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Adaptive least square control in discrete time of robotic arms

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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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References

  • Balaguer-Ballester E, Bouchachia H, Lapish CC (2013) Identifying sources of non-stationary neural ensemble dynamics. BMC Neurosci 14(Suppl 1):15

    Google Scholar 

  • Blazic S, Skrjanc I, Matko D (2003) Globally stable direct fuzzy model reference adaptive control. Fuzzy Sets Syst 139:3–33

    Article  MATH  MathSciNet  Google Scholar 

  • Bordignon F, Gomide F (2014) Uninorm based evolving neural networks and approximation capabilities. Neurocomputing 127:13–20

    Article  Google Scholar 

  • Brodka P, Saganowski S, Kazienko P (2013) Ged: the method for group evolution discovery in social networks. Soc Netw Anal Min 3:1–14

    Article  Google Scholar 

  • Buchachia A (2012) Dynamic clustering. Evolv Syst 3(3):133–134

    Article  Google Scholar 

  • Chen Z, Jagannathan S (2008) Generalized hamilton–jacobi–bellman formulation-based neural network control of affine nonlinear discrete-time systems. IEEE Trans Neural Netw 19(1):90–106

    Article  Google Scholar 

  • Dotschel T, Auer E, Rauh A, Aschemann H (2013) Thermal behavior of high-temperature fuel cells: reliable parameter identification and interval-based sliding mode control. Soft Comput 17:1329–1343

    Article  Google Scholar 

  • García-Cuesta E, Iglesias JA (2012) User modeling: through statistical analysis and subspace learning. Expert Syst Appl 39(5):5243–5250

    Article  Google Scholar 

  • Jagannathan S (2001) Control of a class of nonlinear discrete-time systems using multilayer neural networks. IEEE Trans Neural Netw 12(5):1113–1120

    Article  MathSciNet  Google Scholar 

  • Jahed M, Farrokhi M (2013) Robust adaptive fuzzy control of twin rotor mimo system. Soft Comput 17:1847–1860

    Article  Google Scholar 

  • Lee M, Choi HS (2000) A robust neural controller for underwater robot manipulators. IEEE Trans Neural Netw 11(6):1465–1469

    Article  Google Scholar 

  • Lewis FL, Dawson DM, Abdallah CT (2004) Robot manipulator control theory and practice. ISBN: 0- 8247-4072-6

  • Lughofer E (2012) Sigle pass active learning with conflict and ignorance. Evolv Syst 3:251–271

    Article  Google Scholar 

  • Lughofer E, Trawinski B, Trawinski K, Kempa O, Lasota T (2011) On employing fuzzy modeling algorithms for the valuation of residential premises. Inf Sci 181:5123–5142

    Article  Google Scholar 

  • Lughofer E (2011) Evolving fuzzy systems: methodologies. Advanced concepts and applications. Springer, Berlin

    Book  Google Scholar 

  • Maciel L, Lemos A, Gomide F, Ballini R (2012) Evolving fuzzy systems for pricing fixed income options. Evolv Syst 3:5–18

    Article  Google Scholar 

  • Marques Silva A, Caminhas W, Lemos A, Gomide F (2014) A fast learning algorithm for evolving neo-fuzzy neuron. Appl Soft Comput 14(B):194–209

    Article  Google Scholar 

  • Musial K, Kazienko P (2013) Social networks on the internet. World Wide Web 16:31–72

    Article  Google Scholar 

  • Perez-Cruz JH, Rubio JJ, Pacheco J, Soriano E (2014a) State estimation in MIMO nonlinear systems subject to unknown deadzones using recurrent neural networks. Neural Comput Appl. doi:10.1007/s00521-013-1533-5

  • Perez-Cruz JH, Chairez I, Rubio JJ, Pacheco J (2014b) Identification and control of a class of nonlinear systems with nonsymmetric deadzone using recurrent neural networks. IET Control Theory Appl 8(3):183–192

    Article  MathSciNet  Google Scholar 

  • Perng J-W (2013) Limit-cycle analysis of dynamic fuzzy control systems. Soft Comput 17:1553–1561

