483 results about "Lyapunov stability" patented technology

The invention relates to a method of taking over and controlling attitude after a space tethered robot captures a flexible target satellite. Taking regard of nondeterminacy of inertia, the coupling effect, external interference and other factors, the method of taking over and controlling attitude after space tethered robot captures flexible target satellite establishes a complex attitude and orbit coupling kinetic equation, designs inner and outer ring terminal sliding mode controllers, and gives consideration on the saturation characteristics of a thruster and a tether so as to stably control the attitude and angular velocity of the complex. The method of taking over and controlling attitude after a space tethered robot captures a flexible target satellite includes the following steps: establishing an attitude kinetic equation of a complex after a space tethered robot captures a target satellite; designing inner and outer ring terminal sliding mode controllers and the corresponding adaptive law; taking the inner ring control law and the outer ring control law as the input of a control system to take over and control the attitude after the flexible target satellite is captured; and proving Lyapunov stability. The method of taking over and controlling attitude after a space tethered robot captures a flexible target satellite can solve the problem that the flexible complex parameters are not determined and the self-supplied thruster of the space tethered robot is saturated.

The invention discloses a method for controlling output feedback of a motor position servo system. The method comprises the following steps that a mathematic model of the motor position servo system is established; an extended state observer is designed, and the state of the system and interference in the mathematic model are observed; a second-order low-pass filter is designed so that an error system of the motor position servo system can be established, and an output feedback controller is designed according to the error system; stability certification is conducted on the motor position servo system according to the Lyapunov stability theory, and a result of the global asymptotic stability of the system is obtained according to the Barbalat lemma. According to the method for controlling output feedback of the motor position servo system, considering uncertainty such as external interference, the extended state observer conducts estimation, compensation is conducted during design of a controller, and therefore the robustness of the actual motor position servo system to external interference is improved; in this way, the problems of a high-frequency dynamic condition, measurement noise and the like caused by high-gain feedback are greatly relieved, so that the shadowing property of the system is improved, and the motor position servo system can be applied to practical engineering more conveniently.

A method and system suitable for automated surveillance of intensive care unit patients for information denoting likelihood of in-hospital survival or mortality, represented in the timeseries of scoring systems such as APACHE III. Techniques from digital signal processing and Lyapunov stability analysis are combined in a method that allows for optimization of statistical hypothesis testing that is robust against short time series of as few as five time points. Once optimized, the method and system can achieve high-sensitivity high-specificity classification of survivorship, while avoiding false-positive prediction of mortality.

The invention discloses a motor position servo system control method taking an input time lag into consideration, and belongs to the field of electro-servo control. The method comprises the following steps: establishing a motor position servo system mathematic model taking the input time lag into consideration; designing an expansion state observer for observing a system state and interference in the mathematic model; designing a non-linear output feedback robustness controller for compensating the input time lag; and carrying out stability analysis on a motor position servo system taking the input time lag into consideration by use of a Liapunov stability theory, and proving that the system obtains half-global consistent and bounded stability. By use of an output feedback control method based on expansion state observer, non-linearity including external interference and the like is estimated through the expansion state observer and is compensated in controller design, such that the actual robustness of the motor position servo system for the external interference is improved, the influence exerted by speed measuring noise on system performance is overcome, and application in actual engineering is facilitated.

The invention discloses a robust neural network control system for a micro-electro-mechanical system (MEMS) gyroscope based on sliding mode compensation and a control method of the control system. The control system comprises a given trajectory generation module, a sliding mode surface definition module, a neural network controller, a weight adaptive mechanism module, a sliding mode compensator, an MEMS gyroscope system, a proportional-differential control module, a first adder and a second adder. The control method of the control system comprises the following steps of: establishing an MEMS gyroscope kinetic model based on a sliding mode surface, designing a controller structure, and designing an updating algorithm of a radial basis function (RBF) network weight, so that the trajectory of the MEMS gyroscope is tacked. By the control method, the influence of the unknown dynamic characteristic of the MEMS gyroscope and noise interference can be compensated on line, the vibration trajectory of the MEMS gyroscope completely follows a reference trajectory, and the anti-interference robustness and reliability of the system are improved; the updating algorithm of the network weight is designed on the basis of a Lyapunov stability theory, so that the stability of a closed-loop system is ensured; and a powerful basis is provided for expanding the application range of the MEMS gyroscope.

