BBO optimization of an EKF for interval type-2 fuzzy sliding mode control

Abstract

In this study, an optimized extended Kalman filter (EKF), and an interval type-2 fuzzy sliding mode control (IT2FSMC) in presence of uncertainties and disturbances are presented for robotic manipulators. The main contribution is the proposal of a novel application of Biogeography-Based Optimization (BBO) to optimize the EKF in order to achieve high performance estimation of states. The parameters to be optimized are the covariance matrices Q and R, which play an important role in the performances of EKF. The interval type-2 fuzzy logic system is used to avoid chattering phenomenon in the sliding mode control (SMC). Lyapunov theorem is used to prove the stability of control system. The suggested control approach is demonstrated using a computer simulation of two-link manipulator. Finally, simulations results show the robustness and effectiveness of the proposed scheme, and exhibit a more superior performance than its conventional counterpart.

An Enhanced Lithium-Ion Battery Model for Estimating the State of Charge and Degraded Capacity Using an Optimized Extended Kalman Filter

“…Q can be set intuitively or by the trial and error method, which is tedious and inaccurate; this can lead to filtering divergence over a long operating time, especially when R is set relatively small [33]. Thus, several optimization algorithms are applied to attain the optimal vector of Q that ensures precise estimation, such as the genetic algorithm (GA) [34], differential evolution (DE) [35], and biogeography-based optimization (BBO) [36]. However, none was applied to tune observers for battery states estimation.…”

Optimization of sliding mode control with PID surface for robot manipulator by Evolutionary Algorithms

“…A new combination of a PSO algorithm with super twisting SMC for chattering elimination is proposed in [27]. Medjghou et al have presented an optimized extended Kalman filter with integral type 2 fuzzy SMC using Biogeography-based optimization (BBO) [28]. Conventional SMC was optimized by colonial competitive algorithms in [29].…”