By Guang-Hong Yang
More and extra, the complex technological platforms of at the present time depend on refined keep watch over structures designed to guarantee larger degrees of secure operation whereas optimizing functionality. instead of assuming continuously excellent stipulations, those platforms require adaptive techniques in a position to dealing with inevitable method part faults. traditional suggestions keep an eye on designs don't supply that potential and will bring about unsatisfactory functionality or maybe instability, that's completely unacceptable in complicated platforms reminiscent of airplane, spacecraft, and nuclear strength vegetation the place defense is a paramount hindrance.
Reliable regulate and Filtering of Linear platforms with Adaptive Mechanisms offers fresh examine effects which are advancing the sector. It exhibits how adaptive mechanisms will be effectively brought into the normal trustworthy control/filtering, in order that, in accordance with the web estimation of eventual faults, the proposed adaptive trustworthy controller/filter parameters are up-to-date instantly to make amends for any fault results.
Presenting a brand new process for fault-tolerant regulate (FTC) within the context of current study, this uniquely cohesive quantity, coauthored through major researchers —
- Focuses at the problems with trustworthy control/filtering within the framework of oblique adaptive procedure and LMI techniques
- Starts from the advance and major learn equipment in FTC to supply a scientific presentation of recent equipment for adaptive trustworthy control/filtering of linear structures
- Explains the foundations at the back of adaptive designs for closed-loop platforms in general operation in addition to those who account for either actuator and sensor failures
- Presents rigorous mathematical research of keep watch over equipment in addition to easy-to-implement algorithms
- Includes functional case reports derived from the aerospace together with simulation effects for the F-16
The authors additionally expand the layout thought from linear platforms to linear time-delay platforms through either reminiscence and memory-less controllers. in addition, a few newer effects for the corresponding adaptive trustworthy keep watch over opposed to actuator saturation are integrated. eventually, this remarkably useful source, bargains layout techniques and instructions that researchers can without difficulty hire within the layout of complicated FTC suggestions delivering more desirable reliability, maintainability, and survivability.
Read Online or Download Reliable Control and Filtering of Linear Systems with Adaptive Mechanisms PDF
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Additional info for Reliable Control and Filtering of Linear Systems with Adaptive Mechanisms
1, the adaptive H∞ performance index is close to the standard H∞ performance index when li is chosen to be suﬃciently large. Then the conclusion follows. 2 m Fa (0) = i=1 ρ˜i li(0) . 1 presents a suﬃcient condition for adaptive reliable H∞ controller design via dynamic output feedback. 19) is not LMIs. 19) becomes LMIs and linearly depends on uncertain parameters ρ and ρˆ.
7) where x(t) ∈ Rn is the state, ω(t) ∈ Rs is an exogenous disturbance in L2 [0, ∞], z(t) ∈ Rr is the regulated output, respectively. And ρ is a parameter vector, and ρˆ(t) is a time-varying parameter vector to be chosen. 8) holds. Thus, for 0 ω T (t)ω(t)dt > η, we have ∞ 0 z T (t)z(t)dt ≤ (γ 2 + η) ∞ 0 ω T (t)ω(t)dt 22 Reliable Control and Filtering of Linear Systems ∞ 0 For ω T (t)ω(t)dt ≤ η, it follows ∞ 0 z T (t)z(t)dt ≤ γ 2 η + η 2 which shows that the adaptive H∞ performance index is close to the standard H∞ performance index when η is suﬃciently small.
Denote the optimal solution as A¯Kf = A¯Kf opt , B Then the resultant dynamic output feedback controller gains can be obtained ¯Kf , CKf = Y0opt X −1 . 34) are linear matrix inequalities. 2 gives a method for the reliable dynamic output controller design with ﬁxed gains by two-step optimizations. Step 1 is to a CKf , which solves the corresponding design problem via state feedback. With the CK0 ﬁxed, controller parameter matrices AKf and BKf can be obtained by performing Step 2. In order to reduce the conservativeness of the dynamic output feedback controller with ﬁxed gains, the following dynamic output feedback controller with variable gains is given ˙ ξ(t) = u(t) = AK (ˆ ρ)ξ(t) + BK (ˆ ρ)y(t) ρ)ξ(t) CK (ˆ where ρˆ(t) is the estimation of ρ.