88 results about "Robust control" patented technology

This invention discloses an asynchronous, non-fragile H∞ control method and system for a rotating inverted pendulum, belonging to the field of robust control technology for DC motor rotating inverted pendulums. Addressing the problems of modal asynchrony, controller gain perturbation, time-varying delay, and quantization error in DC motor rotating inverted pendulum systems, this invention proposes an asynchronous, non-fragile H∞ control framework. The main steps include: constructing a Markov jump singular perturbation system model with time-varying delay and variable quantization density; designing an asynchronous, non-fragile output feedback controller for the rotating inverted pendulum, compatible with controller gain perturbation and quantization uncertainty; and deriving the linear matrix inequality conditions through modal-dependent Lyapunov functions to ensure the system's exponential stability in finite time and to meet the preset H∞ performance index. This invention can effectively improve the robustness and stability of DC motor rotating inverted pendulums under attack and parameter perturbation scenarios.

This invention discloses a method and system for carbon operation control of substations under emission constraints, belonging to the field of substation carbon operation control technology. The method includes: constructing a carbon operation sequence dataset and extracting fluctuation feature vectors from the dataset; analyzing historical carbon operation sequence datasets to construct a dynamic envelope boundary for carbon emissions; identifying the carbon fluctuation propagation topology of the substation based on equipment flow connection relationships and identifying a set of fluctuation source paths; constructing a dual-constraint robust control model, outputting control constraint strategies, and executing the system to implement operational control of the fluctuation source path set. This invention solves the technical problems of existing technologies, such as difficulty in accurately locating carbon fluctuation propagation paths and lack of global and local collaborative constraint control, leading to insufficient stability in carbon operation control. It achieves accurate source tracing of substation carbon operation fluctuations and global and local dual-constraint control, thus improving the technical effect of carbon operation control stability.

The application discloses a fuzzy neural network H∞ yaw moment controller based on phase plane analysis and belongs to the technical field of drive-by-wire chassis and vehicle stability control. The application quantitatively describes the real-time stability boundary in the vehicle driving process by the phase plane analysis technology, realizes accurate evaluation of the overall stability state of the system, designs the yaw moment controller by using the H∞ robust control theory, avoids the system chattering problem from the design level, guarantees the robustness of the control system, simultaneously introduces the fuzzy neural network online approximation controller parameter and the nonlinear mapping relationship between the vehicle driving state, realizes the autonomous optimization of the controller parameters under different working conditions, and thus the driving stability and adaptability of the distributed drive vehicle under the complex driving environment are significantly improved.

This invention provides a leakage-type adaptive space robot control method with self-tuning of reinforcement learning parameters, belonging to the field of space robot control technology. The control method includes: acquiring the current operating state of the space robot system, the control input from the previous moment, and the control parameters from the previous moment, as the current observation state; inputting the observation state into a policy network to acquire the current control parameters; outputting the current control parameters to a leakage-type adaptive robust controller to acquire the current control input; applying the current control input to the space robot system to drive it to perform a task; acquiring the current driving state of the space robot system; evaluating the driving state and acquiring the evaluation result; and adjusting the parameters of the policy network based on the evaluation result. This invention achieves adaptive parameter optimization by online tuning of key parameters in the leakage-type adaptive robust control structure.

The embodiment of the application relates to the technical field of tower vibration suppression, and provides a tower vibration active suppression method and system based on PLC control, real-time acquisition of tower acceleration signals, extraction of window vibration energy and dominant frequency characteristics, construction of a composite evaluation system containing performance degradation and model mismatch dimensions, utilization of index normalization and nonlinear weighted fusion technology, intelligent quantification of urgency of re-identification, triggering of a lightweight online identification and control parameter recalculation process only when system control performance significantly decreases and it is confirmed that the decrease is caused by model parameter drift, otherwise maintaining a low-power consumption monitoring mode, which effectively solves the performance attenuation problem caused by the fact that a traditional fixed parameter controller cannot adapt to the time-varying of tower structure characteristics, avoids excessive occupation of PLC computing resources by a persistent complex algorithm, realizes efficient and robust control of a time-varying structure system on a limited computing power platform, and ensures vibration reduction effect in the whole life cycle of a fan.

