3results about How to "Solve chattering problems" patented technology

This application relates to the field of motor control technology and provides a method, device, and medium for positionless control of an air conditioning compressor. The method includes: acquiring the three-phase current of a permanent magnet synchronous motor in the air conditioning compressor and transforming it to an α-β coordinate system to obtain α-axis current components and β-axis current components; inputting the α-axis current components, β-axis current components, and the first α-axis control voltage and the first β-axis control voltage of the previous cycle to a back EMF observer based on an extended state observer to determine the rotor angle of the permanent magnet synchronous motor; determining the second α-axis control voltage and the second β-axis control voltage based on the α-axis current components, β-axis current components, and rotor angle; determining the three-phase duty cycle based on the second α-axis control voltage and the second β-axis control voltage; and driving the permanent magnet synchronous motor to run by controlling an IGBT inverter, thereby improving the robustness and position estimation accuracy of the positionless control of the air conditioning compressor.

The application discloses a rope driving mechanical tracking control method based on TDE and recursive non-singular terminal sliding mode, and relates to the technical field of mechanical hand control. The application comprises the following steps: through mechanical dynamics analysis, the dynamics standard equation of the mechanical hand is determined and is converted into a TDE simplified equation, and a tracking error is defined; an FNTSM function containing a proportional control parameter is constructed, a recursive non-singular terminal sliding mode variable is formed in combination with a recursive integral term; an interference adaptive law is designed to dynamically adjust the proportional control parameter, the convergence speed and the chattering characteristics of the tracking error are analyzed, and a composite reaching law is generated; based on the composite reaching law and the updated TDE simplified equation, a generalized output vector of a joint controller containing a fluctuation parameter is extracted, a complete control law is formed, the generalized vector is output in real time through the control law, the motion trajectory of the mechanical hand is simulated, and high-precision tracking control is realized.

This invention relates to the field of multi-UAV cooperative control technology and discloses a fixed-time neural adaptive formation control method for multi-UAVs to resist aerodynamic disturbances. A hierarchical controller architecture is constructed, with an outer loop of a distributed reference generation and safety tracking controller and an inner loop of a fixed-time terminal sliding mode controller with a cubic non-singular smooth switching mechanism. A fixed-time sliding mode surface with a cubic non-singular smooth switching mechanism is configured to achieve deterministic convergence. An adaptive RBF neural network is used to actively compensate for aerodynamic disturbances, and a safety mechanism based on obstacle functions is combined to constrain the UAV states within a safe set. Lyapunov analysis verifies the fixed-time stability of the system. Simulation results of six UAVs in formation show that the proposed method improves convergence speed by 29.6%, exhibits better robustness, and can achieve safe, accurate, and fast formation control of multiple UAVs under complex aerodynamic disturbances.