    Article  Google Scholar 

  • Precup R-E, David R-C, Petriu EM, Preitl S, Radac M-B (2014) Novel adaptive charged system search algorithm for optimal tuning of fuzzy controllers. Expert Syst Appl 41:1168–1175

    Article  Google Scholar 

  • Pratama M, Anavatti SG, Angelov PP, Lughofer E (2014) Panfis: a novel incremental learning machine. IEEE Trans Neural Netw Learn Syst 25(1):55–68

    Article  Google Scholar 

  • Pratama M, Anavatti SG, Lughofer E (2014) GENEFIS: Towards An Effective Localist Network. IEEE Trans Fuzzy Syst. doi:10.1109/TFUZZ.2013.2264938

  • Rubio JJ (2014) Stable and optimal controls of a proton exchange membrane fuel cell. Int J Control. doi:10.1080/00207179.2014.913201

  • Rubio JJ, Angelov P, Pacheco J (2011) An uniformly stable backpropagation algorithm to train a feedforward neural network. IEEE Trans Neural Netw 22(3):356–366

    Article  Google Scholar 

  • Rubio JJ (2012) Modified optimal control with a backpropagation network for robotic arms. IET Control Theory Appl 6(14):2216–2225

  • Rubio JJ, Zamudio Z, Pacheco J (2013) Proportional derivative control with inverse dead-zone for pendulum systems. Math Probl Eng 2013:1–9

    MathSciNet  Google Scholar 

  • Rubio JJ (2014) Evolving intelligent algorithms for the modelling of brain and eye signals. Appl Soft Comput 14(B):259–268

  • Rubio JJ, Perez-Cruz JH (2014) Evolving intelligent system for the modelling of nonlinear systems with dead-zone input. Appl Soft Comput 14(B):289–304

  • Song R, Xiao W, Wei Q (2013) Multi-objective optimal control for a class of nonlinear time-delay systems via adaptive dynamic programming. Soft Comput 17:2109–2115

    Article  Google Scholar 

  • Skrjanc I, Blazic S, Matko D (2003) Model-reference fuzzy adaptive control as a framework for nonlinear system control. J Intell Robot Syst 36:331–347

    Article  Google Scholar 

  • Spong MW, Vidyasagar M (1989) Robot dynamics and control. Wiley, New York

  • Sun F, Sun Z, Woo PY (2001) Neural network-based adaptive controller design of robotic manipulators with an observer. IEEE Trans Neural Netw 12(1):54–66

    Article  Google Scholar 

  • Tar JK, Rudas IJ, Bito JF, Preitl S, Precup RE (2010) Convergence stabilization by parameter tuning in robust fixed point transformation based adaptive control of underactuated MIMO systems, International joint conference on computational cybernetics and technical informatics, pp 407–412

  • Trawinski B (2013) Evolutionary fuzzy system ensemble approach to model real estate market based on data stream exploration. J Univ Comput Sci 19(4):539–562

  • Wai RJ, Chen PC (2004) Intelligent tracking control for robot manipulator including actuator dynamics via tsk-type fuzzy neural network. IEEE Trans Fuzzy Syst 12(4):552–558

    Article  Google Scholar 

  • Yang F, Yuan R, Yi J, Fan G, Tan X (2013) Direct adaptive type-2 fuzzy neural network control for a generic hypersonic flight vehicle. Soft Comput 17:2053–2064

    Article  Google Scholar 

  • Yu W, Rubio JJ (2009) Recurrent neural networks training with stable bounding ellipsoid algorithm. IEEE Trans Neural Netw 20(6):983–991

    Article  Google Scholar 

  • Zhao D, Wang B, Liu D (2013) A supervised actor–critic approach for adaptive cruise control. Soft Comput 17:2089–2099

    Article  Google Scholar 

Download references

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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Corresponding author

Correspondence to José de Jesús Rubio.

Additional information

Communicated by E. Lughofer.