The invention discloses a preset performance control method for an uncertain under-actuated unmanned surface vehicle (USV) system. The preset performance control method specifically comprises the steps of establishing a USV dynamic model, including kinematics and dynamics models; processing tracking error constraints by proposing a preset performance control method; processing a virtual controllerby applying the dynamic surface control technology; learning the uncertain dynamics of the system online by using the neural network technology; and designing a tracking controller that meets the requirements by applying the Lyapunov stability theory. The preset performance control method of the invention can avoid the singular problem of controller design and can satisfy the pre-specified tracking error transient and steady state performance. The uncertain dynamics of the system are learned online by applying the adaptive neural network control technology and the controller is designed to complete the trajectory tracking control objective and the practicality of the control method is improved.

The invention provides a security control method of an event-driven network control system under a multi-network attack. Considering the network transmission security problem, a random network spoofing attack, a replay attack and a DoS attack are sequentially introduced, and a system closed-loop model under the multi-network attack is established. Further considering the constraint of the limitednetwork bandwidth, an event trigger mechanism is introduced to screen and release sampling data meeting a preset event trigger condition to the network for transmission, thereby establishing a new switching system model on the basis of the system closed-loop model. Based on a switching system model, a Lyapunov stability theory is utilized to obtain a sufficiency condition for enabling the system to meet the stability of a mean square index, and a group of linear matrix inequalities is solved to obtain the gain of the state feedback controller. Compared with an existing controller design method, safe and stable operation of the network control system under the influence of multi-network attacks is guaranteed, meanwhile, the data transmission frequency is reduced, and limited network bandwidth resources are saved.

The invention discloses a permanent magnet linear synchronous motor slip form control system based on a linear expansion state observer, and belongs to the technical field of linear motor control. The system comprises the following steps that: firstly, establishing the dynamic equation of a permanent magnet linear synchronous motor on a two-phase synchronous rotation orthogonal coordinate system; secondly, simplifying the dynamic equation into a special two-order integral series type mathematical model; thirdly, designing the linear expansion state observer to obtain a disturbance estimated value, and considering the estimated value in the design of a slip form control law to eliminate a chattering phenomenon; and finally, applying a Lyapunov stability theory to analyze the stability of the system. The system has the most important characteristics that the state and the disturbance of the system can be accurately estimated by the linear expansion state observer. In addition, the control system is high in robustness, and a given displacement signal can be accurately tracked. In addition, the chattering phenomenon of slip form control can be greatly improved so as to be suitable for designing the permanent magnet linear synchronous motor servo control system.

The invention discloses a motor position servo system self-adaptive control method based on an interference observer. The motor position servo system self-adaptive control method based on an interference observer includes the steps: establishing a mathematical model for a motor position servo system; constructing an interference observer and a self-adaptive controller based on the interference observer; and by means of a Liapunov stability theory, performing stability demonstration on the motor position servo system, and obtaining a global asymptotic stability result of the system by operatinga Barbalat lemma. The motor position servo system self-adaptive control method based on an interference observer is based on an integration series model and the interference observer of the motor position servo system, designs a non-linear control method based on the interference observer, integrates the non-linear control method with self-adaptive control, respectively estimates unmodeled interference and parameter nondeterminacy to enable the servo system to achieve the effect of global asymptotic stability when unmodeled interference is time-varying interference for the servo system, and solves the strong parameter nondeterminacy problem and the strong nondeterminacy non-linear problem of the system so as to enable the system to obtain better tracking performance.

