468 results about "Tracking error" patented technology

The invention relates to the technical field of multi-axis servo systems, and discloses a synchronous control method, device and equipment for a multi-axis servo system, and the method comprises the steps: measuring a mechanical coupling relation parameter, a motor dynamic characteristic parameter and a sampling delay time parameter of the multi-axis servo system, and substituting the parameters into a state equation modeling formula, calculating to obtain a discrete time system state matrix; performing Z transformation processing on the discrete time system state matrix and calculating frequency response characteristics to obtain a transfer function matrix and a target coupling axis pair set; executing hierarchical optimization to obtain a preliminary control instruction; a trajectory tracking error and an inter-axis synchronization error are calculated and substituted into the optimization objective function for sampling delay compensation processing, and a compensation control instruction is obtained; the compensation control instruction is processed in real time, synchronous control signals are output to the servo shafts to achieve multi-shaft coordinated movement, and the synchronous control precision in the multi-shaft servo system is improved by accurately recognizing the key coupling shaft pairs and the sensitive frequency points.

The invention relates to the technical field of air pressure intensity adjustment, and discloses a pressure control method and device for accurately adjusting air pressure intensity, and the method comprises the steps: carrying out the digital conversion and temperature and humidity compensation of an original pressure signal collected by a pressure sensing array, and obtaining a pressure data matrix and a pressure gradient matrix; calculating a pressure distribution mass center and a pressure fluctuation frequency spectrum to obtain a pressure space distribution characteristic matrix and a pressure time domain characteristic matrix; inputting a three-state Markov model for dynamic state identification to obtain a pressure state transition probability matrix and a pressure response prediction matrix; constructing an objective function containing a pressure tracking error term and an organization safety constraint term, and solving to obtain a PID control parameter matrix and a pressure safety limit matrix; and constructing a three-stage control system comprising a basic controller, an interference compensator and a safety protector, and outputting an air pressure regulation driving signal. According to the method, high-precision pressure sensing control is realized.

The invention discloses a data-based predefined time heterogeneous multi-agent formation collision avoidance method. The method comprises the following steps: establishing a nonlinear heterogeneous multi-agent system; designing a bimodal artificial potential field function to perform dynamic obstacle avoidance and prevent regional escape; establishing a self-adaptive robust controller used for generating an obstacle avoidance safety motion trail of the root leader; designing a self-adaptive formation zooming mechanism of the leader, and constructing a predefined time affine observer of the follower based on the self-adaptive formation zooming mechanism; designing a unified obstacle function; constructing a virtual control law for processing tracking errors based on the unified obstacle function; designing a neural network estimator; designing controllers of the leader and the follower according to the virtual control law; and forming a collision avoidance decision of the heterogeneous multi-agent formation based on a self-adaptive robust controller, a predefined time affine observer, a neural network estimator and controllers of the leader and the follower. According to the method, safe, efficient and robust cooperative control of the formation in a complex environment is realized, and the safety and task execution efficiency of the heterogeneous multi-agent formation in a limited and unknown environment are remarkably improved.

The invention discloses a self-adaptive logarithmic terminal sliding mode control method and device based on observation, and relates to the technical field of sliding mode control. According to the method, the terminal attractors are nested into the natural logarithmic function, terminal sliding mode control and logarithmic sliding mode control are effectively combined, rapid convergence can be achieved near and far away from the balance point, the global dynamics of the sliding mode surface is improved, and the sliding mode control efficiency is improved. Meanwhile, a self-adaptive logarithmic terminal sliding mode controller is constructed on the basis of super-spiral sliding mode control and self-adaptive gain, the self-adaptive gain is updated according to a self-adaptive law, the self-adaptive law is constructed on the basis of a preset nonlinear obstacle function, intelligent self-adaption of the super-spiral sliding mode control gain is achieved, and therefore sliding mode variables are restrained within a preset boundary; and the interference estimation error is quickly converged based on the estimation of the observer on the interference, the global quick convergence of the state tracking error is realized under the constraint of the sliding mode variable, the steady-state precision is high, the chattering phenomenon is weakened, and the convergence efficiency and the control precision are improved.

