radial basis function neural network; a self-adaptive neural network state observer based on dynamic gain is designed, simultaneous estimation of the system state and unknown nonlinearity is achieved, and gain over-estimation is avoided; designing a preset time output feedback controller by adopting an inversion control method on the basis of an observer estimation value, and ensuring that all closed-loop signals converge to a neighborhood near an original point within a time predetermined by a user, and the convergence time is irrelevant to an initial condition; and obtaining design requirements of control parameters through Lyapunov stability analysis. According to the method, rapid and accurate trajectory tracking and fault compensation can still be realized under the conditions of sensor faults, model uncertainty and external interference.

The application discloses an optimal output feedback control method suitable for a direct current microgrid, belongs to the technical field of direct current microgrid control, and comprises the following steps: based on a projection theorem, an optimal state filter is designed to eliminate the time correlation of process noise and measurement noise; by means of dynamic programming and a mathematical induction method, the performance index of the system is decomposed into two parts; by means of a state augmentation method, a non-public information set is used to transform the interconnected system; by means of orthogonal decomposition technology, the control input, the state variable, the process noise and the measurement noise of the transformed system are all decomposed into two independent parts; and based on the time correlation noise filter and the system variable decomposition, an optimal output feedback control method suitable for the characteristics of the direct current microgrid is designed. The application effectively improves the control precision and stability of the direct current microgrid in a complex noise environment, solves the information asymmetry problem caused by communication delay, and provides a complete solution convenient for engineering implementation.

The invention belongs to the technical field of nonlinear system control, and discloses a method and a system for introducing composite learning into nonlinear system output feedback adaptive control. According to the method, the existing K-filter in the standard output feedback backstepping control is directly multiplexed to construct the extended prediction error, and an additional observer or a state estimation model (such as a serial and parallel estimation model, a fuzzy observer and the like) parallel to the K-filter does not need to be established. According to the function multiplexing design, the calculation overhead of an extra dynamic system is eliminated, the storage requirement and the real-time calculation burden of the controller are reduced, and the hardware implementation cost and the system debugging complexity are remarkably reduced. Meanwhile, through composite driving of a tracking error and an extended prediction error, accumulated information is continuously introduced by using historical memory of a regression quantity, so that the self-adaption and self-learning capabilities of the control system are remarkably enhanced, and the parameter estimation convergence and the system response performance are improved.

The application provides a predefined finite time control method and system for an island micro-grid, and the method comprises the following steps: establishing a large signal model of a distributed power generation unit (DG) based on an inverter; obtaining a second-order feedback system of a single distributed power generation unit (DG) through input-output feedback linearization; designing a disturbance observer for estimating nonlinear uncertain items of the feedback system; designing a distributed virtual controller based on predefined finite time; and constructing an overall Lyapunov function to verify that all signals are bounded in the distributed power generation units (DG) realized by all designed backstepping control strategies, and the synchronization error converges to a predefined finite time function within a predefined finite time. The application solves the technical problems that the inertia of the DG is small, the IMG is affected by uncertain and intermittent environmental changes, the transient performance index is poor, and the secondary voltage control effect of the IMG is restricted.

The application provides a denitration control method of in-situ NOx actual measurement feedforward and collaborative prediction feedback. The application takes the in-situ NOx concentration at the furnace outlet as a feedforward signal, takes the in-situ NOx concentration at the SCR outlet as a feedback signal, calculates a basic compensation value of ammonia injection based on the in-situ NOx concentration at the furnace outlet, establishes a nonlinear dynamic prediction model and rolls optimization to output a prediction correction amount, takes the set value of the outlet NOx as a target to perform closed-loop fine tuning and output a feedback correction amount, and superimposes the three to generate an ammonia injection adjustment instruction; the method realizes disturbance advance compensation, dynamic optimization and accurate correction, improves the response speed and control precision of the denitration system, reduces ammonia consumption and ammonia escape, adapts to large delay and nonlinear time-varying characteristics, and guarantees that the outlet NOx meets the standard stably.

The embodiment discloses a ship power positioning output feedback control method based on Hamilton theory, obtains state error interconnection and damping distribution passivity control law based on a three-degree-of-freedom port controlled Hamilton model of a ship horizontal plane, obtains auxiliary state variables and disturbance estimation values by constructing a reduced-order extended state observer, estimates and compensates total disturbance in real time, constructs a non-convex constraint operator of a propeller control input, establishes a dynamic auxiliary system to obtain a dynamic auxiliary system state vector, compensates input saturation error by using the dynamic auxiliary system, obtains actual application control instructions after non-convex projection based on an adaptive event triggering mechanism, finally obtains unconstrained ideal control instructions, and obtains actual control input based on the adaptive event triggering mechanism, so that high-precision trajectory tracking and propeller service life extension are realized.

The application discloses a magnetic suspension direct expansion air handling unit control optimization method based on an MPC algorithm. The method collects unit operation state parameters, external environment parameters and target control parameters in real time; a unit dynamic prediction model is constructed based on a mechanism modeling and data driving fusion mode, a mapping relationship between control variables, disturbance variables and output variables is established; a multi-objective rolling optimization function and a device safe operation constraint condition are set; the prediction model is used to predict future working conditions, and an optimal control sequence is solved under the condition of meeting the constraint condition; the first control instruction of the optimal control sequence is executed, actual output feedback is collected in the next period, the prediction model is corrected online based on the actual and prediction deviation, and closed-loop rolling optimization control is realized. Through the construction of the accurate unit prediction model, the application realizes the collaborative optimization adjustment of multiple control variables by combining the rolling optimization strategy, ensures the stable operation of the unit under complex working conditions, and maximally reduces the operation energy consumption and improves the energy efficiency.