Appendices

Appendix A: Proof of Lemma 1

From the first equation of (4) gives:

$$\begin{aligned} x_{1,k+2}=x_{1,k+1}+Tx_{2,k+1} \end{aligned}$$
(20)

Substituting the second equation of (4) into (20) gives:

$$\begin{aligned} x_{1,k+2}&= x_{1,k+1}+Tx_{2,k}+T^{2}M^{-1}(x_{1,k})\nonumber \\&\times \left[ u_{k}-G(x_{1,k})-C(x_{1,k},x_{2,k})x_{2,k}\right] \end{aligned}$$
(21)

Substituting the control (6) into (21) gives:

$$\begin{aligned} \widetilde{x}_{1,k+2}&= \widetilde{x}_{1,k+1}-K_{p}\widetilde{x}_{1,k+1} \nonumber \\ \widetilde{x}_{1,k+2}&= \left[ I-K_{p}\right] \widetilde{x}_{1,k+1} \end{aligned}$$

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:

$$\begin{aligned}&V_{k}^{M}=V_{1}^{M}+\frac{\overline{V}_{k}^{M}-V_{1}^{M}}{tr(\overline{V} _{k}^{M}-V_{1}^{M})}r_{M} \nonumber \\&tr(V_{k}^{M}-V_{1}^{M})=r_{M} \end{aligned}$$

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:

$$\begin{aligned} x_{1,k+2}=x_{1,k+1}+Tx_{2,k+1} \end{aligned}$$

Substituting into the second equation of (4) gives:

$$\begin{aligned} x_{1,k+2}&= x_{1,k+1}+Tx_{2,k}+T^{2}M^{-1}(x_{1,k})\nonumber \\&\times \left[ u_{k}-G(x_{1,k})-C(x_{1,k},x_{2,k})x_{2,k} \right] \end{aligned}$$
(22)

Substituting the control (8) into (22) gives:

$$\begin{aligned} M(x_{1,k})x_{1,k+2}&= M(x_{1,k})x_{1,k+1}-T\widetilde{M}(x_{1,k})x_{2,k} \nonumber \\&+T^{2}\widetilde{G}(x_{1,k})+T^{2}\widetilde{C}(x_{1,k},x_{2,k})x_{2,k}\nonumber \\&+\widehat{M}(x_{1,k})x_{1,d,k+2}-\widehat{M}(x_{1,k})x_{1,d,k+1}\nonumber \\&-\widehat{M}(x_{1,k})K_{p}\widetilde{x}_{1,k+1} \end{aligned}$$
(23)

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:

$$\begin{aligned}&M(x_{1,k})x_{1,k+2}-\widehat{M}(x_{1,k})x_{,1,d,k+2}\\&\quad =M(x_{1,k})x_{1,k+1}-\widehat{M}(x_{1,k})x_{1,d,k+1}-T\widetilde{M} (x_{1,k})x_{2,k}\\&\qquad +T^{2}\widetilde{G}(x_{1,k})\!+\!T^{2}\widetilde{C}(x_{1,k},x_{2,k})x_{2,k} \!-\!\widehat{M}(x_{1,k})K_{p}\widetilde{x}_{1,k+1} \end{aligned}$$

Adding and subtracting \(\widehat{M}(x_{1,k})x_{1,k+2}\) and \(\widehat{M}(x_{1,k})x_{1,k+1}\) gives:

