Stability Analysis for Mamdani-Type Integral Fuzzy-Based Sliding-Mode Control of Systems Under Persistent Disturbances

An improved fuzzy inference strategy using reinforcement learning for trajectory-tracking of a mobile robot under a varying slip ratio

“…The conceptualization of the fuzzy inference system (FIS) was marked by the introduction of two distinguished methods, namely the Mamdani and the Takagi-Sugeno (T-S) methods [36][37][38][39]. The former relies on fuzzy outputs that require a subsequent defuzzification, while the latter employs fired multilinear equations followed by a weighted averaging technique.…”

“…In addition, unlike prior works in refs. [25][26][27][28][29][30][31][32][33][34][35][36][37][38][39][40][41][42] which completely rely on designers' expert knowledge to relate the fuzzy inputs and outputs, this study avoids complete dependency on expert knowledge by utilizing the gathered knowledge of an RL agent after extensive training in a simulated environment.…”

Mamdani‐Type Fuzzy‐Based Adaptive Nonhomogeneous Synchronization

“…e synchronization phenomena among dynamical systems are a widely studied topic in the last decades due to the vast amount of applications in science and engineering [1][2][3]. In the related literature, dynamical systems and synchronization applications in many fields can be found, from biology [4,5], mechanical systems [6][7][8][9], chemistry [10], physics [11,12], fuzzy modeling [13][14][15][16] to secure communications [17][18][19], among many others. In general, it is said that a set of dynamical systems achieve synchronization if trajectories in each system approach a common trajectory.…”