The invention discloses a self-adaptive safety control method of a nonlinear MIMO system under intermittent DoS attack, which comprises the following steps: establishing a nonlinear MIMO system model suffered from intermittent DoS attack, and the system model is in a strict feedback form and comprises an unknown nonlinear function and bounded external disturbance; the DoS attack causes periodic interruption of a communication link from a sensor to a controller, so that actual measurement data is unavailable in an attack activation period; designing a switching type adaptive state estimator; coordinate transformation is carried out by introducing a filter, and a system error is decomposed into a tracking error and a dynamic surface filtering error; a Lyapunov function is constructed; performing stability analysis in stages; designing an adaptive law to update unknown parameter estimation values of the FLS; and a final event triggering controller is designed, and the event triggering controller is combined with a virtual controller to track an error compensation term and an FLS approximation term. The problem that the state cannot be measured is solved.

The invention discloses an AUV second-order sliding mode three-dimensional trajectory tracking control method based on a preset time disturbance observer, and belongs to the field of underwater vehicle control, and the method comprises the steps: building an AUV mathematical model containing external disturbance; designing a preset time interference observer by using a preset time stability theory; according to the dynamic model and the tracking error of the AUV control system, fusing preset time stability and a second-order sliding mode technology, and constructing a preset time second-order sliding mode trajectory tracking controller; and connecting the constructed interference observer and the trajectory tracking controller to the dynamic mathematical model of the AUV, and verifying the dynamic mathematical model of the AUV control system. According to the method, the preset time method is introduced, so that the AUV can quickly track the given expected trajectory, the accurate control of the three-dimensional trajectory of the AUV is realized, and the stability of the control performance of the system under the interference of external ocean current is ensured.

The invention relates to the technical field of mechanical arm control, in particular to a self-adaptive fuzzy fixed time control method based on an output-limited mechanical arm, and the method comprises the following steps: S1, constructing a mechanical arm dynamic model; s2, setting constraint conditions of an output state of the mechanical arm dynamic model; s3, defining a tracking error and a virtual control law of the mechanical arm dynamic model; s4, on the basis of output state limitation and fixed time control, an actual control law is designed to serve as input of the mechanical arm dynamic model, and an adaptive law is constructed; and S5, a three-level logarithmic obstacle Lyapunov function is constructed, the three-level logarithmic obstacle Lyapunov function is combined with the virtual control law, the actual control law and the self-adaptive law step by step and substituted into the virtual control law, the actual control law and the self-adaptive law, and then it is guaranteed that the output state of the mechanical arm dynamic model does not exceed the preset safety boundary according to the actual fixed time control lemma. According to the invention, high-precision trajectory tracking can be realized on the premise of ensuring safe operation of the mechanical arm.

The invention discloses an underwater vehicle tracking control method with state constraint under input saturation. The method comprises the following steps: firstly, constructing an AUV control system containing model uncertainty and unknown interference based on a kinematics model and a dynamics model of the AUV, and obtaining a reference trajectory of a tracking target; establishing an error model of the AUV control system in combination with the error conversion function, and obtaining a design variable related to the position tracking error; meanwhile, deducing a design variable related to a speed tracking error; constructing a fixed time integral sliding mode lumped disturbance observer based on a design variable related to a position tracking error; and finally, combining a fixed time integral sliding mode lumped disturbance observer and design variables related to a speed tracking error to construct a trajectory tracking control system, and designing a fixed time convergence sliding mode controller by using a continuous input signal and a position and speed adaptive law. According to the invention, the tracking error is stabilized to zero in a fixed time; and continuous control input is realized.

