Intelligent Backstepping Control of Nonlinear Time-Varying System With Interval Type-2 Recurrent Fuzzy Neural Network Estimator

Abstract

This research proposes an intelligent backstepping control with an interval type-2 recurrent fuzzy neural network (IBSCIT2RFNN), which can modify the inherent nonlinear and time-varying control characteristics of a nonlinear time-varying system. In the IBSCIT2RFNN, a backstepping control (BSC) law is designed to stabilize the closed-loop control system. The lumped uncertainty in the BSC design is estimated using an interval type-2 recurrent fuzzy neural network (IT2RFNN). Initially, a step-by-step design of a nonlinear BSC is formulated for tracking periodic reference trajectories, with uncertainties lumped by a conservative constant. However, practical applications often involve unknown and difficult-to-predict lumped uncertainties. To address this, an IT2RFNN is introduced for real-time estimation of the lumped uncertainty. The Lyapunov stability method is applied to ensure asymptotic stability, leading to the development of online learning algorithms for the IT2RFNN. Additionally, an adaptive compensator is presented to proactively compensate for the estimation error of the IT2RFNN. Finally, a case study is included, presenting experimental results from a synchronous reluctance motor (SRM) position servo drive with maximum torque per ampere (MTPA) control. These results aim to validate the effectiveness and robust qualities of the proposed IBSCIT2RFNN.

Keywords

• Backstepping control (BSC)
• intelligent backstepping control with interval type-2 recurrent fuzzy neural network (IBSCIT2RFNN)
• interval type-2 recurrent fuzzy neural network (IT2RFNN)
• maximum torque per ampere (MTPA)
• synchronous reluctant motor (SRM)