343 results about "Tracking error" patented technology

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 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 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 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 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 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 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 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.

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.

The invention discloses a numerical control system feed-forward compensation method based on an edge controller, and the method comprises the steps: enabling the edge controller to obtain a machine tool instruction and a machine tool reality of a numerical control system in real time, calculating a tracking error, calculating a feed-forward compensation amount through an algorithm, transmitting the feed-forward compensation amount to the numerical control system, and installing a PLC function module in the numerical control system. And transmitting the received feed-forward compensation quantity to a servo feed-forward channel through a functional block, thereby realizing the feed-forward compensation function of the numerical control system. Through cooperation of edge calculation and a numerical control system, a feed-forward compensation algorithm is decoupled from a main controller, and high-precision real-time compensation is achieved through the parallel calculation capacity of an edge module. The design not only solves the contradiction between computing power and delay of a traditional system, but also supports multi-source data fusion and adaptive algorithm deployment through an open architecture, and provides a brand new path for performance improvement of a numerical control system.

The invention discloses a multi-agent-based virtual power plant layered voltage coordination control system and method, and relates to the technical field of coordination control, and the method comprises the steps: obtaining the original operation data of a distributed energy unit in a virtual power plant, and constructing a dynamic mapping model representing the relation between node voltage and control input; decomposing the dynamic mapping model into a fast dynamic sub-model and a slow dynamic sub-model; generating a fast loop control law based on the fast dynamic sub-model, and performing residual calculation and online correction on the slow dynamic sub-model according to the fast loop control law; establishing a global optimization objective function according to the corrected slow dynamic sub-model, and solving a reference value of each node through a distributed algorithm; issuing the node reference value to a local inverter, and generating a control input signal in combination with a fast loop control law; according to the method, the problems of voltage oscillation, tracking errors and slow convergence caused by virtual power plant fast and slow control conflicts are solved through fast and slow dynamic layering cooperative control.

The invention belongs to the technical field of EL system control, and discloses a preset performance control method considering dynamic and constraint of an execution mechanism. The method comprises the following steps: firstly, establishing a system dynamics model considering dynamic and input saturation constraints of an executing mechanism; then, designing an asymmetric tubular performance boundary to restrain a system tracking error, and defining a conversion error according to the system tracking error; meanwhile, a disturbance observer based on immersion and invariance is designed, and composite disturbance is estimated. Furthermore, a tubular preset performance controller is designed by adopting a backstepping method, a virtual control law and an expected torque are constructed, and a multi-state auxiliary system is introduced to compensate the input saturation influence. And finally, a control instruction is generated by integrating the auxiliary system state, the virtual control law, the expected torque and the interference estimation value, and the control instruction acts on the system after being processed by a saturation function. According to the method, actuator dynamics, input saturation and external disturbance can be processed at the same time, and tracking errors are ensured to strictly follow a preset performance boundary.

The invention discloses a steer-by-wire vehicle trajectory tracking control method based on learning enhancement MPC. The method comprises the following steps: establishing a tracking error model combined with vehicle dynamics; a trajectory tracking controller based on MPC is designed, a tracking error model is used as a prediction model of an MPC algorithm, a prediction sequence matrix is constructed, a target trajectory is tracked, the tracking error approaches 0, and the optimal control quantity is obtained through optimization and solution; the MPC algorithm is enhanced by using a depth deterministic strategy gradient algorithm, a terminal cost function of the MPC algorithm is replaced, and an MPC trajectory tracking controller enhanced by using a DDPG algorithm is obtained through training. According to the method, the DDPG algorithm is utilized, the enhanced MPC trajectory tracking controller is obtained through pre-training and combined training, the vehicle can better track a target trajectory in a complex dynamic scene, the transverse instability risk is reduced, and the driving safety and the riding comfort are improved.