The invention discloses an aircraft self-learning optimization control method based on adaptive dynamic programming, and the method comprises the steps: firstly, designing a basic stability controller under a backstepping method frame in order to guarantee the stable control of an aircraft under the uncertain and unknown external disturbance of a model; then, considering that the tracking performance of the system has a weakening trend along with disturbance change, introducing an ADP-based parameter self-adjustment intelligent optimization algorithm to design an auxiliary controller, and obtaining a compensation control signal by executing and evaluating updating adjustment of a network weight along with tracking deviation so as to further reduce the tracking error; and the self-learning and self-adaptive capabilities of the controller to environmental changes are realized. And finally, integrating the basic controller and the auxiliary optimization controller to finally obtain a complete robust self-learning optimization controller. The method is high in universality and high in calculation precision, and has important engineering practice reference significance.

The invention provides an optimal trajectory tracking control method and device for a nonlinear motor servo system, and relates to the technical field of artificial intelligence, and the method comprises the steps: building an error dynamic model for tracking control of a motor system, introducing a new state variable, converting the new state variable into an augmented system model, and carrying out the tracking control of the motor system; determining a description form of an optimal tracking control problem under the constraint of a state inequality; based on the augmented system model, establishing an identification model of an unknown dynamic system by using a function approximation method; estimating unknown parameters in the identification model by using online data and historical data through a parallel learning method; and based on the identification model, updating control input through an iterative algorithm, and solving an optimal tracking control problem under the constraint of the state inequality to obtain an optimal tracking control strategy. By adopting the scheme, the method does not depend on an accurate model of the motor system, and the tracking error and the control energy consumption in the limited time domain are minimized while the condition that the state of the motor system meets the existing constraint is ensured.

The invention discloses an intelligent numerical control machine tool contour error prediction compensation system and a control method thereof, and belongs to the technical field of industrial control and intellectualization, the intelligent numerical control machine tool contour error prediction compensation system comprises a parameter setting module used for setting system parameters and processing parameters of a numerical control machine tool and importing external planning data and configuration files; the online planning module is used for acquiring planning data online; the core control module is used for controlling the executing mechanism to realize multiple motion modes; the data monitoring module is used for dynamically collecting real-time monitoring data in the machining process of the executing mechanism; the neural network prediction module is used for performing tracking error prediction on the plurality of processing single axes based on a neural network model; and the error intelligent control module is used for carrying out iterative optimization on the processing track. Through modular design, all links of numerical control machining are integrated into a unified system, so that the technical problems that a control system of a traditional numerical control machine tool is poor in function expansibility, low in resource utilization rate, high in development threshold, tedious in process and the like are solved.

The invention discloses an ODE-PDE-based formation control method and system, and belongs to the field of multi-agent cluster control, and the method comprises the steps: building a heterogeneous multi-agent system model of a to-be-controlled system, and calculating an ODE tracking error system model of each agent, and an ODE-PDE error system model shared by the sparse-dense distributed intelligent agents is obtained through partial continuous processing. And at any moment, fitting a nonlinear function of a sparse distribution intelligent agent by using the first RBF neural network, calculating a control signal, substituting the control signal into a corresponding ODE tracking error system model, fitting a nonlinear function of a dense distribution intelligent agent by using the second RBF neural network, calculating a control signal, and substituting the control signal into a corresponding PDE tracking error system model. If the ODE-PDE tracking error system converges, the formation control is ended; otherwise, continuing to update the control signal and enabling the entity object to execute the corresponding action. According to the invention, the cooperative control of the multi-agent system can be effectively utilized to realize the formation control of any scale and any distribution system.

The invention discloses a non-cooperative target-oriented ocean robot cluster tracking optimization control method, which comprises the following steps of: firstly, respectively establishing a linearized ocean robot cluster dynamic model and a non-cooperative target dynamic model for an ocean robot system working in a water surface environment; then constructing a state tracking error system; designing a finite time sliding mode control protocol, and solving an equivalent state tracking error system moving on a sliding mode surface; a performance index function of the ocean robot cluster system is defined, a distributed state feedback nominal optimization control protocol is designed, and it is ensured that the performance index function meets global Nash equilibrium; and finally proving that the distributed state feedback nominal optimization control protocol can ensure the asymptotic stability of the equivalent state tracking error system by utilizing a Hurwitz stability theory. According to the method disclosed by the invention, the optimal tracking control of the ocean robot cluster system on the non-cooperative target with an uncertain control strategy can be realized under the interference of water surface storm waves in a complex ocean environment.

