Abstract
This paper studies the event-based decentralized adaptive finite-time tracking control problem of the interconnected nonlinear time-varying systems. A novel tracking control strategy associating event-triggered techniques, dynamic surface control, and finite-time control is presented. Correspondingly, the newly designed controller not only ensures finite-time convergence but also decreases the communication burden between the controller and the actuator. Moreover, the complexity explosion problem caused by the backstep** design procedure can be excluded. In addition, the difficulty caused by the system uncertainty is solved by utilizing bound estimation methods and constructing a suitable smooth function. Simulation results verify the effectiveness of our proposed control strategy.
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Data Availability Statement
Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.
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Funding
This work was supported in part by the Funds of National Science of China (Grant Nos. 61973146, 62173172), in part by the Distinguished Young Scientific Research Talents Plan in Liaoning Province (Grant Nos. XLYC1907077, JQL201915402), in part by the Taishan Scholar Project of Shandong Province of China (Grant No. tsqn201909097), and in part by the Applied Basic Research Program in Liaoning Province (Grant No. 2022JH2/101300276).
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Appendices
Appendix: Derivation of inequality (28)
Proof
Based on Assumption 2 and Lemma 1, one can obtain
Then, from (26), one has
Noting \(\underline{g}_{i,1}\tilde{\ell }_{i,1}-\underline{g}_{i,1}\hat{\ell }_{i,1}=-\underline{{g}}_{i,1}\ell _{i,1}=-1\), which yields (28). This ends the proof of (28). \(\square \)
Appendix: Derivation of inequality (75)
Proof
Define the compact set as follows \( \varPi _{i0}:=\{(y_{di},\dot{y}_{di}, \ddot{y}_{di}):y^2_{di}+\dot{y}_{di}^2+\ddot{y}_{di}^2\le K _{0}\}\), \(\varPi _{i,k}:=\{V_i(t)\le p_i\}\) where \(K_{0}\), \(p_i>0\), \(i=1,\ldots ,N\), \(k=1,\ldots ,n_i-1\). Clearly, for every i and k, \(\varPi _{i0}\times \varPi _{i,k}\) is also a compact set. Therefore, the continuous function \(B_{i,k}\) has a maximum, say, \(M_{i,k}\), on \(\varPi _{i0}\times \varPi _{i,k}\). Hence, one has \(|B_{i,k}|\le M_{i,k}\).
Thus, according to Lemma 3, and substituting (70)–(72) into (69), we get
with \(c_{i}=\min _{1\le i\le N, 1\le q\le n_i}\{2^{\beta -1} \underline{k_i}, 2\left( \frac{1}{\lambda _{i,q}}-1\right) ,\sigma _{\nu _i}\gamma _{\nu _i},\sigma _{\rho _i}\gamma _{\rho _i},\sigma _{\ell _{i,q}}\gamma _{\ell _{i,q}}\}\), \(\underline{k_i}=\min _{1\le q\le n_i}\{k_{i,q}\}\) and \(h_i=\sum _{j=1}^{N}\sum _{q=1}^{n_j}\varrho _{j,q,i}\phi _{j,q,i}(y_i) -\rho _iz_{i,1}\varphi _i\) are the uncertain terms generated by interactions.
Due to \(\phi _{j,q,i}\ge 0\) and the definitions of \(\rho _i\) and \(\varphi _i\), we have
By (B.2), it can be deduced that, for each \(i=1,\ldots ,N\), on the one hand, if \(\mid z_{i,1}\mid > \sqrt{\lambda _i}\), \(h_i<0\). And on the other hand, if \(\mid z_{i,1}\mid \le \sqrt{\lambda _i}\), \(y_i\) is bounded from (8). In summary then, \(h_i\) has an upper bound \(H_i \ge 0\).
Then, applying Lemma 4, let \(\var** _1=\sum _{q=2}^{n_i}\frac{1}{2}\phi _{i,q}^2\), \(\var** _2=\frac{1}{2\gamma _{\rho _i}}\tilde{\rho }_i^2\), \(\var** _3=\frac{1}{2\gamma _{\nu _i}}\tilde{\nu }_i^2\), \(\var** _4=\sum _{q=1}^{n_i}\frac{ \underline{g}_{i,q}}{2\gamma _{\ell _{i,q}}}\tilde{\ell }_{i,q}^2\), \(\varDelta =1\), \(\zeta _1=\beta \), \(\zeta _2=1-\beta \), \(\zeta _3=\beta ^{-1}\), it follows that
Then, substituting (B.2) and (B.3) into (B.1), we have
Inequality (75) is then obtained. \(\square \)
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Shi, S., Li, YX. & Tong, S. Event-based decentralized adaptive finite-time tracking control of interconnected nonlinear time-varying systems. Nonlinear Dyn 111, 3479–3495 (2023). https://doi.org/10.1007/s11071-022-08022-0
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DOI: https://doi.org/10.1007/s11071-022-08022-0