Adaptive Robust Backstepping Sliding Mode Control of a De-Icing Industrial Robot Manipulator Using Neural Network With Dead Zone

Sampled‐data‐based event‐triggered adaptive prescribed performance control of uncertain strict‐feedback nonlinear systems

“…where the design parameters c 1 and c 2 can be determined by T and 𝜖. By using the time-varying scaling function (7), one can define the following transformation…”

“…Remark 5. Compared with the PPF whose initial value depends on the range of the initial tracking error, the reciprocal of the designed novel time-varying scaling function (7) is directly regarded as a PPF and the initial value can be chosen freely (rather than within a preassigned and fixed range). This characteristic is the precondition for realizing global PPC.…”

“…To solve the over‐parametrization problem in adaptive backstepping controllers, Reference 5 proposes the tuning function method. After decades of development, there have been a lot of theoretical research results on adaptive backstepping controllers combined with various control approaches such as sliding mode control, 6 neural network control, 7 and fuzzy control 8 . Besides, the adaptive backstepping control approach has also been applied to many practical engineering fields, for example, dual‐motor servo systems, 9 flexible robotic manipulators, 10 and wheeled mobile robots 11 …”