This invention discloses a robust control method for robot visual servoing based on the theory of all-drive systems, belonging to the field of visual servoing for robotic arms. The steps are as follows: Taking the eye-to-hand robot visual servoing system as the research object, based on the efficient second-order minimization (EMS) method and combined with the virtual visual servoing method, the target depth and attitude parameters are estimated online to construct a high-precision image interaction matrix; utilizing the all-drive characteristics of robot joint dynamics, the image feature error is mapped to joint space error through the interaction matrix, establishing an open-loop error dynamic model of the visual servoing all-drive system; based on the theory of all-drive systems, it is transformed into a linear second-order system through nonlinear state feedback, and the state space equation is constructed; a sliding mode robust model reference tracking controller is designed. This method can maintain system stability under conditions of camera calibration error, dynamic parameter disturbance, and unmodeled dynamics coexisting, achieving high-precision, high-dynamic visual servoing tracking control.

This invention discloses an adaptive robust brake-by-wire method and system, relating to the field of vehicle brake-by-wire technology. The method comprises the following steps: S1, establishing a multi-coupled dynamic mathematical model of the brake-by-wire system and determining the system state variables and control inputs; S2, online observation and approximation of the system's hydraulic coupling disturbances, mechanical friction disturbances, and external road surface disturbances; S3, designing a hierarchical adaptive robust control based on backstepping sliding mode control technology and combined with observations from a multi-disturbance observer; S4, adjusting the gain coefficient of the sliding mode control online; S5, verifying the asymptotic stability of the entire control system; and S6, converting the designed control law into an electrical signal and inputting it to the actuator of the brake-by-wire system. This invention achieves precise, rapid, and stable control of braking pressure under complex operating conditions, improving the robustness and adaptability of the brake-by-wire system.

This invention discloses an assisted driving method and device for longitudinal following scenarios, relating to the field of vehicle assisted driving technology. The assisted driving method includes: dynamically fusing a rule model and a data-driven model based on the current distance between the vehicle and the vehicle in front, the vehicle's driving data, and the vehicle's driving scenario, to construct a dynamic fusion control law for the vehicle specifically for longitudinal following scenarios. The rule model is based on H... ∞ Robust control is constructed, and the data-driven model is constructed based on a deep deterministic policy gradient model. The dynamic fusion control law is used to generate the following acceleration of the vehicle relative to the vehicle in front. The vehicle is adjusted according to the following acceleration to improve the driving experience and traffic efficiency.

The application discloses a kind of based on multi-modal data fusion fermentation environment control method, comprising: collection multi-modal fermentation data and executes pre-processing, forms multivariate time series dataset;Improved MTAD-GAT model is input, calculates prediction error reconstruction error and consistency score, generates dynamic reliable weight;Coupling relationship matrix is constructed across mode, coupling residual is calculated and iterative correction data, reconstructs state vector and uncertainty;Coupling stability index is calculated, and state anchoring is executed, obtains stable reference state parameter;Based on state vector and reference state, construct metabolic feasible region, form control constraint interval;Self-scaling robust controller is constructed, dynamic scaling constraint and rolling optimization calculation, output fermentation control instruction.The application realizes stable identification to fermentation process state and adaptive control to fermentation environment parameter by multi-modal data fusion, coupling relationship analysis and self-scaling robust predictive control.

This invention discloses a robust visual servo control method for robots based on an adaptive update law, comprising: associating the pixel coordinates of feature points with their 3D coordinates in the camera coordinate system according to a pinhole camera projection model, and establishing a global linear regression model containing unknown position parameter vectors; designing an adaptive update law for the estimated values ​​of the unknown position parameter vectors based on the global linear regression model, used to estimate the unknown depth information parameters of feature points in real time, and reconstructing the visual Jacobian matrix online based on the estimated depth information parameters; constructing a visual state observer to obtain smoothed image feature estimates; defining a joint space-assisted tracking error based on the smoothed image feature estimates based on the reconstructed visual Jacobian matrix; and designing an integral sliding mode robust feedback term based on the integral sliding mode in the robot joint driving torque expression based on the lumped disturbance in robot dynamics, combined with feedforward compensation, to generate the final joint driving torque to drive the robot to perform servo tracking tasks. This invention can achieve highly robust and high-precision asymptotic tracking of target feature points under complex working conditions.

