Abstract
This paper focuses on the state-feedback control problem for a class of high-order nonlinear systems with unknown time delay and control coefficients. Based on a novel dynamic gain-based backstepping technique and radial basis function neural network (RBF NN) approximation approach, the restrictions on high-order and nonlinearities are removed or further relaxed. Under these weaker conditions, a smooth state-feedback controller is skillfully constructed with only one adaptive parameter. In addition, the knowledge of time delay, NN nodes and weights is not necessary to be known a priori. It is proven that the designed controller can render the closed-loop system be semi-globally uniformly ultimately bounded. Finally, both practical and numerical examples are shown to demonstrate the effectiveness of the proposed scheme.


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Acknowledgements
This work is supported by National Natural Science Foundation of China (Nos. 61573172, 61403173, 61503166), 333 High-Level Talents Training Program in Jiangsu Province (No. BRA2015352) and Program for Fundamental Research of Natural Sciences in Universities of Jiangsu Province (No. 15KJB510011) and Jiangsu Province Innovation Program of Graduate Students of China (No. KYCX17_0364).
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Appendix
Appendix
Proof of Proposition 1
We prove it by induction. Assume that at step (\(i-1\)), there exist a series of virtual control laws
such that \(U_{i-1}\) satisfies
Now, we prove (52) still holds for (30). Firstly, from (4) and (7), one has
where \(\bar{f}_i=f_i-\sum _{j=1}^{i-1}\frac{\partial \alpha _i}{\partial x_j} (x_{j+1}^{p_j}+f_j)\,-\sum _{j=1}^{i-2}\frac{\partial \alpha _i}{\partial L_j}\dot{L}_j\) and \(\bar{g}_{id}=g_{id}-\sum _{j=1}^{i-1}\frac{\partial \alpha _i}{\partial x_j}g_{jd}\). Choosing Lyapunov function (30) and using (31), (52)–(53), one can get
In the sequel, we focus on estimating the third–fifth terms on the right-hand side of (54). From (2), one gets \(\bar{f}_i(X_i)=W_i^{*^T}S_i(X_i)+\delta _i(X_i)\), \(|\delta _i(X_i)|\le \varepsilon _i\), where \(\varepsilon _i>0\) is a design constant, \(X_i=(\bar{z}_i,\bar{L}_{i-1},\hat{\theta })^T \in S_{X_i}=\{X_i|X_i\in S_x\}\). Then, using \((a+b)^n=\sum _{i=0}^nC_n^ia^{n-i}b^i\), (5)–(7), Lemmas 1 and 2 and \(\Vert W_{i}^{*^T}\Vert ^2\Vert S_{i}\Vert ^2\le \Vert W_{i}^{*^T}\Vert ^2N_{i}\le \theta\), there exist positive constants \(\xi _i\), \(\bar{\gamma }_i=\sum _{j=0}^{\frac{p_i-1}{2}}\gamma _{ij}\), \(\gamma _{ij}\), \(\tilde{\gamma }_i\), and smooth functions \(\lambda _i(\hat{\theta })\), \(\Phi _{i}(\hat{\theta })\), \(\Phi _{i+1,0}(\bar{z}_i,\bar{L}_{i-1},\hat{\theta })\), \(\varrho _{ij}(\bar{z}_{jd})\), \(\varrho _{ii}(\bar{z}_{id},\bar{L}_{i-2,d})\) with \(j=1,\ldots ,i-1\) such that
Finally, by choosing \(U_i\) as (30) and substituting (55)–(57) into (54), one can yield (32). The proof is completed. \(\square\)
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Duan, N., Min, H., Shu, Z. et al. A combined NN and dynamic gain-based approach to further stabilize nonlinear time-delay systems. Neural Comput & Applic 31, 2183–2193 (2019). https://doi.org/10.1007/s00521-017-3180-8
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DOI: https://doi.org/10.1007/s00521-017-3180-8