The invention discloses a double-capacity water tank control method based on multi-target reward Q learning, and belongs to the technical field of machine learning and automatic control. The method comprises the following steps: firstly, establishing a discretization mathematical model of a double-capacity water tank liquid level control system, and designing a multi-target reward function; secondly, empirical data are stored only when the liquid level tracking error exceeds the limit or the control quantity suddenly changes, and sampling weights are distributed according to award absolute values; and finally, updating the Q table based on the time difference error, and performing closed-loop real-time control on the double-tank water tank according to the dynamically adjusted PID parameter. According to the method, a five-dimensional reward function is designed, and multi-index dynamic balance is realized through linear weighting; the empirical tuple is stored only when the liquid level tracking error exceeds the limit, and the quick response requirement of the dynamic time-varying scene of the double-capacity water tank is met; dimensionality reduction is performed on a continuous state space by adopting a non-uniform grading strategy, so that the calculation complexity is remarkably reduced; a perception-decision-execution integrated framework is constructed, and the dependence of a traditional control method on a mathematical model of the water tank is abandoned.

The invention relates to a trajectory tracking optimization control method for a two-connecting-rod mechanical arm with an incomplete measurable state, and belongs to the technical field of control engineering. The method utilizes a preset performance function and an event triggering mechanism to implement event triggering model prediction optimization control: establishing a mathematical model of a two-connecting-rod mechanical arm system, and obtaining a state-space equation of the system according to the mathematical model; designing an extended state observer, setting a preset performance function, adjusting parameters of the preset performance function to meet transient performance requirements, and performing error conversion on an output tracking error according to the transient performance requirements; designing an event triggering model predictive controller based on a preset performance function; solving an optimization problem to obtain an optimization control input variable and a state variable; and designing a self-adaptive time-varying trigger mechanism to reduce the optimization times of predictive control. The method integrates the advantages of a preset performance control method, an event triggering mechanism and a prediction control method, has the advantages of high response speed, high positioning precision, small calculation amount and the like, and has relatively good tracking performance and control flexibility.

The invention discloses a preset performance asymptotic control method for a hydraulic servo system with control signal unknown dead zone compensation. The preset performance asymptotic control method comprises modeling of the hydraulic servo system with the control signal unknown dead zone and the preset performance asymptotic control method with the dead zone compensation. Firstly, in order to weaken the influence of an unknown dead zone, a smooth inverse dead zone model is introduced, and parameterization processing is carried out to facilitate control design; secondly, a decreasing constraint is applied to the tracking error by defining a preset performance function; a parameter adaptive law is further designed, and in combination with a robust technology based on interference upper bound estimation, it is ensured that the system has presettable performance and asymptotic stability under bounded time-varying disturbance. According to the control method, the preset performance and the asymptotic stability of the hydraulic servo system containing the control signal unknown dead zone under the time-varying interference are achieved for the first time.

The invention relates to the technical field of multi-agent control, in particular to a second-order multi-agent system control method based on dynamic event triggering, and the method comprises the steps: building a kinetic model of a leader-follower second-order multi-agent system considering unknown external interference, and designing a distributed fixed time observer on the basis, so as to achieve the control of the leader-follower second-order multi-agent system. State information of the leader is estimated in real time; introducing a fixed time extended state observer to estimate external disturbance; proposing a sliding mode surface based on the observation error; a distributed fixed-time sliding mode consistency tracking control protocol is designed, so that tracking errors are accurately converged within fixed time, an ideal control effect can be achieved even if external interference exists, the communication burden is effectively reduced through introduction of a dynamic event triggering mechanism, and the sesame phenomenon is avoided; tracking consistency control of the multi-agent system can be realized in a fixed time, the robustness and reliability of the system are improved, and communication resources are saved.

