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A soft-bound interval control problem is proposed for general non-Gaussian systems with the aim to control the output variable within a bounded region at a specified probability level. To find a feasible solution to this challenging task, the initial soft-bound interval control problem has been transformed into an output probability density function (PDF) tracking control problem with constrained tracking errors, thereby the controller can be designed under the established framework of stochastic distribution control. Fault tolerant control (FTC) is investigated for soft-bound interval control systems in presence of faults. Three fault detection methods are proposed based on criteria extracted from the initial soft-bound control problem and the recast PDF tracking problem. An integrated design for fault estimation and FTC is proposed based on a double proportional integral structure. This integrated FTC design is developed through linear matrix inequality. Extensive simulation studies have been conducted to examine the key design factors, the implementation issues and the effectiveness of the proposed approach.
|Number of pages||13|
|Journal||IEEE Transactions on Systems, Man and, Cybernetics: Systems|
|Early online date||19 Oct 2018|
|Publication status||Published - 14 Jan 2021|
- non-Gaussian systems
- soft-bound control
- stochastic distribution control (SDC)
- probability density function (PDF)
- fault detection
- fault tolerant control (FTC)
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