$$\begin{aligned}&M(x_{1,k})x_{1,k+2}-\widehat{M}(x_{1,k})x_{1,d,k+2}\\&\qquad +\widehat{M}(x_{1,k})x_{1,k+2}-\widehat{M}(x_{1,k})x_{1,k+2} \\&\quad =M(x_{1,k})x_{1,k+1}-\widehat{M}(x_{1,k})x_{1,d,k+1}+\widehat{M} (x_{1,k})x_{1,k+1}\\&\qquad -\widehat{M}(x_{1,k})x_{1,k+1}-T\widetilde{M}(x_{1,k})x_{2,k}+T^{2} \widetilde{G}(x_{1,k})\\&\qquad +T^{2}\widetilde{C}(x_{1,k},x_{2,k})x_{2,k}-\widehat{M}(x_{1,k})K_{p} \widetilde{x}_{1,k+1} \end{aligned}$$
$$\begin{aligned} \widetilde{x}_{1,k+2}&= B\widetilde{x}_{1,k+1}+\left[ \widehat{M}^{-1} (x_{1,k})*\left( \widetilde{M}(x_{1,k})A_{k}\right. \right. \nonumber \\&\left. \left. \!\quad +\widetilde{C}(x_{1,k} ,x_{2,k})D_{k}+\widetilde{G}(x_{1,k})H_{k}\right) \right] \end{aligned}$$
(24)

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:

$$\begin{aligned} \widetilde{x}_{1,k+2}&= B\widetilde{x}_{1,k+1}+\widehat{M}^{-1}(x_{1,k})\left( \widetilde{V}_{k}^{M}\varrho ^{M}\left( z_{M,k}\right) +\in _{k}^{M}\right) A_{k}\nonumber \\&\quad +\widehat{M}^{-1}(x_{1,k})\left( \widetilde{V}_{k}^{C}\varrho ^{C}\left( z_{C,k}\right) +\in _{k}^{C}\right) D_{k}\nonumber \\&\quad +\widehat{M}^{-1}(x_{1,k})\left( \widetilde{V}_{k}^{G}\varrho ^{G}\left( z_{G,k}\right) +\in _{k}^{G}\right) H_{k} \end{aligned}$$
(25)

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:

$$\begin{aligned} L_{k+1}\!=\!\frac{1}{2}tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}\right\} \!+\!\frac{1}{2}tr\left\{ \widetilde{V}_{k-1}^{T}P_{k-1} ^{-1}\widetilde{V}_{k-1}\right\} \quad \end{aligned}$$
(26)

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:

$$\begin{aligned} \Delta L_{k+1}&= \frac{1}{2}tr\left\{ \widetilde{x}_{1,k+2}\widetilde{x}_{1,k+2}^{T}\right\} -\frac{1}{2}tr\left\{ \widetilde{x}_{1,k+1} \widetilde{x}_{1,k+1}^{T}\right\} \nonumber \\&\quad +\frac{1}{2}tr\left\{ \widetilde{V}_{k}^{T}P_{k}^{-1}\widetilde{V} _{k}\right\} -\frac{1}{2}tr\left\{ \widetilde{V}_{k-1}^{T}P_{k-1} ^{-1}\widetilde{V}_{k-1}\right\} \nonumber \\&= \frac{1}{2}tr\left\{ \widetilde{x}_{1,k+2}\widetilde{x}_{1,k+2}^{T}\right\} -\frac{1}{2}tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}\right\} \nonumber \\&\quad -\frac{1}{2}tr\left\{ \left( \widetilde{V}_{k}-\widetilde{V}_{k-1}\right) ^{T}P_{k-1}^{-1}\left( \widetilde{V}_{k}-\widetilde{V}_{k-1}\right) \right\} \nonumber \\&\quad +\frac{1}{2}tr\left\{ \widetilde{V}_{k}^{T}\left( P_{k}^{-1}-P_{k-1} ^{-1}\right) \widetilde{V}_{k}\right\} \nonumber \\&\quad +tr\left\{ \widetilde{V}_{k}^{T}P_{k-1}^{-1}\left( \widetilde{V} _{k}-\widetilde{V}_{k-1}\right) \right\} \end{aligned}$$
(27)