The application belongs to the technical field of nonlinear system control, and discloses a method and system for introducing composite learning into nonlinear system output feedback adaptive control. The application directly reuses the existing K-filter in standard output feedback backstepping control to construct an extended prediction error, and does not need to establish an additional observer or a state estimation model (such as a series-parallel estimation model, a fuzzy observer, etc.) in parallel with the K-filter. This functional reuse design eliminates the calculation overhead of the additional dynamic system, reduces the storage requirement and real-time calculation burden of the controller, and significantly reduces the hardware implementation cost and system debugging complexity. Meanwhile, through the composite driving of the tracking error and the extended prediction error, the cumulative information is continuously introduced by using the historical memory of the regression quantity, so that the adaptive and self-learning ability of the control system is significantly enhanced, and the parameter estimation convergence and the system response performance are improved.

The invention relates to the technical field of behavior data credibility evaluation, in particular to a behavior data credibility evaluation system based on time sequence row feature fusion, which is characterized in that an input capture unit captures year and month sub-node input time sequence data of starting and ending time of a project filled by a user in real time; the interrupt monitoring unit detects a system pop-up window and a user active pause as external interrupt, and records an interrupt duration and a continuous filling starting point, and the feature fusion unit performs dynamic time sequence window segmentation and cross-modal alignment; a time sequence behavior feature vector containing a year priority mark, a month lagging time consumption ratio, a cross-year modification time consumption effect difference value and a continued time consumption deviation rate is generated, a credibility evaluation unit judges a behavior mode by means of a four-dimensional decision matrix, a bidirectional threshold comparison and scoring algorithm is detected through a logic fault, a filling behavior authenticity probability value is output, and the filling behavior authenticity probability value is calculated. And the output feedback unit feeds back the probability value to the evaluation system according to sectional risk mapping, so that risk domain differentiation warning is realized, and the credibility evaluation precision is improved.

The invention relates to the technical field of multi-area power systems, in particular to a multi-area power system distributed control method considering market dynamics, which comprises the following steps: establishing a multi-area power system model considering market dynamics, constructing a distributed attack detection estimator based on output feedback, generating a residual signal through the distributed attack detection estimator, and outputting the residual signal. Selecting an evaluation function and a threshold function, constructing a closed-loop multi-region power system model based on the false data injection FDI attack, and constructing a distributed controller based on the false data injection FDI attack; according to the method, the defense capability for the FDI attack is improved, the robustness and the real-time response performance of the system are enhanced, and meanwhile, the distributed controller is designed for the system suffering from the FDI attack, so that the stability and the safety of the system are improved.

This invention discloses a time-varying output formation control method for multi-agent systems under DoS attacks, based on hierarchical asynchronous dynamic event triggering. The method includes establishing dynamic models of the leader and followers; establishing constraints; establishing a DoS attack model; designing a Luenberger observer; designing an adaptive state observer; constructing an output feedback formation controller and establishing a corresponding dynamic tracking error system model; analyzing and proving the observation error of the multi-agent system under DoS attacks; analyzing the formation tracking performance of the multi-agent system under DoS attacks; and verifying its effectiveness in time-varying output formation problems. This invention can estimate the leader's state information in real time and effectively avoids the waste of communication resources and the negative impact of DoS attacks. Based on this observer, combined with output adjustment techniques and state feedback control, the multi-agent system can achieve the desired time-varying formation configuration within a certain error bound.

The invention discloses a discrete nonlinear hysteresis system output feedback control method and system based on a fuzzy state observer, and belongs to the technical field of automatic control. Firstly, a discrete time fuzzy state observer is designed for estimating an unmeasurable state of a system; a hysteresis temporary controller is designed in combination with a dynamic surface control technology, so that complex model conversion on a discrete nonlinear system is avoided; and constructing an implicit inverse compensator, and avoiding explicit construction of an inverse hysteresis inverse model. Under the physical limitation that the system state cannot be directly measured, the fuzzy logic system and the state observer are introduced, and the defect of state information missing is overcome. The fuzzy weight and controller parameters are adjusted on line through an adaptive law, and a plurality of performance indexes such as state estimation errors, tracking errors and control input of the system are comprehensively optimized, so that the system can be adaptive to a dynamically changing operation environment for a long time, and the balance among control precision, robustness and calculation efficiency is realized.

The invention provides an injection molding system optimal set point tracking control method based on output feedback, and relates to the technical field of automatic control of an injection molding system.The method comprises the steps that a discrete time state-space equation of an injection molding system is established, and the equation at least comprises a state vector and an output vector; subtracting the output vector from a corresponding set point in the reference trajectory to obtain a tracking error, and constructing an augmentation system according to the state vector and the tracking error; constructing an infinite domain cost function based on an augmentation system; obtaining a non-minimum state vector based on the augmented system by using a state parameter method, and defining an output feedback Q function based on the vector; input and output data of an injection molding system are collected to design a dead-beat controller, the controller serves as an initial stable control strategy to execute an output feedback strategy Q learning algorithm, an output feedback Q function is converged through iterative solution, an optimal output feedback control strategy for minimizing an infinite domain cost function is obtained, and the control precision is improved.