Abstract
The main aim of this paper is to propose a robust fault-tolerant control for a three degree of freedom (DOF) mechanical crane by using a convex quasi-Linear Parameter Varying (qLPV) approach for modeling the crane and a passive fault-tolerant scheme. The control objective is to minimize the load oscillations while the desired path is tracked. The convex qLPV model is obtained by considering the nonlinear sector approach, which can represent exactly the nonlinear system under the bounded nonlinear terms. To improve the system safety, tolerance to partial actuator faults is considered. Performance requirements of the tracking control system are specified in an H∞ criteria that guarantees robustness against measurement noise, and partial faults. As a result, a set of Linear Matrix Inequalities is derived to compute the controller gains. Numerical experiments on a realistic 3 DOF crane model confirm the applicability of the control scheme.
Highlights
In recent years, fault-tolerant control (FTC) has become a relevant research field and has attracted significant attention because of its applicability to industrial systems, which increases their security and reliability
To validate the convex nonlinear model (7) of the 3 degree of freedom (DOF) crane with respect to the nonlinear model given in [27], the parameters given in Table 2 are considered with initial conditions x(0) = 0, l(0) = 0.22 [m], θ(0) = 0
This paper has presented a passive fault-tolerant controller for a 3 degree of freedom mechanical crane
Summary
Fault-tolerant control (FTC) has become a relevant research field and has attracted significant attention because of its applicability to industrial systems, which increases their security and reliability. A fault can be defined as abnormal behavior of at least one characteristic property or parameter that changes the system performance [1,2]. It is important to note that a fault denotes a breakdown rather than a catastrophe [3,4]. A fault not necessarily ends in a system stop. If no action is taken on time, the system performance begins to degrade that could end in a catastrophe [5]. In order to guarantee a minimum level of performance, it is necessary to develop methods to improve system safety and reliability
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