# Super Twisted Sliding Mode Observer for Enhancing Ventilation Drive Performance
## 1 Introduction  
Squirrel cage induction motors (IMs) are widely used in ventilation systems across various applications owing to their cost-effectiveness and mechanical durability. These motors demonstrate remarkable dynamic stability when operated under vector control [1]. In closed-loop control systems, an input signal representing a controlled state variable is essential for operation. Sensors are commonly employed in vector control systems to gather information on speed along with other state variables [2]. However, in scenarios involving extreme operational conditions, high-speed applications, or stringent size constraints, sensors such as tachometers and encoders are connected to the motor to acquire speed data. Integrating these devices into the motor reduces system reliability in noisy environments owing to encoder signal degradation. Moreover, additional measures to mitigate noise in such cases increase both design time and costs. Consequently, electric motor control systems primarily utilize sensorless position estimation to address these challenges. Speed sensorless control enables cost savings, simplifies system design, and reduces spatial requirements by eliminating the need for sensor connections [3]. Machine-model-based techniques rely on certain variables, such as voltage and current, to estimate speed. These methods allow for reliable and accurate speed estimates, especially at high and medium speeds. The accuracy of the machine model at zero-speed operation is critical to the overall reliability of machine-model-based techniques. Developing reliable estimators for this operational range remains an active area of research within the context of machine-model-based approaches. Previous studies in this field have developed several relevant methods, including the extended Kalman filter [4], adaptive flux observer [5], reference model adaptive system (MRAS) observer [6], and sliding mode observer (SMO) [7]. Among these, the SMO based on variable structure control theory has recently garnered considerable attention for its desirable attributes, including a fast dynamic response, robustness, insensitivity to parameter fluctuations, and resistance to external distortions. Recent research has proposed alternatives to the conventional proportional-integral (PI) adaptation mechanism to improve the approximation accuracy of MRAS approaches [8; 9]. A reference model in one MRAS method employs a second-order SMO to estimate rotor flux [10; 11]. Another study has proposed a speed observer utilizing the SM model to measure magnetizing current [12]. In most of the above-mentioned studies, an SMO technique was utilized to improve the efficacy of the conventional MRAS methodology. Meanwhile, numerous studies have also introduced fundamental SMO techniques [13; 14; 15]. These techniques rely on SM functions resulting from the fluxes calculated along the axes in the current estimate. Here, the SM function is integrated into the flux estimation process, enabling rotor speed to be directly derived from the estimated rotor flux and the defined SM functions. The conventional SMO is affected by chattering, which arises from the switching function in the discontinuous control rule. Consequently, the accuracy of parameter estimation is impacted by the system's energy consumption and the total harmonic distortion of the motor current, both of which are exacerbated by pronounced chattering. To address these challenges, an enhanced SMO was developed to estimate the position and velocity of IM drives (IMDs) [15]. In [16], the author investigates the asymptotic adaptive appointed-time performance control for electro-hydraulic servo systems affected by unknown, variable linearizable parameters, nonlinear parameters, and disturbances. One study [17] proposes a new neural network (NN)-based adaptive dynamic surface asymptotic tracking controller that ensures assured transient performance for n-degrees of freedom (DOF) hydraulic manipulators.