The invention discloses an unmanned agricultural machine path tracking preset performance control method and system, and the method comprises the steps: collecting the pose information of an unmanned agricultural machine, building an unmanned agricultural machine path tracking error kinematic model, and designing a finite time preset performance function based on the unmanned agricultural machine path tracking error kinematic model. The constraint is converted through nonlinear mapping; based on the Lyapunov stability theory, adopting a backstepping method to recursively design an actual control law, and according to the actual control law, performing inverse transformation on the input of the unmanned agricultural machine tracking control system in the error kinematics model of the unmanned agricultural machine tracking control system to obtain the front wheel steering angle of the unmanned agricultural machine. According to the method, convergence is completed within finite time, and transient control performance and steady control performance of a path tracking control system are considered.

The invention relates to the technical field of optimal tracking control, and provides an optimal tracking control method, device and equipment based on adaptive dynamic programming. Constructing an augmented system according to a tracking error and a reference trajectory of the system (the system is constrained by part of unknown disturbance), and designing a corresponding value function; based on the designed value function, the H infinity tracking problem is converted into a zero sum game, and a corresponding Hamilton-Jacobi-Isaacs (Hamilton-Jacobi-Isaacs, HJI) equation is constructed; and the evaluation-execution-disturbance network approximates the HJI equation, the optimal control strategy and the worst disturbance strategy are solved, and the optimal control strategy is applied to the controller. According to the method, the control input and the disturbance input during neural network training are updated by using a double-event triggering mechanism, and the resource utilization efficiency is improved. According to the optimal control input data, external disturbance factors suffered by the controlled system are considered, so that the robust performance of the obtained optimal control input data is improved.

The invention relates to the technical field of auxiliary driving, in particular to a front-wheel steering and rear-wheel distributed driving vehicle path tracking method. The method comprises the following steps: S1, establishing a nonlinear vehicle dynamics model of front-wheel steering and rear-wheel distributed driving; s2, establishing a path tracking task constraint; s3, designing an adaptive robust controller based on task constraints; s4, based on Lyapunov analysis, stability and constraint following error performance of the adaptive robust controller are analyzed; and S5, using the controller in the step S3 to control the vehicle to perform path tracking. According to the path tracking control algorithm provided by the invention, a model which better conforms to the actual dynamic characteristics of the vehicle is established, and the condition of relatively large initial tracking deviation is considered and processed, so that the control stability of the vehicle under the influence of uncertain factors and relatively large initial tracking deviation can be improved, and a tracking error sample is limited in a specified area.

The invention discloses a VSG control method for oscillation instability, and relates to the technical field of wind power control, and the method comprises the steps: determining the sequence impedance of a VSG grid-connected system according to a VSG inverter structure and a control circuit model; generating and analyzing a Bode diagram according to the sequence impedance of the VSG grid-connected system and the equivalent impedance of the power grids with different intensities to obtain a phase margin set; when at least one phase margin which is corresponding to the power grid intensity meeting the first preset condition and is a negative value exists, voltage and current tracking errors are calculated; calculating a current inner loop control rate according to a sliding mode surface function corresponding to the integral sliding mode surface, the voltage and current tracking errors and an exponential reaching law; when it is judged that the sliding mode surface function meets a second preset condition, an actual current measurement value is obtained; when the actual current measurement value does not conform to a third preset condition, the VSG grid-connected system sequence impedance is deduced again until no negative phase margin exists in the phase margin set; the steady-state error can be effectively eliminated, and the control performance is improved.

The invention provides a three-order hydraulic servo position control system active-disturbance-rejection control method based on friction compensation, and relates to the technical field of fluid transmission and control, and the method comprises the steps: S1, building a state space model of a three-order hydraulic servo position control system, building a linear expansion state observer, and obtaining a linear active-disturbance-rejection controller of the three-order hydraulic servo position control system; s2, nonlinear time-varying disturbance compensated by the third-order hydraulic servo position control system is converted into a tracking error compensation item controlled by a friction compensation algorithm; s3, verifying the stability of the closed-loop third-order hydraulic servo bit control system through error convergence before and after feed-forward compensation; and S4, the control voltage of the third-order hydraulic servo position control system is input, and nonlinear friction feedforward compensation control of the third-order hydraulic servo position control system is achieved. According to the method, a model-free control strategy is adopted, an innovative solution path is provided for friction suppression and self-adaptive friction compensation, and the control precision and robustness of the hydraulic servo system are improved.