The invention belongs to the technical field of airplane nonlinear system control, and discloses a deicing vehicle hierarchical control method and system under an airplane idling condition. According to the method, a deicing vehicle overall dynamic model under incomplete constraint is constructed based on a Lagrange equation, a vehicle-arm combined trajectory tracking error state model is constructed, a cost function of a model prediction controller is designed according to a control target of deicing vehicle combined trajectory tracking, a quadratic programming problem is constructed on an upper layer to solve an optimal virtual control quantity, and the optimal virtual control quantity is calculated. And the lower layer analyzes the virtual control quantity into control input which can actually act on an execution mechanism by designing a self-adaptive fuzzy logic strategy so as to realize trajectory tracking control. According to the invention, a hierarchical control strategy is provided by combining model predictive control and an adaptive fuzzy logic system (FLS), the stability and high efficiency of system operation are ensured, and the deicing vehicle is ensured to always maintain a stable operation state in a complex and changeable operation environment.

The invention discloses a layered direct trajectory planning method for an unmanned special vehicle in a complex non-structural environment. The method comprises the following steps: 1, acquiring perception information such as obstacles and road boundaries in the non-structural environment and a vehicle starting point state; 2, aiming at anti-rollover and efficient driving requirements in a virtual space, planning and modeling a long sight distance trajectory into an off-line constraint optimal control problem based on pseudo-spectral method numerical solution, and obtaining a global reference trajectory; 3, aiming at tracking error elimination and dynamic obstacle avoidance requirements in a real space, planning and modeling a short-sight-distance trajectory into an online optimal control problem based on model prediction control, and obtaining a local actual trajectory; and 4, inputting the local actual track into a bottom layer control system, and obtaining an end point state as a subsequent planning starting point state. On the basis of meeting common track planning requirements such as obstacle avoidance, the special vehicle can be prevented from rolling over under aggressive working conditions, and meanwhile the passing efficiency is improved.

The invention belongs to the technical field of sliding mode tracking control, and provides an uncertain nonlinear system nonsingular preset time sliding mode tracking control method, which comprises the following steps: establishing an uncertain nonlinear system model, defining a tracking error variable, and establishing an error dynamic model according to the uncertain nonlinear system model and the tracking error variable; based on the error dynamic model, according to a comparison result of an absolute value of the tracking error variable and a preset error threshold value, a piecewise function is adopted to establish a sliding mode surface; setting a preset convergence time upper limit value and a self-adaptive gain value, and determining a reaching law of the self-adaptive preset time according to the preset convergence time upper limit value, the self-adaptive gain value and the sliding mode surface; and establishing a sliding mode controller according to the sliding mode surface and the reaching law, and executing a sliding mode tracking control task by using the sliding mode controller. According to the scheme, parameter design is simplified, setting difficulty is reduced, meanwhile, stable convergence of all variables of a closed-loop system within preset time is ensured, and high-precision tracking of an expected trajectory is achieved.

The invention discloses a finite time preset performance fault-tolerant control method for a two-degree-of-freedom rigid mechanical arm. The method mainly solves the problems that according to a conventional trajectory tracking control scheme, unknown disturbance in the external environment of a system cannot be estimated, disturbance cannot be reduced, and the stability of the system is affected. The method comprises the steps of constructing dynamical models of partial failure and complete failure of an actuator, designing a finite time preset performance function constraint tracking error, constructing a virtual controller and an actual controller based on a backstepping method, introducing an adaptive disturbance observer to compensate unknown external disturbance, and proving system stability through a Lyapunov function. According to the method, high-precision trajectory tracking of the mechanical arm under the actuator fault condition can be achieved, it is ensured that tracking errors converge to a steady-state area within the set time, and meanwhile dependence on external disturbance and system parameter priori knowledge is reduced.

The invention discloses a single-connecting-rod mechanical arm dynamic memory event triggering fixed time control method and system, and the method comprises the steps: describing a controlled system as a nonlinear system with state time lag and input time lag through modeling; introducing coordinate transformation based on a nonlinear system, and defining a tracking error; designing a virtual control law and an adaptive updating law through the defined tracking error; the stability of the control system is ensured by providing a dynamic memory event triggering mechanism, the system comprises a sensor module, a communication module, a control system and a man-machine interaction module, and the dynamic memory event triggering control mechanism in the method is used as a novel nonlinear control strategy, so that network resources and data transmission can be saved, and the system reliability is improved. The designed fixed time control strategy enables the system to achieve stable control within strictly specified time by accurately presetting the convergence time boundary, the response speed is remarkably improved, and the method is particularly suitable for industrial automation scenes with strict requirements for time precision.