The invention provides an adaptive inversion nonsingular terminal sliding mode control method of a micro gyroscope. An inversion method and a terminal sliding mode are combined, an inversion terminal sliding mode control law is designed according to the Lyapunov stability theory, and therefore, system status can be converged to a balance point within the short limited time. Furthermore, by means of self-adaptive control, angular velocity and other system parameters of the micro gyroscope can be identified. Nonsingular terminal sliding mode control is introduced in consideration of singularity in the existing terminal sliding mode control, whole nonsingular control of the system is achieved, response speed of a controller is completely comparable to the traditional terminal sliding mode, and therefore, the adaptive inversion nonsingular terminal sliding mode control method has important theoretical and practical application value. By means of the adaptive inversion nonsingular terminal sliding mode control method of the micro gyroscope, the rate of convergence and tracking performance are ensured while strong robustness and adaptive ability to external disturbance are achieved.

The invention discloses an on-line identification and control method for a parameter of an alternating current position servo system model. According to the invention, a model reference adaptive identification algorithm based on a Lyapunov stability theory is utilized; and on-line identification is carried out on a rotary inertia J and a viscous damping coefficient B of a controlled object of an alternating current servo system; after convergence is carried out on an identification parameter, an on-line design on a position controller is carried out according to J and B values and the design is automatically switched to position control. According to the invention, an efficiency on an alternating current servo system design can be substantially improved.

The invention discloses a fault-tolerant control method for a networked control system with time-varying delay. In view of conditions of parameter perturbation, time-varying delay, external disturbance and random failure happening to an actuator, a discrete time closed-loop nonlinear networked control system model is firstly built, a Lyapunov-Krasovskii function with the delay information is then built, a Lyapunov stability theory and a linear matrix inequality analysis method are used, sufficient condition for asymptotic stability of the nonlinear networked control system and existence of an H-infinity fault-tolerant controller are obtained, a Matlab LMI toolbox is used for solution, and a gain matrix for a non-fragile fault-tolerant controller is: K=K<->P; and conditions for optimizing the minimum disturbance rejection rate gamma is given, and the controller gain matrix K<*> optimized under the minimum disturbance rejection rate gammamin= square root of e is acquired. The condition of time-varying delay existing in the system is considered, the time-varying delay is analyzed and processed based on a free-weighting matrix method, and the conservation is reduced.

The invention discloses a self-adaptive fuzzy control method for an active power filter based on feedback linearization, relates to the technical field of an active power filter, and respectively designs independent self-adaptive controllers aiming at a current loop and a voltage loop. As the current loop adopts the self-adaptive fuzzy controller for the active power filter based on the feedback linearization, the feedback linearization theory, fuzzy control, sliding mode control, self-adaptive control and Lyapunov stability theory are comprehensively utilized, and on the basis of ensuring the global stability, the purpose of harmonic compensation is realized. As the voltage loop adopts the self-adaptive fuzzy PI (process integration) control, according to the fuzzy rule, the control strategy can automatically adjust the PI parameter, strengthen the robustness of the system, improve the dynamic performance of the system, and ensure the tracing to the command voltage.

The invention aims at an intermittent process with uncertainty, and proposes a constrained 2D tracking control method for the uncertainty intermittent process. The constrained 2D tracking control method comprises the steps of: firstly, designing an iterative learning control law for a given system dynamic model; secondly, converting the original system dynamic model into a 2D-FM closed-loop systemmodel expressed in the form of a predictive value according to a 2D system theory and the designed iterative learning control law through introducing state errors and output errors; and finally, giving a sufficient condition expressed in the form of a linear matrix inequality (LMI) for ensuring robust asymptotic stability of a closed-loop system and an expression form of the optimal control law according to a designed infinite time domain performance index and a Lyapunov stability theory. According to the constrained 2D tracking control method, numerical values of tracking errors under the control of the constrained 2D tracking control method are smaller, and the convergence is faster; more importantly, the control input does not drastically fluctuate and only require slight adjustment, thereby being conducive to resource conservation and reducing troubles caused by frequent operations.

The invention discloses an electronic throttle valve adaptive backstepping control method based on a Romberg sliding-mode observer, and relates to the technical field of automobile electronic control. Firstly, in order to solve the problem that the change of the opening degree of an electronic throttle valve can not be measured, the change is estimated through the Romberg sliding-mode observer and based on the electronic throttle valve state equation; secondly, approaching is conducted on the nonlinear unknown quantity-gear clearance torque through the approaching characteristic of an RBF neural network; finally, a control law, an RBF network weight renewing law and an interference adaptive law are designed based on the Lyapunov stability theory and through combination with the nonlinear backstepping control method. By means of the method, the problem that nonlinear factors and some parameters easily change over time during electronic throttle valve control can be well solved, and the control effect and dynamic response performance are further improved.

