Fault-Tolerant Process Control: Methods and Applications by Prashant Mhaskar

By Prashant Mhaskar

Fault-Tolerant procedure Control makes a speciality of the advance of normal, but sensible, equipment for the layout of complex fault-tolerant keep watch over platforms; those be certain an effective fault detection and a well timed reaction to reinforce fault restoration, hinder faults from propagating or constructing into overall disasters, and decrease the chance of security risks. To this finish, equipment are awarded for the layout of complicated fault-tolerant keep watch over platforms for chemical techniques which explicitly care for actuator/controller mess ups and sensor faults and knowledge losses. particularly, the publication places ahead:

· A framework for detection, isolation and prognosis of actuator and sensor faults for nonlinear systems;

· Controller reconfiguration and safe-parking-based fault-handling methodologies;

· Integrated-data- and model-based fault-detection and isolation and fault-tolerant keep an eye on methods;

· tools for dealing with sensor faults and knowledge losses; and

· equipment for tracking the functionality of low-level PID loops.

The methodologies proposed hire nonlinear structures research, Lyapunov strategies, optimization, statistical equipment and hybrid platforms concept and are predicated upon the belief of integrating fault-detection, neighborhood suggestions keep watch over, and supervisory keep an eye on. The applicability and function of the tools are proven via a few chemical approach examples. Fault-Tolerant strategy Control is a important source for tutorial researchers, commercial practitioners in addition to graduate scholars pursuing learn during this area.

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Extra resources for Fault-Tolerant Process Control: Methods and Applications

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3. A receding horizon scheme. This scheme introduces the notion of feedback into the control law to compensate for disturbances and modeling errors, whereby only the first piece of the future input trajectory is implemented and the constrained optimization problem is resolved at the next sampling instance. Consider the control of the system of Eq. 1) and assume that the state measurements of the system of Eq. t. 50) where S(Δ) is the family of piece-wise constant functions with sampling period Δ, N is the prediction horizon, Qc and Rc are strictly positive definite symmetric weighting matrices, x˜ is the predicted trajectory of the system due to control input u with initial state x(tk ) at time tk , and F (·) denotes the terminal penalty.

22) 18 2 Background on Nonlinear Systems and Control where α, ρ ∈ K∞ . Such functions V are called ISS-Lyapunov functions. If the system of Eq. 17) is ISS, then θ (t) → 0 implies x(t) → 0. The system of Eq. 17) is said to be locally input-to-state stable (locally ISS) if the bound of Eq. , if there exists a δ > 0 such that Eq. 19) is satisfied whenever x(0) ≤ δ and θ s[0,t] ≤ δ. It turns out that (local) asymptotic stability of the unforced system x˙ = f (x, 0) implies local ISS. 4 Stabilization of Nonlinear Systems This book is primarily about control design.

33) using feedback to cancel the nonlinearities in Eq. 39): u= 1 Lg Lr−1 f h(x) −Lrf h(x) + v . 40) Then the dynamics of y and its derivatives are governed by a chain of r integrators: y (r) = v. Since our original system of Eq. 33) has dimension n, we need to account for the remaining n − r states. Using differential geometry tools, it can be shown that it is always possible to find n − r functions ψr+1 , . . , ψn (x) with ∂ψi ∂x (x)g(x) = 0, for i = r + 1, . . , n such that the change of coordinates ζ1 = y = h(x), ζ2 = y˙ = Lf h(x), .

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