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:

$$\begin{aligned}&\widetilde{x}_{1,k+2}\widetilde{x}_{1,k+2}^{T}-\widetilde{x}_{1,k+1} \widetilde{x}_{1,k+1}^{T}\nonumber \\&\quad =-\widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}\nonumber \\&\qquad +\left( \left[ B\widetilde{x}_{1,k+1}+\widehat{M}^{-1}(x_{1,k})\left( \widetilde{V}_{k}^{T}\varrho +\in _{k}\right) \beta _{k}\right] \right. \nonumber \\&\qquad *\left. \left[ B\widetilde{x}_{1,k+1}+\widehat{M}^{-1}(x_{1,k})\left( \widetilde{V}_{k}^{T}\varrho +\in _{k}\right) \beta _{k}\right] ^{T}\right) \nonumber \\&\quad =-\widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}+B\widetilde{x}_{1,k+1} \widetilde{x}_{1,k+1}^{T}B^{T}\nonumber \\&\qquad +2\widehat{M}^{-1}(x_{1,k})\widetilde{V}_{k}^{T}\varrho \beta _{k}\widetilde{x}_{1,k+1}^{T}B^{T}\nonumber \\&\qquad +2\widehat{M}^{-1}(x_{1,k})\in _{k}\beta _{k}\widetilde{x}_{1,k+1}^{T}B^{T}\nonumber \\&\qquad +\widehat{M}^{-1}(x_{1,k})\widetilde{V}_{k}^{T}\varrho \beta _{k}\beta _{k} ^{T}\varrho ^{T}\widetilde{V}_{k}\widehat{M}^{-T}(x_{1,k}) \nonumber \\&\qquad +2\widehat{M}^{-1}(x_{1,k})\widetilde{V}_{k}^{T}\varrho \beta _{k}\beta _{k} ^{T}\in _{k}^{T}\widehat{M}^{-T}(x_{1,k})\nonumber \\&\qquad +\widehat{M}^{-1}(x_{1,k})\in _{k}\beta _{k}\beta _{k}^{T}\in _{k}^{T}\widehat{M}^{-T}(x_{1,k}) \end{aligned}$$
(28)

Using the following inequalities:

$$\begin{aligned}&2\widehat{M}^{-1}(x_{1,k})\widetilde{V}_{k}^{T}\varrho \beta _{k}\widetilde{x}_{1,k+1}^{T}B^{T}\nonumber \\&\quad \le B\widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T} B^{T}\nonumber \\&\qquad +\widehat{M}^{-1}(x_{1,k})\widetilde{V}_{k}^{T}\varrho \beta _{k}\beta _{k} ^{T}\varrho ^{T}\widetilde{V}_{k}\widehat{M}^{-T}(x_{1,k}) \end{aligned}$$
(29)
$$\begin{aligned}&2\widehat{M}^{-1}(x_{1,k})\in _{k}\beta _{k}\widetilde{x}_{1,k+1}^{T}B^{T}\nonumber \\&\quad \le B\widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}B^{T}\nonumber \\&\qquad +\widehat{M}^{-1}(x_{1,k})\in _{k}\beta _{k}\beta _{k}^{T}\in _{k}^{T}\widehat{M}^{-T}(x_{1,k}) \end{aligned}$$
(30)
$$\begin{aligned}&2\widehat{M}^{-1}(x_{1,k})\widetilde{V}_{k}^{T}\varrho \beta _{k}\beta _{k} ^{T}\in _{k}^{T}\widehat{M}^{-T}(x_{1,k}) \nonumber \\&\quad \le \widehat{M}^{-1}(x_{1,k})\in _{k}\beta _{k}\beta _{k}^{T}\in _{k}^{T} \widehat{M}^{-T}(x_{1,k}) \nonumber \\&\qquad +\widehat{M}^{-1}(x_{1,k})\widetilde{V}_{k}^{T}\varrho \beta _{k}\beta _{k} ^{T}\varrho ^{T}\widetilde{V}_{k}\widehat{M}^{-T}(x_{1,k}) \end{aligned}$$
(31)