The invention relates to a power-reaching-law-based sliding-mode controlled magnetic suspension train system control method. Based on a single magnet structure magnetic suspension system model, and onthe basis of performing linearization on the input and output relation, the power-reaching-law-based sliding-mode controlled magnetic suspension train system control method analyzes the control relation of the application of sliding-mode control of the power reaching law in the single magnet structure magnetic suspension system, and utilizes a Lyapunov stability theory to analyze the stability ofthe method. The Matlab simulation result shows that compared with traditional sliding-mode control, the sliding-mode control of the power reaching law has better control performance, and has the advantages of significantly weakening buffeting, being smaller in the positional signal error, being better in dynamic characteristic, and being higher in robustness.

The invention relates to an inverse oscillation adaptive control method of a fractional-order arched MEMS resonator, and belongs to the field of inverse oscillation control. In the design process, uncertain items are compensated through a Chebyshev neural network with updated single weights, and the problem of the unknown control direction caused by the excitation characteristic in Caputo fractional-order calculus is solved through the Nussbaum function; meanwhile, a tracking differentiator based on the hyperbolic sine function is designed, and the problem of repeated differential with complexfractional-order calculation in virtual control is solved; then under an adaptive backstepping control frame, through a continuous frequency distribution model, an inverse oscillation adaptive control scheme integrated with the Nussbaum function, the neural network and the tracking differentiator is invented; on the basis of the criterion of the fractional-order Lyapunov stability, the asymptoticstability of a closed-loop system is proven; and finally, the validity of the proposed scheme is verified through numerical simulation.

The invention discloses a self-adaption fuzzy sliding control method for an active filter based on fuzzy approximation, relates to a control system for an active power filter and particularly belongs to application of the self-adaption fuzzy sliding control method on control of the active power filter. The control system doesn't rely on an accurate model of a controlled object, is good in dynamic and static performance, has good self adaption and robustness, and can guarantee stability of the whole system. The system is mainly designed based on fuzzy control, sliding control, self adaption control and lyapunov stability theory, can improve robustness and adaption of the system to non linear load change and system parameter unmatching, and furthermore has capacity on achieving harmonic compensation under complex power grid environment. In addition, the system carries out fuzzy approximation on a switch item of a controller, and a novel self-adaption fuzzy controller is designed to effectively reduce buffeting.

The present invention discloses a gain self-adjustment type supercoiling slip form control method for an electro-hydraulic positioning servo system. By utilizing the information of an existing system model, a model-based feed-forward control law is led into the control algorithm of a traditional supercoiling slip form, so that the system servo accuracy is improved. The gain of a controller is updated in real time based on the self-adapting rule. In this way, the exact boundary of the system modeling nondeterminacy is not required to be apriori known. The conservative property due to the artificially preset control gain related to the above boundary in the conventional algorithm can be avoided. Based on the Lyapunov stability theory, the global stability of the closed-loop system is proved. The tracking error of the system asymptotically converges to be in an arbitrarily small range near to zero within the finite period of time. Meanwhile, the convergence speed and the boundary of the steady-state error can be adjusted through parameters.

The invention discloses a self-regulating error symbol integration robust control method for a direct-drive motor system. The method comprises the steps of establishing a mathematical model of the direct-drive motor system; designing a self-regulating error symbol integration robust controller; carrying out stability certification on the direct-drive motor system by using the Lyapunov stability theory, and acquiring a result of the global asymptotic stability of the system by using the Barbalat lemma. The self-regulating error symbol integration robust control method integrates the idea of self-adaptive control based on a traditional error symbol integration robust control method, and designs a controller gain self-regulating law so as to carry out on-line regulation on an integration robust gain value of the RISE controller. The self-regulating error symbol integration robust control method effectively solves problems of randomness, conservation, limitation and potential high-gain feedback in symbolic function gain regulation of a traditional RISE control method, and acquires better tracking performance.