The invention discloses a permanent magnet synchronous motor control method of an adaptive fractional order fast terminal sliding mode. The method comprises the following steps: firstly, constructing a mathematical model of the permanent magnet synchronous motor in a d-q axis rotor rotating coordinate system; then designing a fractional order fast terminal sliding mode surface, and solving an equivalent control law of sliding mode control based on the designed fractional order fast terminal sliding mode surface; performing adaptive design on the switching control gain and the boundary layer thickness to obtain an adaptive switching control law; and integrating the equivalent control law and the adaptive switching control law to obtain a total control law. Compared with existing surface-mounted permanent magnet synchronous motor sliding mode control, the surface-mounted permanent magnet synchronous motor sliding mode control method introduces a fractional order fast terminal sliding mode surface on the basis of a traditional integral sliding mode so as to reduce system buffeting and ensure that tracking errors converge in finite time, and meanwhile designs a self-adaptive control law of switching control rate gain and boundary layer thickness. The uncertainty influence of external disturbance is counteracted, and the robustness of the system is improved.

The invention belongs to the field of automatic control, and particularly relates to a nonlinear system dynamic gain global trajectory tracking control method based on a self-adaptive neural network observer, which comprises the following steps: converting an uncertain nonlinear system model with unknown interference according to the requirement of output feedback control to obtain a first nonlinear system model; enabling an unknown nonlinear continuous function in the first nonlinear system model to be expressed as a function only containing an output signal and other system state estimation values; performing approximate approximation on an unknown nonlinear continuous function in the first nonlinear system model by using the first radial basis function neural network vector to obtain a second nonlinear system model; designing a second radial basis function neural network vector to construct a dynamic gain state observer of the second nonlinear system model, and obtaining an estimated value of a system state and an estimated value of an unknown nonlinear continuous function in the model; according to an inversion control method, defining a dynamic system tracking error index containing an intermediate virtual control signal, designing a Lyapunov function, and obtaining a change rate of a dynamic gain, a weight adaptive update rate of a second radial basis function neural network vector, and a control rate of the intermediate virtual control signal and a system input signal; different from most existing nonlinear system control methods, the method combines a novel dynamic gain neural network observer with an inversion controller with dynamic gain, and provides a robust global trajectory tracking control solution for a dynamic system operating under uncertain conditions.

The invention relates to an iterative learning control method for a non-repetitive time-varying system average operator, and the method comprises the steps: constructing a discrete dynamic model which allows system parameters to change in a non-repetitive manner along with the time and the number of iterations, and generating an expected trajectory and a tracking error of dynamic truncation; performing dynamic truncation processing on the randomly changed track length through random variables of Bernoulli distribution to generate an optimization correction error signal; constructing a variable track length average operator based on weighted average and correction error signals of historical control input, designing an iterative learning control law, and verifying the convergence of the iterative learning control law; and finally, the tracking precision and robust stability of the system under the variable trajectory length and variable initial state are verified through simulation. The method breaks through the limitation of traditional iterative learning control on hypotheses such as a fixed system model and a fixed test length, solves the problem of tracking failure of a non-repetitive time-varying system caused by parameter drift and trajectory abrupt change, does not need to depend on an accurate model or a large amount of data training, and has algorithm conciseness, real-time performance and interpretability.

The invention belongs to the technical field of unmanned ship control, and particularly discloses a control method of a fixed time event triggering terminal sliding mode with preset performance constraints. The control method comprises the following steps: S1, establishing an all-drive model of a multi-under-actuated unmanned ship system; s2, designing a fixed time disturbance observer; s3, constructing a cooperative tracking error system according to the cooperative control target and the interactive topological structure; s4, restraining and converting the cooperative tracking error by using a preset performance function, and updating a cooperative tracking error system; and S5, constructing a relative threshold event triggering mechanism, designing a distributed event triggering-fixed time terminal sliding mode controller, generating an actuator control instruction, and realizing cooperative tracking control of the system through an output instruction of an actuator. According to the method, the problem of fixed-time cooperative tracking control under the preset performance constraint is solved, two indexes of'rapid cooperative control 'and'communication resource saving' are realized at the same time, and the method is applied to a cluster scene of multiple unmanned ships.