The application relates to the technical field of robot control, and discloses a dynamic environment tower inspection robot anti-interference adaptive control system and method. In view of the problems that the interference is difficult to decouple under high-altitude strong wind and guide rail deformation, and the instability caused by the gravity center deviation, the application contains modules such as dynamics solving, state estimation and adaptive control. The wind load and displacement fluctuation are taken into an extended state vector to perform Kalman filtering to realize physical level decoupling of the interference; a nonlinear optimization is solved to generate an augmented reference trajectory balanced in the coding gravity center and the normal force, and a sliding mode control torque is output based on the Lyapunov theory to eliminate instruction conflicts. The application realizes multi-target collaborative robust control, and guarantees the safe and intelligent inspection of high-voltage towers.

This invention discloses a navigation path planning method for multi-story buildings based on elevation maps, applicable to multi-story buildings containing stairs, ramps, and suspended structures. The core lies in using hierarchical cost modeling to uniformly incorporate clearance and height differences into global path search and model predictive control rolling optimization, achieving highly executable path generation and robust control against dynamic obstacles. The system integrates multi-sensor data to construct multi-slice elevation and cost maps, and introduces a time window decay mechanism to suppress the long-term impact of short-term dynamic structures on the cost field. Based on the hierarchical cost map, connecting areas such as stairs and corridors are raster-labeled, and an improved Theta* is used for arbitrary-angle global search to output a reference trajectory with terrain risk annotations; local layers employ model predictive control rolling optimization to achieve path tracking and obstacle avoidance, generating control commands. This invention significantly improves path executability, control stability, and robustness against dynamic obstacles in complex elevation environments.

The application provides an integrated control method for active suspension and differential steering of a distributed drive unmanned vehicle, which realizes trajectory tracking of the unmanned vehicle through differential steering, realizes vehicle body roll during steering through active suspension, improves the speed of the unmanned vehicle during cornering, and improves the steering stability. First, a dynamic model of the unmanned vehicle including differential steering and active suspension is established, a reference model is established to obtain a reference yaw rate and a reference vehicle body roll angle, a single-point preview driver model based on fractional calculus theory is designed to obtain a reference front wheel steering angle required by the reference model, and an H ∞ A robust controller controls the yaw rate and the vehicle body roll angle of the differential steering unmanned vehicle to track the reference values, and differential torques and control forces of left and right active suspensions are obtained. The results show that the integrated control method can make the differential steering unmanned vehicle realize trajectory tracking and vehicle body posture control.

The application discloses a heavy-load robot adaptive tracking and following control method for solid-phase additive, and relates to the technical field of robot control. The method comprises the following steps: performing dynamic modeling on the joint module of the heavy-load robot; based on the uncertainty in the solid-phase additive process, adjusting the dynamic model; modeling the performance requirements of the heavy-load robot as servo constraints, and calculating the second-order form of the servo constraints; based on the second-order form, solving the servo constraint force by using the Udwadia-Kalaba method; based on the servo constraint force, the adjusted dynamic model and preset assumption requirements, constructing a robust controller that fuses feedforward compensation, robust feedback and online parameter adaptation, so as to realize trajectory tracking control of the heavy-load robot. Thus, by fusing the U-K constraint framework and adaptive robust control, high-precision and strong-robust tracking of a complex trajectory required by the solid-phase additive process can be realized under multiple uncertainties, and the forming quality and stability can be ensured.

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

The present application relates to the technical field of industrial control system, disclose a kind of basin ecological water resources multi-objective collaborative optimization control method, comprising: the state space model of controlled unit is built, disturbance parameter sequence is collected;According to model prediction solution obtains control feasible solution set under different working conditions, extract the public intersection satisfying threshold constraint to establish robust control envelope set;Real-time monitoring the state deviation rate of change of controlled main loop, according to this dynamic adjustment controlled slave loop control dead zone;According to the deviation attenuation characteristics of main loop, anti-saturation reset is implemented to the integral regulator of slave loop, and inertia filter is implemented to given value, the present application establishes online adjustment boundary using robust control envelope set, reduces the impact of random disturbance on control law stability, effectively suppresses the oscillation generated in the adjustment process, realizes the nonlinear decoupling of different time scale control loop, and guarantees the running stability of controlled object.