Substituting (29), (30), and (31) into (28) gives:

$$\begin{aligned}&\widetilde{x}_{1,k+2}\widetilde{x}_{1,k+2}^{T}-\widetilde{x}_{1,k+1} \widetilde{x}_{1,k+1}^{T}\nonumber \\&\quad \le -\widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}+3B\widetilde{x} _{1,k+1}\widetilde{x}_{1,k+1}^{T}B^{T}\nonumber \\&\qquad +3\widehat{M}^{-1}(x_{1,k})\widetilde{V}_{k}^{T}\varrho \beta _{k}\beta _{k} ^{T}\varrho ^{T}\widetilde{V}_{k}\widehat{M}^{-T}(x_{1,k})\nonumber \\&\qquad +3\widehat{M}^{-1}(x_{1,k})\in _{k}\beta _{k}\beta _{k}^{T}\in _{k}^{T}\widehat{M}^{-T}(x_{1,k}) \end{aligned}$$
(32)

(32) can be rewritten as follows:

$$\begin{aligned}&tr\left\{ \widetilde{x}_{1,k+2}\widetilde{x}_{1,k+2}^{T}\right\} -tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}\right\} \nonumber \\&\quad \le -\left[ 1-3\lambda _{\max }^{2}\left( B\right) \right] tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}\right\} \nonumber \\&\qquad +3tr\left\{ \widehat{M}^{-1}(x_{1,k})\widetilde{V}_{k}^{T}\varrho \beta _{k}\beta _{k}^{T}\varrho ^{T}\widetilde{V}_{k}\widehat{M}^{-T}(x_{1,k})\right\} \nonumber \\&\qquad +3tr\left\{ \widehat{M}^{-1}(x_{1,k})\in _{k}\beta _{k}\beta _{k}^{T}\in _{k} ^{T}\widehat{M}^{-T}(x_{1,k})\right\} \end{aligned}$$
(33)

Using (15), (16), and (18) in (33) gives:

$$\begin{aligned}&tr\left\{ \widetilde{x}_{1,k+2}\widetilde{x}_{1,k+2}^{T}\right\} -tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}\right\} \nonumber \\&\quad \le -\left[ 1-3\lambda _{\max }^{2}\left( B\right) \right] tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}\right\} \nonumber \\&\qquad +3\gamma _{\max }^{2}\beta _{\max }^{2}tr\left\{ \widetilde{V}_{k}^{T} \varrho \varrho ^{T}\widetilde{V}_{k}\right\} \nonumber \\&\qquad +3\gamma _{\max }^{2}\beta _{\max }^{2}tr\left\{ \overline{\in }\overline{\in } ^{T}\right\} \end{aligned}$$
(34)

Using the following equality:

$$\begin{aligned}&atr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}\right\} +2atr\left\{ \widetilde{V}_{k}^{T}\varrho \widetilde{x}_{1,k+1}^{T}\right\} \nonumber \\&\qquad +atr\left\{ \widetilde{V}_{k}^{T}\varrho \varrho ^{T}\widetilde{V}_{k}\right\} \nonumber \\&\qquad -a\left[ \widetilde{x}_{1,k+1}+\widetilde{V}_{k}^{T}\varrho \right] \left[ \widetilde{x}_{1,k+1}+\widetilde{V}_{k}^{T}\varrho \right] ^{T}=0 \end{aligned}$$
(35)

where \(a>0\in \mathfrak {R}\). Substituting (35) into (34) gives:

$$\begin{aligned}&tr\left\{ \widetilde{x}_{1,k+2}\widetilde{x}_{1,k+2}^{T}\right\} -tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}\right\} \nonumber \\&\quad \le -\left[ 1-3\lambda _{\max }^{2}\left( B\right) -a\right] tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}\right\} \nonumber \\&\qquad +\left[ 3\gamma _{\max }^{2}\beta _{\max }^{2}+a\right] tr\left\{ \widetilde{V}_{k}^{T}\varrho \varrho ^{T}\widetilde{V}_{k}\right\} \nonumber \\&\qquad +2atr\left\{ \widetilde{V}_{k}^{T}\varrho \widetilde{x}_{1,k+1}^{T}\right\} +3\gamma _{\max }^{2}\beta _{\max }^{2}tr\left\{ \overline{\in }\overline{\in } ^{T}\right\} \nonumber \\&\qquad -\,a\left[ \widetilde{x}_{1,k+1}+\widetilde{V}_{k}^{T}\varrho \right] \left[ \widetilde{x}_{1,k+1}+\widetilde{V}_{k}^{T}\varrho \right] ^{T} \end{aligned}$$
(36)