The invention discloses a self-adaption fuzzy neural compensating nonsingular terminal sliding mode control method of a micro gyroscope. The method mainly involves a nonsingular terminal sliding mode controller and a fuzzy neural network compensating controller; the designed nonsingular terminal sliding mode enables that a system can reach a sliding mode surface and a balance point within a limited time from any initial state, and therefore, the convergence rate and steady stacking precision of the system are improved; meanwhile, the fuzzy neural network compensates the parametric modeling error of the micro gyroscope and outside disturbance effect on line, in order to improve the tracking performance; the fuzzy neural network is practiced on line, the self-adaption learning algorithm of weight of the fuzzy neural is on the basis of the lyapunov stability theorem, which ensures the tracking performance and the stability of the whole control system. The simulation result shows that the method is able to improve the problem of trace tracking of the micro gyroscope and can also effectively inhibit the parameter uncertainty and the influence from the outside disturbance, and as a result, the robust tracking can be realized.

The present invention belongs to the technical field of power system, and relates to a method of time-delay stability criterion. The method adopts the idea of Lyapunov stability criterion; an appropriate Lyapunov functional is selected at first, and is transformed into a group of linear matrix inequalities (LMI) for solution through the Leibniz-Newton formula, relaxation and other means; simultaneously, the redundant items which are generated in the deduction process is eliminated so as to acquire the stability criterion. In the method, the items to be calculated are greatly reduced, and the operating efficiency is correspondingly improved. Moreover, because the time-delay stage widely occurs in various systems, the method is suitable for various environments with the time delay.

The invention discloses a near-space aircraft control system, which belongs to the technical field of aviation aerospace propulsion control. The near-space aircraft control system aims at the attitudestable tracking control problem of a near-space aircraft, and considers the problems of unknown interference and parameter uncertainty. According to the near-space aircraft control system, firstly, an estimated value of an interference observer for interfering the outside world, then the parameter uncertainty of the aircraft is analyzed, and a self-adaptive estimated value of the aircraft parameters is obtained; secondly, an integral sliding mode surface is selected thus a self-adaptive integral sliding mode controller is designed for tracking control of an inner ring attitude angle of the near-space aircraft; and finally, the stability of the controller is proofed by utilizing a Lyapunov stability theory. The near-space aircraft control system can ensure the global stability of a closed-loop system, and enable the system to have good tracking performance and robust performance.

The invention relates to an adaptive RBF (radial basis function) neural network control technique for three-phase parallel active filters, belonging to an active power filter control technique. The invention provides an adaptive RBF neural network control method for three-phase parallel active power filters, which is used for controlling a compensation current output by a three-phase parallel active power filter through a controller, thereby eliminating harmonic waves and improving the power supply quality of a power grid. According to an adaptive control rule provided by the invention, the boundedness of weights is ensured, and the stability of the controller is proved by using a Lyapunov stability theory; and simulation results show that the control method effectively reduces the distortion factor of harmonic waves and is good in dynamic response, and when parameters change, the controller has good robustness and adaptability.

The invention discloses a micro gyroscope self-adaptation inversion control system and method based on a neural network. The control system comprises a reference track module, a middle signal generating module, an inversion controller, a neural network self-adaptation system, a micro gyroscope self-adaptation controller, a first adding device and a micro gyroscope system. According to the micro gyroscope self-adaptation inversion control system and method based on the neural network, the advantages of the inversion design technology are utilized, the design processes of the micro gyroscope control system are simplified, a new path of the design of the micro gyroscope control system is opened up, meanwhile, the characteristics of the control technology of the neural network are combined, the weight parameters of the neural network are adjusted in an on-line and real-time mode, an updating algorithm of network weights is designed based on the Lyapunov stability theory, and the stability of a closed-loop system is ensured. According to the micro gyroscope self-adaptation inversion control system and method based on the neural network, unknown dynamic characteristics and influences of noise interference of the micro gyroscope can be compensated in an on-line mode, two-shaft vibration tracks of the micro gyroscope are made to track the reference track, and meanwhile the reliability and the anti-interference robustness of the system are improved.