The present application relates to the technical field of ship control, in particular to a ship fin stabilizer variable gain robust control method and system. It includes: constructing a three-layer nested working condition judgment model based on real-time navigation data, sequentially evaluating the roll energy, resonance risk and actuator capacity to determine whether to start control. The concept of virtual leader is introduced to construct a dynamic topology to coordinate the cooperative control of multiple fin stabilizer units. A multi-objective optimization algorithm is used to set the variable gain robust control parameters to balance the roll reduction effect, energy consumption and robustness. During the control process, a three-level abnormality fusing mechanism is implemented: fine-tune the parameters when there is a slight abnormality, backtrack the working condition judgment when there is a moderate abnormality, and roll back to the historical stable strategy when there is a serious abnormality. Through performance evaluation and intermittent freezing mechanism, the parameter optimization is suspended when the working condition is stable to reduce energy consumption and equipment wear and tear, and quickly recover the response when there is a mutation. The method realizes complete control from working condition identification, cooperative control, parameter optimization to safety guarantee.

The application aims at the networked automatic driving vehicle formation system under network attack, and proposes a robust control framework considering the change of communication topology structure. The random characteristics of deception attack in the formation system are modeled, and an adaptive event-triggered robust control model is adopted to ensure the reliability of the formation control. Finally, traffic flow simulation experiments are carried out to verify the effectiveness of the formation control model and the adaptive event-triggered strategy.

The embodiment of the application relates to the technical field of wind power converter optimization, and provides a wind power converter current optimization method and system based on model predictive control, which firstly constructs a system state vector capable of comprehensively reflecting real-time state of a power grid, and innovatively introduces fuzzy reasoning, takes normalized voltage amplitude, voltage unbalance degree and other key state quantities as inputs, and generates adaptive weight factors in real time and dynamically through the fuzzy reasoning, so as to online adjust a cost function of model predictive control. In this way, multiple control targets such as current tracking accuracy and switching loss can be online weighted and optimized under different power grid conditions, and finally, accurate and robust control of the converter current under a complex power grid environment is realized, so that the adaptability of the wind power converter to the power grid and the reliability of grid-connected operation are significantly enhanced.

The application relates to the technical field of unmanned aerial vehicle control, and discloses a four-unmanned aerial vehicle cooperative hoisting system for emergency rescue and a robust control method thereof; the robust control method solves the problem of low applicability of a rigid model by constructing a coupling model of the four-unmanned aerial vehicle cooperative hoisting system, establishing a dynamic equation based on Lagrange-D'Alembert theorem, accurately describing a high-order coupling relationship between unmanned aerial vehicle displacement and hoisting rope swing angle, designing a swing reduction control model, constructing a Lyapunov candidate function and a coupling error function by using an energy method, integrating position, formation and swing angle errors, regulating and controlling the swing reduction control model, dynamically adjusting parameters such as damping gain, realizing swing suppression, ensuring system asymptotic stability, improving model precision, enhancing control robustness, greatly reducing swing amplitude, and meeting the requirements of rapidness and safety of power emergency rescue.

This invention relates to the field of transformer voltage regulation collaborative control technology, specifically a transformer voltage regulation collaborative control system based on Internet of Things (IoT) data acquisition. The system includes: a heterogeneous control strategy execution unit for generating expected control command signals; a power grid regulation resilience quantification unit for acquiring real-time control command data and calculating the cumulative resilience decay index; a data attack identification unit for simultaneously acquiring the original voltage measurement signal and the expected control command signal generated by the heterogeneous control strategy execution unit, obtaining an attack-related index; a control switching decision unit for calculating the switching trigger value, generating the final control output; the heterogeneous control strategy execution unit is also used to switch between a deep reinforcement learning strategy and a classical robust control strategy in response to the final control output. This invention achieves accurate assessment of the physical consequences of attack behavior, preventing equipment failure due to excessive fatigue.

