Abstract
This paper investigates the problem of designing a robust \(H_{\infty }\) state feedback plus state-derivative feedback control mechanism for a class of uncertain nonlinear time-varying delay systems described by a Takagi–Sugeno fuzzy model. A linear matrix inequality approach is applied to derive a robust controller for such a system. The proposed controller satisfies design requirements that ensure that the closed-loop system is asymptotically stable and meets pre-prescribed \(H_{\infty }\) performance index values. Finally, the illustrative examples are simulated to illustrate the effectiveness of the proposed methodology.
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Acknowledgements
The authors would like to thank the Department of Electronic and Telecommunication Engineering at King Mongkut’s University of Technology Thonburi, Bangkok, Thailand for supporting this study.
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Appendices
Appendix 1: Proof of Theorem 1
Proof
Write the quadratic Lyapunov–Krasovskii functional V(x(t)) as
where \(Q = {P^{- 1}}>0, S=W^{-1}>0\) and \(\beta =\frac{1}{1-\tau _d}\). Differentiate \(V\left( {x(t)} \right) \) along the closed-loop system (14); using the fact that for any vector x(t) and \(x(t-\tau (t))\) and matrix G,
where R is a positive definite matrix, we have
By adding and subtracting the equation
to and from (37), we acquire
where
Next, let us consider Theorem 1. By applying the Schur complement, we have
By applying the algebraic inequality
(41) then yields
and by substituting \(\Phi _{ij}\) shown in Theorem 1 of (43), we have
or
with \(K_{d_j}=Y_{d_j}P^{-1}\) and \(K_{s_j}=Y_{s_j}P^{-1}.\) Then, (45) yields
Pre- and post-multiply both sides of (46) by \(\left( {\begin{array}{ccc} {{(I+B_iK_{d_j})}^{-1}}&{}0&{}0\\ 0&{}I&{}0\\ 0&{}0&{}I \end{array}} \right) \) and \(\left( {\begin{array}{ccc} {{(I+B_iK_{d_j})}^{-T}}&{}0&{}0\\ 0&{}I&{}0\\ 0&{}0&{}I \end{array}} \right), \) respectively, and we have
or, in a more compact form,
By multiplying both sides of (48) by \(\left( {\begin{array}{cccc} Q&{} 0&{} 0&{} 0\\ 0&{} I&{} 0&{} 0\\ 0&{} 0&{} I&{} 0\\ 0&{} 0&{} 0&{} I \end{array}} \right), \) we obtain
\(i=1, 2, 3\ldots , r\,\), and
\(i<j \le r\). Apply the Schur complement to (49)–(50), and rearrange them; we then have
\(i=1, 2, 3\ldots , r\), and
\(i<j \le r\). From (51)–(52), it is clear that
where \(W={S^{-1}}\) and \(i, j = 1, 2,\ldots , r\). As (53) is less than zero, \(\mu _n\ge 0\) and \(\sum \nolimits _{n = 1}^r {\mu _n} = 1\), (39) becomes
Integrating both sides of (54) yields
As \(V(x(0)) = 0\) and \(V\left( {x({T_{\mathrm{f}}})} \right) \ge 0\) for all, \({T_{\mathrm{f}}} \ne 0,\) we have
Hence, the inequality (8) holds. This is the case when \({{w}}(t) = 0\), and (54) becomes \(\dot{V}(t) \le - {z^{\mathrm{T}}}(t)z(t) \le 0\). Therefore, the system (14) is asymptotically stable, and (b) is achieved. This completes the proof. \(\square \)
Appendix 2: Proof of Theorem 2
Proof
Choose the quadratic Lyapunov–Krasovskii functional V(x(t)) as
where \(Q = {P^{- 1}}>0, S=W^{-1}>0\) and \(\beta =\frac{1}{1-\tau _d}\). Differentiate \(V\left( {x(t)} \right) \) along the closed-loop system (24); from (36), we have
By adding and subtracting \(- {\tilde{z}^{\mathrm{T}}}(t)\tilde{z}(t)\;+\;{\gamma ^2}\sum \nolimits _{i = 1}^r {\sum \nolimits _{j = 1}^r} {\sum \nolimits _{m = 1}^r} {\sum \nolimits _{n = 1}^r} {{\mu _i}{\mu _j}{\mu _m}{\mu _n}} \left[ {\tilde{w}^{\mathrm{T}}}(t)\tilde{w}(t) \right] \) to and from (59), we have
where
Next, let us consider Theorem 2. We apply the Schur complement and the algebraic inequality (42) and in turn have
or
with \(K_{d_j}=Y_{d_j}P^{-1}\) and \(K_{s_j}=Y_{s_j}P^{-1}.\) Pre- and post-multiply both sides of (63) by \(\left( {\begin{array}{ccc} {{(I+B_iK_{d_j})}^{-1}}&{}0&{}0\\ 0&{}I&{}0\\ 0&{}0&{}I \end{array}} \right) \) and \(\left( {\begin{array}{ccc} {{(I+B_iK_{d_j})}^{-T}}&{}0&{}0\\ 0&{}I&{}0\\ 0&{}0&{}I \end{array}} \right), \) respectively, and we have
By multiplying both sides of (64) by \(\left( {\begin{array}{cccc} Q &{}0 &{}0 &{}0\\ 0 &{}I &{}0 &{}0\\ 0 &{}0 &{}I &{}0\\ 0 &{}0 &{}0 &{}I \end{array}} \right), \) applying the Schur complement and rearranging them, we have
and
\(i<j \le r\). Using (65)–(66) and the fact that
we have
where \(W={S^{-1}}\) and \(i, j = 1, 2,\ldots , r\). As (68) is less than zero, \(\mu _n\ge 0\) and \(\sum \nolimits _{n = 1}^r {\mu _n} = 1\), (60) becomes
Integrating both sides of (69) yields
As \(V(x(0)) = 0\) and \(V\left( {x({T_{\mathrm{f}}})} \right) \ge 0\) for all, \({T_{\mathrm{f}}} \ne 0,\) we have
Putting \(\tilde{z}(t)\) and \(\tilde{w}(t)\), respectively, given in (61) and (24) into (72) and using the fact that \(\Vert F(x(t),t)\Vert \le \rho , {\lambda }^2 = 1 + \rho ^2 \sum \nolimits _{i = 1}^r \sum \nolimits _{j = 1}^r \left[ \parallel {H^{\mathrm{T}}_{2_i}H_{2_j}}\parallel \right] \) and (67), we have
By adding and subtracting
to and from (73), one obtains
From the triangular inequality and the fact that \(\Vert F(x(t),t)\Vert \le \rho \), we have
Hence, the inequality (8) holds. This is the case when \({{w}}(t) = 0\), and (69) becomes \(\dot{V}(t) \le - {z^{\mathrm{T}}}(t)z(t) \le 0\). Therefore, the closed-loop system (24) is asymptotically stable, and (b) is achieved. This completes the proof. \(\square \)
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Ruangsang, S., Assawinchaichote, W. A novel robust \(H_{\infty }\) fuzzy state feedback plus state-derivative feedback controller design for nonlinear time-varying delay systems. Neural Comput & Applic 31, 6303–6318 (2019). https://doi.org/10.1007/s00521-018-3452-y
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DOI: https://doi.org/10.1007/s00521-018-3452-y