The invention discloses a mechanical arm self-adaptive sliding mode control method based on a disturbance observer, and relates to the technical field of mechanical arm control, and the mechanical arm self-adaptive sliding mode control method comprises the following steps: constructing an initial dynamic model based on a Lagrange equation; constructing a simplified two-degree-of-freedom rigid mechanical arm dynamic model based on the initial dynamic model; designing a novel reaching law fusing the advantages of an exponential reaching law and a power reaching law, and combining a hyperbolic tangent function to replace a sign function to construct a sliding mode surface; designing a nonlinear disturbance observer, estimating the total disturbance of the system in real time, carrying out feed-forward compensation, and bringing the composite disturbance influence into a control law; designing a control law and an adaptive law of the mechanical arm through a Lyapun function; the stability of the system is analyzed through a Lyapunov function, tracking errors are proved to be asymptotically converged, and the method is used for ensuring the reliability and stability of the control system. According to the method, the disturbance observer and the self-adaptive sliding mode control strategy are combined, and the control precision and robustness of the mechanical arm when external disturbance and model uncertainty exist are effectively improved.

The invention relates to the technical field of heavy-load robot control, in particular to a self-adaptive robust control method of a heavy-load robot fusing differential homeomorphic mapping. According to the method, position information of each joint is collected in real time and accurately compared with an expected trajectory, a ternary input vector is output, differential homeomorphic mapping parameters are updated in real time through a T-S fuzzy inference system, and a joint space state is mapped to a new coordinate space based on the optimized parameters, so that the position information of each joint is accurately compared with the expected trajectory. Neighbor joint information is obtained through distributed communication to generate a cooperative control item, a sliding mode surface is constructed in combination with a local tracking error to generate a robust control item, a compensation item is generated based on adaptive law estimation parameter uncertainty, three-item parallel control output is formed, dynamic weight distribution and fusion are performed on the three control items, a basic control moment is obtained, and the control precision is improved. And then a feed-forward compensation item based on a nominal dynamical model is superposed to generate a final comprehensive control torque, and the comprehensive control torque optimizes energy distribution while ensuring the performance.

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

The invention relates to the technical field of unmanned aerial vehicle anti-interference control, and provides an anti-interference control method, system and device for a photovoltaic panel cleaning unmanned aerial vehicle, and a medium, and the method comprises the steps: constructing an unmanned aerial vehicle dynamic linearization control model based on the external unknown disturbance of the unmanned aerial vehicle and the dynamic characteristics of a four-rotor unmanned aerial vehicle; respectively carrying out system input control analysis and pseudo partial derivative estimated value control analysis by taking a balance tracking error and an input quantity change as a principle and taking a balance modeling error and a pseudo partial derivative estimated value change as a principle to obtain a corresponding model-free adaptive control law and a model-free adaptive parameter estimation law; and acquiring an external unknown disturbance monitoring quantity based on a preset disturbance observer, and generating a control signal in combination with the dynamic linearization control model of the unmanned aerial vehicle to perform attitude regulation and control. On the basis of model-free self-adaptive control scheme design considering unknown disturbance, the control modeling workload can be reduced, self-adaptive change adjustment can be achieved, the self-adaptive anti-interference and vibration suppression capacity is improved, and the attitude control precision is effectively improved.

The invention discloses a two-way output consistency control method for a heterogeneous multi-agent system, and the method comprises the following steps: 1, constructing state and output equations of a follower agent and a leader agent, calculating an output tracking error ei (k), and constructing an augmented system state space model and a linear control input ui (k); 2, designing a distributed bipartite observer for each follower agent to enable an output tracking error ei (k) to be zero all the time, and replacing a leader agent state of the augmented system state space model with a state of the distributed bipartite observer; 3, defining a Q function, solving the Q function to obtain an optimal control strategy, and introducing a Q-learning algorithm to obtain an optimal control gain only depending on an input state of the augmented system state space model; 4, introducing a fuzzy logic system approximation Q function to enable a matrix phi i to meet a full-rank condition, and updating a fuzzy weight by minimizing a Bellman residual error to obtain an optimal control gain of the input state of the augmented system state space model; and updating the control gain by adopting a gradient descent method, and calculating linear control input ui (k) by utilizing the updated control gain.

