Adaptive fuzzy moving sliding mode control for a class of perturbed underactuated nonlinear systems

Adaptive Fuzzy Sliding Mode Tracking Control of Uncertain Underactuated Nonlinear Systems: A Comparative Study

“…In this section, we consider the fourth-order underactuated system in its original coupled form. We aim at presenting a comparison between AFSMC for underactuated systems designed by Aloui et al [14], MAFSMC [13], and the proposed approach developed in Section 3 which is defined as AFHSMC.…”

“…In this connection, sliding mode is one of the robust controller design methods. Owing to its insensitivity to system parameter variations, fast response, and good transient performance, sliding mode control (SMC) has been successfully applied to underactuated systems [7][8][9][10][11][12][13]. Nonetheless, designing a common sliding mode controller for underactuated systems is not appropriate.…”

“…An additional term is added to the control law to compensate the approximation of the singular matrix. In [13], a moving adaptive fuzzy sliding mode control (MAFSMC) for underactuated systems is developed. The sliding surfaces are moved by changing the magnitude of the slopes by adaptive laws.…”

Moving sliding mode controller for overhead cranes suffering from matched and unmatched disturbances

“…Sun et al (2014, 2017) propose some nonlinear coupling regulation control methods for two-dimensional (2D) overhead cranes by analysing the system energy. In addition to the aforementioned control laws, abundant efficient control strategies have been developed by scholars, including trajectory planning (Uchiyama et al, 2013; Wu and Xia, 2014), model predictive control (Chen et al, 2016; Kapernick and Graichen, 2013; Wu et al, 2015) and fuzzy logic control (Baklouti et al, 2015; Smoczek, 2014; Xu et al, 2011) to improve the productivity of overhead crane systems. However, most of the above methods are sensitive to model parameter variations and external disturbances, which is a serious problem that must be taken into consideration.…”

“…This method can shorten the distance between the system state error and sliding mode surface, and finally shorten the time of the reaching phase. Specially, in Baklouti et al (2015), Shi et al (2012) and Yakut (2014), the sliding mode surface slope is adjusted based on neural network and fuzzy logic theory, and the convergence time of the state error variables is shortened greatly. However, crane model parameters must be known exactly in advance when fuzzy logic technology is adopted to update the slope values.…”