“…Moreover, sensors are susceptible to noise interference, resulting in the acquisition of inaccurate measurements. A review of the existing literature shows that although a number of well-known control strategies, including neural NN-based system identification [26,27], fuzzy control (FC) [13,23], sliding mode control (SMC) [28], model-free control (MFC) [29], and hybrids of these, can mitigate the inherent problems of model-based control, but each has its own limitations. For example, NN often requires extensive experimental datasets for effective training and testing; to ensure robustness, FC relies on intricate qualitative logic rules derived from the relevant experience of the operator or designer; while SMC accelerates system stabilization, but often leads to undesirable oscillation.…”