The invention discloses an output consistency control method based on a one-dimensional fluctuation partial differential multi-agent system, and relates to the cross technical field of multi-agent system control and partial differential equation application, and the method comprises the steps: constructing a dynamic model, an interference model and a reference signal model of a multi-agent system; constructing a directed communication topological graph including a leader and a plurality of agents; based on the directed communication topological graph and the reference signal model, respectively constructing reference signal observers of the informed intelligent agent and the uninformed intelligent agent; based on the interference model, constructing a local interference observer of the intelligent agent; constructing a conversion function, and converting the dynamical model into a dynamical model under the auxiliary system according to the conversion function; constructing a control law for the dynamical model under the auxiliary system; according to the control law, adjusting the state of each agent under the auxiliary system until the tracking error is zero; according to the invention, the output consistency of the multi-agent system can be realized in a complex environment.

The invention provides an unmanned surface vehicle course tracking control method with signal quantization based on a double-expansion observer, and the method comprises the steps: building a third-order response type unmanned surface vehicle course control mathematical model according to the characteristics of a ship and the sea condition information of a surrounding environment; designing a double-expansion observer model for simultaneously estimating matched interference and non-matched interference in the ship course tracking control system and observing uncertain items and external interference in the ship control system; based on a backstepping method and a dynamic surface control algorithm, a course controller of the unmanned surface vehicle is designed, and the calculation complexity brought by a virtual control law is reduced; based on the Lyapunov stability theory, the stability of the designed unmanned surface vehicle course tracking control system with state quantization and input quantization is proved, all signals in a closed-loop system are consistent and bounded in a semi-global mode, and the tracking error is arbitrarily small by adjusting parameters of a controller.

The invention discloses a composite anti-interference voltage stabilization control method for a solid-state transformer rectifier stage, and the method comprises the steps: constructing a discrete full-order voltage model of the rectifier stage, and defining internal parameter change and external disturbance as variables to be observed, and enabling the variables to be equivalent to sum interference; designing a full-order stabilization control law to convert the model into a closed-loop system taking a tracking error as a state; constructing an intermediate observer, determining the gain of the intermediate observer by solving a linear matrix inequality so as to estimate sum interference on line, and solving an optimal solution of a quadratic cost function based on an optimal control theory so as to obtain a state feedback gain; and forming a composite anti-interference control law in combination with the interference estimation value and the state feedback gain, and inversely transforming the composite anti-interference control law into actual control input. According to the invention, through active observation and feed-forward compensation of interference, the voltage fluctuation of the DC bus is effectively suppressed, and the robustness and voltage stabilization precision of the system under complex working conditions are improved.

The invention discloses a ship rust removal mechanical arm anti-interference motion control method introducing jet flow counter-acting force, and relates to the technical field of ship rust removal equipment control. Aiming at the problems of poor robustness, easy saturation of an integral term and poor dynamic response effect when a ship rust removal mechanical arm is faced with water jet reaction force and other interferences in traditional PID control, the invention provides an active disturbance rejection control (ADRC) scheme introducing jet reaction force. The method comprises the following steps: constructing a jet reaction force model, mapping the jet reaction force model into a joint torque compensation item based on a Jacobian matrix, and integrating the joint torque compensation item to an ADRC control law; an improved tracking differentiator, a nonlinear extended state observer (ESO) and a nonlinear state error feedback (NLSEF) module are designed, real-time estimation and compensation of internal and external disturbance of a system are achieved, and joint control performance is optimized. Experiments show that by means of the method, the joint angular displacement tracking error can be reduced by 10% or above, the adjusting time is shortened to 2 s from 10 s, the absolute value of the transient error is smaller than 0.3 rad, and the anti-interference capacity and the control precision of the mechanical arm are remarkably improved.