The full-drive adaptive robust control method of direct-drive precision motion platform belongs to the control field of linear motors. The existing adaptive robust control method of direct-drive precision motion platform has the problems of parameter adjustment difficulty, parameter adjustment blindness, and the problem of poor control effect of the existing full-drive system control method applied directly to the control of the direct-drive precision motion platform. The full-drive system model of the direct-drive precision motion platform is established firstly, and then the value of the related parameters of the controller is determined through the parameterization method. In real-time control, the error between the actual position and the expected position of the motion platform in the current period is calculated, and a virtual error variable is constructed accordingly. Then, combined with the controller parameter value, the parameter vector in the full-drive model, the regression vector and the acceleration of the expected trajectory, the control amount of the current sampling period is calculated. Finally, the control amount is applied to the linear motor to realize high-precision robust control of the motion platform. The adaptive robust control of the direct-drive precision motion platform is mainly carried out.

This invention belongs to the field of unmanned aerial vehicle (UAV) control technology, specifically relating to a method and system for attitude stabilization control of UAVs under special environmental disturbances. The method includes the following steps: S1: Establishing a wind field disturbance model, a dust particle impact model, and an electromagnetic interference model, and dynamically coupling these three models into a total disturbance vector experienced by the UAV; S2: Constructing a six-degree-of-freedom dynamic model incorporating the influence of the total disturbance vector. This model describes the translational and rotational motion of the UAV's center of mass under multi-source disturbances in complex environments; S3: Based on traditional aerodynamic models and incorporating environmental disturbance parameters, constructing a variable structure model with aerodynamic coefficients. This invention effectively improves the UAV's adaptability to multi-source coupled disturbances by constructing a unified multi-layered architecture for environmental identification, disturbance observation, Lyapunov robust control, and actuator compensation, thereby ensuring mission accuracy and safety.

The application discloses a ship reinforcement learning control method based on motion prediction and belongs to the technical field of ship autonomous control; the method comprises the following steps: constructing a limited time domain motion prediction module trained by a sequence regressor as a prior component, inputting a current state and a candidate action sequence, and outputting a short-time prediction trajectory; combining a prediction module output trajectory and a task cost function into a multi-step value evaluator, realizing short-domain cumulative cost estimation of a state-action sequence, and performing online training of the sequence regressor; designing a sequence type strategy network and training the sequence type strategy network with the goal of multi-step value maximization, and outputting an action sequence with a length of N; only the first step action of the action sequence is issued during online control, so that the low delay requirement is met, and interactive data is written back to an experience replay pool to continuously optimize the model. The application improves the utilization rate of prior sea trial data, reduces the cumulative risk of estimation bias, realizes robust control under a ship black box model, and is suitable for ship track keeping, dynamic positioning and other autonomous control scenes.

This invention discloses a large-signal modeling method for a photovoltaic-storage-charging energy routing system based on T-S fuzzy logic. The method includes: considering the coupling relationships between various units in the photovoltaic-storage-charging energy routing system, establishing a large-signal nonlinear state-space model of the system; identifying nonlinear terms in the large-signal nonlinear state-space model; performing fuzzy linearization on the nonlinear terms to obtain the linearized result; and integrating the linearized result to construct a T-S fuzzy model of the photovoltaic-storage-charging energy routing system. This invention can establish an accurate dynamic model covering the entire operating range of the system, providing an effective model foundation for large-signal stability analysis, robust controller design, and operational optimization of the system.

The application provides a mobile robot self-learning robust control method based on a hybrid Kalman filter and belongs to the technical field of robots.The technical scheme comprises the following steps: step 1, a dynamic model of an uncertainty factor;step 2, real-time estimation of state parameters and model parameters of the mobile robot to optimize the original dynamic model;step 3, introduction of an adaptive forgetting factor recursive least square method to dynamically adjust filter parameters;step 4, design of a non-singular fast terminal sliding mode controller based on a saturation function as a system main controller;step 5, establishment of an error correction RBF neural network and introduction of an adaptive error learning strategy;step 6, completion of stability analysis and proof of the control system by means of Lyapunov stability theory and simulation experiment of the optimized model.The application effectively suppresses the chattering of the sliding mode control and significantly improves the control precision and robust performance of the four-wheel robot.