13 results about "Robust control system" patented technology

The invention discloses a multi-agent system double-layer Q learning control method and system based on Warisstein distance. The method comprises the following steps: establishing a multi-agent system state space model under fault and unknown distribution interference; constructing an inner-layer fault-tolerant control system and an outer-layer robust control system; designing an internal fault-tolerant control gain based on a Q learning algorithm; designing an outer layer robust control gain and a bias item based on the Warisstein distance and a zero-sum game framework; and designing a distributed consistency protocol in combination with neighborhood state information. According to the method, under the conditions that a system model is unknown, an executor has additive time-varying faults, external disturbance probability distribution is uncertain and only depends on limited samples, the influence of compensation faults and disturbance can be effectively reduced, the H infinity performance constraint is met, meanwhile, asymptotic state synchronization of all agents is achieved, and system stability is ensured.

The invention provides an unmanned aerial vehicle cluster robust control system and method for electric power inspection in a complex environment, and relates to the technical field of unmanned aerial vehicle cluster control, and the method comprises the steps: obtaining local related information when an unmanned aerial vehicle cluster executes an inspection task in the complex environment, and obtaining an optimal inspection path and task allocation based on the local related information, executing inspection based on the optimal inspection path and task allocation, and realizing unmanned aerial vehicle cluster maintenance and anti-interference control based on an H-infinity robust controller in the inspection process. According to the method, the inspection task can be autonomously completed in a weak information environment, autonomous decision, task allocation and robust control technologies are combined, the control difficulty caused by communication interruption or information loss of a traditional unmanned aerial vehicle cluster in a complex environment is solved, and the power inspection efficiency and safety are improved.

A computer-implemented method for determining a robust steering control of a steering system controller is disclosed, comprising: determining a solution to a system of statements comprising an equals sign, a greater-than sign, a greater-than or equal-to sign, a less-than sign and / or a less-than or equal-to sign, wherein at least a first part of the statements can be written equivalently or substantially equivalently as a first symmetric matrix greater than or equal to zero, wherein the first symmetric matrix is ​​based on system parameters of a system of one or more differential equations for modeling a steering of the steering system or their discretization, wherein the first symmetric matrix is ​​based on one or more perturbation parameters acting on the system of one or more differential equations; and calculating the steering control based on the solution.A further method for manufacturing and / or dataing a controller of a steering system is disclosed, comprising calculating the steering control; and adjusting the controller based on the steering control, in particular wherein the steering control is adjusted in the controller.

The invention discloses an unmanned helicopter complex environment flight robust control system based on digital twinning, which relates to the technical field of unmanned helicopters and comprises an environment sensing data acquisition module, an environment field quantification module, a pneumatic digital evolution module, a control instruction optimization module and a robust control execution module. Establishing a five-layer environment risk model through a flight threat field construction method oriented to wind direction anisotropy and chance constraint, and generating dynamic flight threat density field data; joint modeling is carried out based on flight data and a fluid mechanics reduced-order model, and adaptive evolution of aerodynamic characteristics is realized through a dynamic parameter calibration mechanism; combining the aerodynamic model and the threat density field, constructing a multi-objective optimization function with trajectory deviation, energy consumption and security constraint as objectives, and performing strategy training by adopting reinforcement learning; according to the invention, adaptive, energy-saving and safe flight control of the unmanned helicopter can be realized in a strong-disturbance and multi-threat environment, and the flight control robustness in a complex environment is improved.

The application discloses a multi-agent system double-layer Q learning control method and system based on a Wasserstein distance, which comprises the following steps: establishing a multi-agent system state space model under faults and unknown distribution disturbances; constructing an inner fault-tolerant control system and an outer robust control system; designing internal fault-tolerant control gains based on a Q learning algorithm; designing outer robust control gains and bias terms based on a Wasserstein distance and a zero-sum game framework; and designing a distributed consistency protocol in combination with neighborhood state information. The method can effectively reduce the influence of compensation faults and disturbances under the condition that a system model is unknown, an actuator has additive time-varying faults, external disturbance probability distribution is uncertain, and only limited samples are available, and can realize asymptotic state synchronization of all agents while meeting H-infinity performance constraints, thereby ensuring system stability.

A system includes an exhaust aftertreatment system and a controller including a processor coupled to a memory device storing instructions that, when executed by the at least one processor, cause the controller to perform operations. The operations include receiving sensor data comprising a diesel oxidation catalyst inlet temperature, a selective catalytic reduction system inlet temperature, and exhaust flow data; determining outer loop feedback data based on a target SCR temperature, and the SCR inlet temperature; determining inner loop feedback data based on the outer loop feedback data and the SCR inlet temperature; determining inner loop feedforward data based the outer loop feedback data, the DOC inlet temperature, and the exhaust flow data; determining heater control data based on the inner loop feedback data and the inner loop feedforward data; and causing the heater to operate in a stable manner while maintaining a stead desired temperature target.

This invention discloses a robust flight control system for unmanned helicopters in complex environments based on digital twins, relating to the field of unmanned helicopter technology. It includes an environmental perception data acquisition module, an environmental field quantization module, an aerodynamic digital evolution module, a control command optimization module, and a robust control execution module. By constructing a flight threat field oriented towards wind anisotropy and opportunity constraints, a five-layer environmental risk model is established, generating dynamic flight threat density field data. Joint modeling is performed based on flight data and a reduced-order hydrodynamic model, and adaptive evolution of aerodynamic characteristics is achieved through a dynamic parameter calibration mechanism. Combining the aerodynamic model and the threat density field, a multi-objective optimization function is constructed with trajectory deviation, energy consumption, and safety constraints as objectives, and reinforcement learning is used for policy training. This invention enables adaptive, energy-saving, and safe flight control of unmanned helicopters in strong disturbance and multi-threat environments, improving the robustness of flight control in complex environments.

