Feedback Linearization Control for Systems with Mismatched Uncertainties via Disturbance Observers
This paper focuses on a novel feedback linearization control (FLC) law based on a self-learning disturbance observer (SLDO) to counteract mismatched uncertainties. The FLC based on BNDO (FLC-BNDO) demonstrates robust control performance only against mismatched time-invariant uncertainties while the FLC based on SLDO (FLC-SLDO) demonstrates robust control performance against mismatched time-invariant and -varying uncertainties, and both of them maintain the nominal control performance in the absence of mismatched uncertainties. In the estimation scheme for the SLDO, the BNDO is used to provide a conventional estimation law, which is used as being the learning error for the type-2 neuro-fuzzy system (T2NFS), and T2NFS learns mismatched uncertainties. Thus, the T2NFS takes the overall control of the estimation signal entirely in a very short time and gives unbiased estimation results for the disturbance. A novel learning algorithm established on sliding mode control theory is derived for an interval type-2 fuzzy logic system. The stability of the overall system is proven for a second-order nonlinear system with mismatched uncertainties. The simulation results show that the FLC-SLDO demonstrates better control performance than the traditional FLC, FLC with an integral action (FLC-I) and FLC-BNDO.
Code (0)
등록된 구현이 없습니다.
Tasks
Self-LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Sliding Mode Control for Systems with Mismatched Time-Varying Uncertainties via a Self-Learning Disturbance Observer
This paper presents a novel Sliding Mode Control (SMC) algorithm to handle mismatched uncertainties in systems via a novel Self-Learning Disturbance Observer (SLDO). A computationally efficient SLDO is developed within a…
Self-LearningFeedback linearization through the lens of data
Controlling nonlinear systems, especially when data are being used to offset uncertainties in the model, is hard. A natural approach when dealing with the challenges of nonlinear control is to reduce the system to a line…
validAdaptive Control with Global Exponential Stability for Parameter-Varying Nonlinear Systems under Unknown Control Gains
It is nontrivial to achieve exponential stability even for time-invariant nonlinear systems with matched uncertainties and persistent excitation (PE) condition. In this paper, without the need for PE condition, we addres…
Partial Feedback Linearization Control of a Cable-Suspended Multirotor Platform for Stabilization of an Attached Load
In this work, we present a novel control approach based on partial feedback linearization (PFL) for the stabilization of a suspended aerial platform with an attached load. Such systems are envisioned for various applicat…
Robust Optimal Control for Nonlinear Systems with Parametric Uncertainties via System Level Synthesis
This paper addresses the problem of optimally controlling nonlinear systems with norm-bounded disturbances and parametric uncertainties while robustly satisfying constraints. The proposed approach jointly optimizes a nom…