Improved Synovial Control FTSMC, STSMC, CSMC/High Order Synovial, Magnetic Flux Sensorless Observer

Improved Synovial Control FTSMC, STSMC, CSMC/High Order Synovial, Magnetic Flux Sensorless Observer
Improve synovial control of FTSMC, STSMC, CSMC
1. FTSMC (Fast Terminal Sliding Mode Control): It combines the advantages of traditional sliding mode control and terminal sliding mode control. By introducing the terminal sliding surface, the system state can quickly converge to the equilibrium state or desired trajectory within a finite time. In addition, its control law is continuous and does not contain switching terms, which helps to eliminate system chattering caused by switching functions.
2. STSMC (Super Twisted Sliding Mode Control): Compared to traditional sliding mode control, STSMC introduces a biquadratic Lyapunov function and a dual gain adjustment mechanism, which helps to drive the system state to the sliding mode surface faster. Once the system state reaches the sliding surface, it can maintain a stable sliding mode, thereby achieving fast response and precise control.
Model Highlights:
(1) Contains SMC, STSMC, FTSMC three types of sliding mode speed control, plus high-order sliding mode, magnetic flux sensorless observer, and corresponding derivation proof documents, which is very suitable for learning;
(2) The model is entirely modeled using discretization, and can directly generate code for the model. The simulation is consistent with the actual motor control, and the algorithm has been integrated and tested on the development board;
(3) Good observability and controllability of the model: It can switch between sensing and controller observer types with just one click,
The motor system is a nonlinear, strongly coupled system (which can also be used for robot control, power system control, and aircraft control, etc.). Compared with the PI speed loop controller, the sliding mode controller has the advantages of being insensitive to parameters, fast speed, and simple physical implementation. This simulation model can learn various typical sliding mode control algorithms well.