The invention relates to an automatic lane changing layered control system and method of an intelligent electric automobile. The layered control system is provided with a lane changing track programming and a lane changing track tracking, wherein dynamic programming of an intelligent electric automobile lane changing track is realized by the lane changing track programming through a manner of polynomial fit; the lane changing track tracking comprises an upper layer controller and a lower layer controller. The method comprises the following steps of obtaining a vehicle pose and road condition information through a vehicle data acquisition unit, so as to perform the programming of the lane changing track according to information of the pose of a current vehicle, the pose of a front vehicle and the like; and performing the lane changing track tracking; estimating the deviation between an actual pose of the intelligent electric automobile and the required expectation pose of a lane changing expectation track; establishing a deviation kinematics model of automatic lane changing of the intelligent electric automobile; based on the estimating deviation and the actual pose of the intelligent electric automobile, designing the upper layer controller based on a Lyapunov stability theory, determining a vehicle expectation speed V<c> and an expectation front wheel steering angle delta <c>, constructing an adaptive-blur PID controller, and determining a motor torque T required by a tracking expectation speed.

The invention discloses a fractional-order adaptive inverse fuzzy sliding mode control method for a microgyroscope. The method comprises a step of establishing a dimensionless dynamic equation mathematical model and a reference trajectory model of a microgyroscope system, and a step of constructing an inversion adaption fuzzy sliding mode controller based on a fractional order. According to the method, the real-time tracing of a target by the microgyroscope can be achieved, the robustness of the system is increased, and good performance still can be maintained under the condition of external interference. A fractional order adaptive law is designed based on a fractional order sliding mode surface, a self-adaptive identification method is designed based on a Lyapunov stability criterion, various unknown system parameters of the microgyroscope is estimated in an online way in real time, compared with an integer order, adjustable items are added, and a control effect and a parameter estimation effect are improved. A fuzzy system approaches upper bound values of a parameter uncertainty and an external disturbance total number, through the fuzzy approximation of the upper bound values,switching terms in the sliding mode controller can be continuous, and buffeting can be greatly reduced.

An adaptive control system (ACS) uses direct output feedback to control a plant. The ACS uses direct adaptive output feedback control developed for highly uncertain nonlinear systems, that does not rely on state estimation. The approach is also applicable to systems of unknown, but bounded dimension, whose output has known, but otherwise arbitrary relative degree. This includes systems with both parameter uncertainty and unmodeled dynamics. The result is achieved by extending the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input / output data. The network weight adaptation rule is derived from Lyapunov stability analysis, and guarantees that the adapted weight errors and the tracking error are bounded.

The invention discloses a micro-gyroscope control system based on a neural network. The micro-gyroscope control system comprises a control system which is in connection communication with a micro-gyroscope, wherein the control system comprises a nominal controller and a neural network compensator. The control scheme of a feedback controller based on a micro-gyroscope nominal model and the neural network compensator is applicable to control of the micro-gyroscope, so that the tracking effect and robustness of the micro-gyroscope control system are improved. The adaptive law of a weight value of the neural network is designed based on a Lyapunov stability theory, so that the stability of the whole control system can be guaranteed under the condition of presence of a modeling error of the neural network.

The present invention discloses a flexible spacecraft sensor fault adjustment method, belonging to the field of aerospace flight control technology. A fault estimation module and a fault tolerance controller are mainly employed by the flexible spacecraft sensor fault adjustment method, and the method comprises the steps that: a filter and an adaptive observer are employed to form a fault estimation module; fault estimation information and sensor output signals are employed to establish the fault tolerance controller through combination of adaptive integral sliding mode control technology; through adoption of the Lyapunov stability theory, the asymptotic stability of a system is proved in a condition of generation of sensor faults; and finally, a simulation experiment result proves the validity of the method provided by the invention. The fault tolerance control problem when sensor measurement offset faults are generated in an operation process of a flexible spacecraft is solved so thataccurate control of an attitude angle in the sensor fault condition is achieved and the system has a high tolerance capability for the faults; and moreover, the method fully takes the consideration of disturbances generated by spacecraft parameter uncertainty and flexible accessories in the design process and is suitable for engineering application.