Abstract
In this paper, the trajectory tracking problem of robotic arms in discrete time is considered. To solve this problem, an adaptive least square controller is proposed. The uniform stability of the tracking error and parameters error for the aforementioned controller is guaranteed by means of a Lyapunov-like analysis. The effectiveness of the proposed controller is verified by on-line simulations.
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Acknowledgments
The author is grateful with the editors and the reviewers for their valuable comments and insightful suggestions, which helped to improve this research significantly. The author thanks the Secretaría de Investigación y Posgrado, Comisión de Operación y Fomento de Actividades Academicas del IPN, and Consejo Nacional de Ciencia y Tecnología for their help in this research.
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Communicated by E. Lughofer.
Appendices
Appendix A: Proof of Lemma 1
From the first equation of (4) gives:
Substituting the second equation of (4) into (20) gives:
Substituting the control (6) into (21) gives:
Obtaining the absolute value gives (7).
Appendix B: Proof of Lemma 2
Consider the case that \(tr(\overline{V}_{k}^{M}-V_{1}^{M})\le r_{M}\), from (12) gives that \(tr(V_{k}^{M}-V_{1}^{M})\le r_{M}\). Now consider \(tr(\overline{V}_{k}^{M}-V_{1}^{M})>r_{M}\), from (12) gives:
then \(V_{k}^{M}\) is always on the ball with center \(V_{1}^{M}\) and radius \(r_{M}\). The application of the above procedure in \(V_{k}^{C}\) and \(V_{k}^{G}\)gives a similar result. Thus, (13) obtained using (12).
Appendix C: Proof of Lemma 3
From the first equation of (4) gives:
Substituting into the second equation of (4) gives:
Substituting the control (8) into (22) gives:
where \(\widetilde{M}(x_{1,k})=\widehat{M}(x_{1,k})-M(x_{1,k})\), \(\widetilde{C}(x_{1,k},x_{2,k})=\widehat{C}(x_{1,k},x_{2,k})-C(x_{1,k},x_{2,k})\) and \(\widetilde{G}(x_{1,k})=\widehat{G}(x_{1,k})-G(x_{1,k})\). It can be expressed as:
Adding and subtracting \(\widehat{M}(x_{1,k})x_{1,k+2}\) and \(\widehat{M}(x_{1,k})x_{1,k+1}\) gives:
where \(B=I-K_{p}\), \(A_{k}=x_{1,k+2}-x_{1,k+1}-Tx_{2,k}\), \(D_{k}=T^{2}x_{2,k}\), \(H_{k}=T^{2}1\), and \(1=\left[ 1,\ldots ,1\right] ^{T}\in \mathfrak {R}^{n\times 1}\). Substituting (9), (10), and (23) into (24) gives:
where \(\widetilde{V}_{k-1}^{M}=V_{k-1}^{M}-V_{1}^{M}\), \(\widetilde{V} _{k-1}^{C}=V_{k-1}^{C}-V_{1}^{C}\) and \(\widetilde{V}_{k-1}^{G}=V_{k-1} ^{G}-V_{1}^{G}\). (25) can be written as (14), it proves the lemma.
Appendix D: Proof of Theorem 1
Define the Lyapunov-like function as:
where \(tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}\right\} \) and\(\ tr\left\{ \widetilde{V}_{k-1}^{T}P_{k-1}^{-1}\widetilde{V} _{k-1}\right\} \) are positive scalar numbers, the first difference is given by:
Substituting (14) and using (15) and (16) into \(\widetilde{x}_{1,k+2}\widetilde{x}_{1,k+2}^{T}-\widetilde{x}_{1,k+1} \widetilde{x}_{1,k+1}^{T}\) gives:
Using the following inequalities:
Substituting (29), (30), and (31) into (28) gives:
(32) can be rewritten as follows:
Using (15), (16), and (18) in (33) gives:
Using the following equality:
where \(a>0\in \mathfrak {R}\). Substituting (35) into (34) gives:
Using the definitions of \(\alpha \), \(b\), and \(c\) of (18), (36) can be written as:
Substituting (37) into (27) gives:
The term \(P_{k}^{-1}-P_{k-1}^{-1}-b\varrho \varrho ^{T}\) of (38) may be equal to zero to reach the objective \(\Delta L_{k+1}\le 0\); therefore, using the matrix inversion lemma gives:
This gives (11). The term \(\widetilde{V}_{k}-\widetilde{V} _{k-1}+P_{k-1}\left\{ a\varrho \widetilde{x}_{1,k+1}^{T}\right. \) \(\left. +b\varrho \varrho ^{T}\widetilde{V}_{k}\right\} \) may be equal to zero to reach the objective \(\Delta L_{k+1}\le 0\) which gives:
Thus (11) is derived. From (38) finally gives:
From Rubio et al. (2011), Rubio (2012), and using (26), (41), it is guaranteed that the tracking error and parameters error of the closed-loop system are uniform stable and \(L_{k}\) is bounded. Considering the addition of (41) from \(1\) to \(T\) and using that \(L_{T}>0\) and that \(L_{1}\) is a constant:
Thus (18) is satisfied.
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de Jesús Rubio, J. Adaptive least square control in discrete time of robotic arms. Soft Comput 19, 3665–3676 (2015). https://doi.org/10.1007/s00500-014-1300-2
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DOI: https://doi.org/10.1007/s00500-014-1300-2