A system includes an exhaust aftertreatment system and a controller including a processor coupled to a memory device storing instructions that, when executed by the at least one processor, cause the controller to perform operations. The operations include receiving sensor data comprising a diesel oxidation catalyst inlet temperature, a selective catalytic reduction system inlet temperature, and exhaust flow data; determining outer loop feedback data based on a target SCR temperature, and the SCR inlet temperature; determining inner loop feedback data based on the outer loop feedback data and the SCR inlet temperature; determining inner loop feedforward data based the outer loop feedback data, the DOC inlet temperature, and the exhaust flow data; determining heater control data based on the inner loop feedback data and the inner loop feedforward data; and causing the heater to operate in a stable manner while maintaining a stead desired temperature target.

The invention discloses a self-adaptive robust control system and method of an electro-hydraulic servo system, and belongs to the technical field of electro-hydraulic servo system control. According to the scheme, the method comprises the steps that a state-space equation module establishes a system dynamic model, an RBF neural network module estimates system characteristics and disturbance, an offline optimization module adopts a raccoon optimization algorithm to optimize RBF parameters, and robustness initial configuration is formed; the online optimization module is combined with an Actor-Critic reinforcement learning framework to adaptively adjust parameters; the nonsingular terminal sliding mode control module guarantees finite time convergence of the system; and the network communication module realizes data transmission among the modules. High-precision, quick-response and high-robustness electro-hydraulic servo system control is realized, and the problems of insufficient control precision, low convergence speed, weak anti-interference capability, insufficient network communication adaptability and the like in the prior art are effectively solved.

The application discloses a kind of robust control system and disassembly robot for harsh environment, comprising: track linear walking module, for collecting work area coordinates and transmission to control module;Receive the movement instruction generated by control module and move to work area;Contact type vertical alignment module, for receiving the calibration instruction generated by control module and calibrating the Z-axis perpendicularity reference coordinates of execution end;Laser ranging module, for emitting laser beam to the surface of workpiece to be disassembled, obtain the real-time distance from the surface of workpiece to be disassembled to execution end, calculate the cutting depth parameter;Control module generates cutting motion trajectory according to Z-axis perpendicularity reference coordinates and cutting depth parameter and sends to execution end. Improve the positioning accuracy of disassembly robot, eliminate the influence of vibration interference, make disassembly robot accurate parking to work area, improve the precision, stability and efficiency of disassembly work under harsh environment.

This application provides a robust H∞ control method and system for target drone flight at high angle of attack. The method includes: Step 1, obtaining the target drone speed, target drone angular velocity, target drone roll angle, and target drone airflow angle; Step 2, setting the constraint domain of the closed-loop poles of the robust control system for target drone flight at high angle of attack; Step 3, setting the tracking error model and target drone uncertainty structure of the robust control system for target drone flight at high angle of attack; Step 4, setting the robust H∞ control method for target drone flight at high angle of attack. ∞ The control method uses a system of linear matrix inequalities for the control parameters; Step 5: Set the objective of solving the system of linear matrix inequalities for the control parameters, and use the linear matrix inequality toolbox to solve the control parameters; Apply the parameters to the target drone's robust control system for high angle-of-attack flight to obtain the target drone's robust H for high angle-of-attack flight. ∞ The controller output; Step 6: Apply the values ​​of the aileron, elevator and rudder output by the controller to the target drone to realize the target drone tracking the high angle of attack command, sideslip angle command and track roll angle command.

The application discloses a variable-frequency water supply pressure setting method and system based on flow prediction, and particularly relates to the technical field of variable-frequency water supply control, which comprises the following steps: obtaining pipe network historical flow data, water consumption period characteristic data and pipe network pressure response data of a target water supply area, constructing a multi-dimensional water consumption characteristic database, and predicting water flow based on the multi-dimensional water consumption characteristic database by using a long short-term memory network model with a time period attention mechanism. The application improves the flow prediction accuracy of key periods such as morning and evening peaks by using LSTM and multi-time scale consistency prediction, actively offsets the pressure lag caused by pipe network inertia through dynamic feedforward compensation driven by prediction and online real-time identification of pipe network friction factor, so that the system completes pre-regulation before flow mutation, pressure fluctuation is reduced, a double-closed-loop robust control system with continuous self-learning ability is constructed, and the long-term operation stability is ensured while the water consumption mode changes are adapted.

The application provides a high-precision control method for an auxiliary robot, and relates to the field of medical robot motion control. The method is used for an auxiliary robot system with strong nonlinearity, unobservable mode and complex disturbance. Trajectory sample data is collected first, and an original high-dimensional linearization state space model is constructed through a dynamic state feature extraction algorithm. Then, an unobservable subsystem is extracted and stability constraints are applied to obtain an optimized high-dimensional linearization model. A low-dimensional reduced linear state space model is constructed by using a state energy truncation reduction method, and a nominal controller and a state observer are designed based on the model. At the same time, an adaptive estimation learner is constructed to estimate complex disturbance in real time, and a disturbance compensation signal is combined to form an overall robust control system. The application effectively improves the suppression and processing capacity of the auxiliary robot system on strong nonlinearity, unobservable mode and complex disturbance, and realizes high-precision trajectory tracking control.