Abstract
This paper proposes an integrated design of fault-tolerant control (FTC) for nonlinear systems using Takagi–Sugeno (T–S) fuzzy models in the presence of modeling uncertainty along with actuator/sensor faults and external disturbance. An augmented state unknown input observer is proposed to estimate the faults and system states simultaneously, and using the estimates, an FTC controller is developed to ensure robust stability of the closed-loop system. The main challenge arises from the bidirectional robustness interactions, since the fault estimation (FE) and FTC functions have an uncertain effect on each other. The proposed strategy uses a single-step linear matrix inequality formulation to integrate together the designs of FE and FTC functions to satisfy the required robustness. The integrated strategy is demonstrated to be effective through a tutorial example of an inverted pendulum system (based on robust T–S fuzzy designs).
Highlights
During two decades there has been a growing interest in robust fault-tolerant control (FTC) system designs which are capable of tolerating faults whilst accounting for effect of modelling uncertainties [1], [2]
The nonlinear nature of dynamic systems means that methods such as Takagi-Sugeno (T-S) fuzzy [5] inference reasoning can be combined with the appropriate FTC theory as an extension to the linear robustness strategies
The above studies motivate the proposal in this paper to integrate the observer based fault estimation (FE) and FTC designs for application to a class of nonlinear systems subjected to actuator/sensor faults
Summary
During two decades there has been a growing interest in robust fault-tolerant control (FTC) system designs which are capable of tolerating faults whilst accounting for effect of modelling uncertainties [1], [2]. The above studies motivate the proposal in this paper to integrate the observer based FE and FTC designs for application to a class of nonlinear systems subjected to actuator/sensor faults. The integrated observer and state estimate controller designs (based on T-S fuzzy systems) aim to obtain the observer and controller gains simultaneously This is the widely known strategy for robust state estimate control using H∞ optimization which is typically achieved using a singlestep linear matrix inequality (LMI) formulation [24]. This optimization approach does not take into account the system modelling uncertainty [24] and FTC is out of the scope of this study considered.
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