Observer-based sampled-data control for nonlinear systems: Robust intelligent digital redesign approach

Intelligent digital redesign for T–S fuzzy systems: sampled‐data filter approach

“…If there exist sampled-data filter laws x d (t) and s^d(t), and a scalar γ such that the error system (5) is asymptotically stable under the zero-disturbance condition, and the performance index value satisfies J < 0 under the zero-initial condition, then x d (t) and s^d(t) are the optimal sampled-data filter laws for the IDR of the T-S fuzzy system (2). Remark 2: The main difference of the sampled-data filter design techniques between the direct [15,28,31,32] and IDR [21][22][23][24] approaches is as follows: The direct approach designs the sampleddata filter which estimates the system output s(t). On the other hand, the IDR approach designs the sampled-data filter which estimates the given continuous filter output s^c(t).…”

“…• The state-matching condition is indirectly guaranteed by minimising the norm distance between the system matrices, not the dynamic models [21][22][23][24] which can provide a more direct assurance of the state-matching condition. • Minimising the state-matching error requires a discretisation process, which causes discretization error [21][22][23][24][25][26].…”

“…However, although this method handles non-linear systems, it does not consider whole fuzzy systems and the stability condition for closed-loop sampled-data systems. These limitations have been solved by the global statematching approach [21], which has been successfully extended to robust control [22], observer-based control [23,24], and other techniques. Recently, the state-matching performance of IDR has been improved by a guaranteed cost function [25].…”

Intelligent digital redesign of fuzzy controller for non‐linear systems with packet losses

“…This technique is called intelligent digital redesign (IDR). Based on a previous study [18], various IDR techniques have been proposed: a global state‐matching condition [19], an observer‐based output feedback [20, 21] and a robust stabilisation [22]. However, these techniques only achieved the state‐matching condition by minimising the norm distance between the system matrices, but could not directly minimise the state‐matching error.…”

FIR-type robust H 2 and H ∞ control of discrete linear time-invariant polytopic systems via memory state-feedback control laws

“…Numerical results demonstrated that the PTVMSFC was very effective in reducing the conservatism of the traditional static state-feedback approaches. More recently, further improvement was made in [50][51][52][53][54][55][56][57][58][59] by devising the concept of the finite impulse response (FIR) controller built by adding different control signals depending linearly on the current and the past states, and combining the PTVMSFC scheme with the FIR control approach.…”