The invention discloses an electro-hydraulic servo system finite time accurate tracking control method and system. The method specifically comprises the steps that a mathematical model of a single-rod electro-hydraulic position servo system is established; based on a multilayer feedforward neural network, constructing a finite time neural network extended state observer, and estimating matching and non-matching unknown function disturbances and system states suffered by the single-rod electro-hydraulic servo system; constructing a finite time accurate tracking control algorithm of the single-rod electro-hydraulic servo system for model uncertainty compensation, and designing a finite time multilayer neural network adaptive law with a fractional order; and an initial value of a neural network weight parameter, an adaptive law matrix and a controller parameter are selected to realize the compensation of the uncertainty of a system model, so that the output of the system tracks an expected control target. According to the method, the steady-state tracking error of the single-rod electro-hydraulic servo system controller is reduced in finite time, the uncertainty compensation capability of the model is improved, and the tracking precision and the tracking speed of the single-rod electro-hydraulic servo system are improved.

A finite time dynamic mechanical arm control method based on a speed reconstruction strategy comprises the steps that firstly, a mechanical arm system physical model containing joint torque is constructed, joint tracking errors are defined, and a mechanical arm system state space model is obtained; 2, constructing a finite-time high-bandwidth gain speed estimator, defining a state estimation error, and obtaining an estimation error dynamic equation; 3, defining an auxiliary state to obtain an auxiliary state dynamic equation; 4, constructing a finite time non-recursive adaptive controller; and 5, obtaining a final input torque of the controlled system, and outputting the final input torque as a joint torque. According to the method, through the processes of error modeling, state estimation, auxiliary conversion, control law design and closed-loop feedback, the dynamic tracking problem of the mechanical arm is converted into the finite time convergence problem of the error state, meanwhile, dynamic gain and robust compensation are introduced, the adaptability of a system to parameter uncertainty and external disturbance is improved, and the system reliability is improved. And a high-precision and high-robustness control target is realized.

A cracking furnace outlet temperature multi-step compensation predictive control method with a time-lag characteristic belongs to the field of advanced control of an industrial process and comprises the following steps: step 1, modeling a cracking furnace outlet temperature control system, and establishing a state space model combining a linear model and an unmodeled dynamic state; 2, establishing an expanded incremental model; 3, designing a robust time-delay prediction controller according to the time-varying and time-delay characteristics of the system; and 4, designing an unmodeled dynamic multi-step compensation controller. A brand new scheme is provided for designing a controller of a cracking furnace outlet temperature control system with uncertainty, strong nonlinearity and time-varying and time-lag, and system tracking errors are eliminated as far as possible by combining data driving and a multi-step compensation principle, so that the system can better track a set value, and meanwhile, the long-term operation capability of the system is improved.

The invention discloses a two-degree-of-freedom prediction control method of an LCL filtering type three-phase grid-connected inverter system, and belongs to the field of power electronic converter control. A prediction control reference instruction is obtained by sampling parameters of inverter side current, capacitor voltage, network side current and power grid voltage at the moment k, and a continuous and discrete model of an LCL filtering type three-phase grid-connected inverter system is established; designing a control function based on a grid-side current reference tracking error and a gradient thereof, and establishing a prediction model; constructing a prediction cost function based on a control function, and obtaining a prediction control reference instruction by using a minimum value of the prediction cost function; and determining an optimal voltage vector and an action duty ratio according to the prediction control reference instruction, generating a control pulse signal according to a symmetrical five-section pulse mode, and acting on each switching tube in the inverter to realize prediction control of the LCL filtering type three-phase grid-connected inverter system. The method is simple in step, convenient to use, constant in switching frequency and good in stability.