Using the definitions of \(\alpha \), \(b\), and \(c\) of (18), (36) can be written as:

$$\begin{aligned}&tr\left\{ \widetilde{x}_{1,k+2}\widetilde{x}_{1,k+2}^{T}\right\} -tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}\right\} \nonumber \\&\quad \le -\,\alpha tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1}^{T}\right\} +btr\left\{ \widetilde{V}_{k}^{T}\varrho \varrho ^{T}\widetilde{V}_{k}\right\} \nonumber \\&\qquad +2atr\left\{ \widetilde{V}_{k}^{T}\varrho \widetilde{x}_{1,k+1}^{T}\right\} +ctr\left\{ \overline{\in }\overline{\in }^{T}\right\} \end{aligned}$$
(37)

Substituting (37) into (27) gives:

$$\begin{aligned} \Delta L_{k+1}&\le -\frac{\alpha }{2}tr\left\{ \widetilde{x}_{1,k+1} \widetilde{x}_{1,k+1}^{T}\right\} +\frac{c}{2}tr\left\{ \overline{\in }\overline{\in }^{T}\right\} \nonumber \\&\quad +\frac{b}{2}tr\left\{ \widetilde{V}_{k}^{T}\varrho \varrho ^{T}\widetilde{V}_{k}\right\} +atr\left\{ \widetilde{V}_{k}^{T}\varrho \widetilde{x}_{1,k+1}^{T}\right\} \nonumber \\&\quad -\frac{1}{2}tr\left\{ \left( \widetilde{V}_{k}-\widetilde{V}_{k-1}\right) ^{T}P_{k-1}^{-1}\left( \widetilde{V}_{k}-\widetilde{V}_{k-1}\right) \right\} \nonumber \\&\quad +\frac{1}{2}tr\left\{ \widetilde{V}_{k}^{T}\left( P_{k}^{-1}-P_{k-1} ^{-1}\right) \widetilde{V}_{k}\right\} \nonumber \\&\quad +tr\left\{ \widetilde{V}_{k}^{T}P_{k-1}^{-1}\left( \widetilde{V} _{k}-\widetilde{V}_{k-1}\right) \right\} \nonumber \\&\le -\frac{\alpha }{2}tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1} ^{T}\right\} +\frac{c}{2}tr\left\{ \overline{\in }\overline{\in }^{T}\right\} \nonumber \\&\quad -\frac{1}{2}tr\left\{ \left( \widetilde{V}_{k}-\widetilde{V}_{k-1}\right) ^{T}P_{k-1}^{-1}\left( \widetilde{V}_{k}-\widetilde{V}_{k-1}\right) \right\} \nonumber \\&\quad +\frac{1}{2}tr\left\{ \widetilde{V}_{k}^{T}\left( P_{k}^{-1}-P_{k-1} ^{-1}-b\varrho \varrho ^{T}\right) \widetilde{V}_{k}\right\} \nonumber \\&\quad +tr\left\{ \widetilde{V}_{k}^{T}P_{k-1}^{-1}\right. \nonumber \\&\quad *\left. \left( \widetilde{V}_{k}-\widetilde{V}_{k-1}+P_{k-1}\left\{ a\varrho \widetilde{x}_{1,k+1}^{T}+b\varrho \varrho ^{T}\widetilde{V} _{k}\right\} \right) \right\} \nonumber \\ \end{aligned}$$
(38)

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:

$$\begin{aligned}&P_{k}^{-1}=P_{k-1}^{-1}+b\varrho \varrho ^{T} \nonumber \\&\quad \Longrightarrow P_{k}=\left[ P_{k-1}^{-1}+b\varrho \varrho ^{T}\right] ^{-1}\nonumber \\&\quad \Longrightarrow P_{k}=P_{k-1}-\left[ \frac{1}{b}I+\varrho \varrho ^{T} P_{k-1}\right] ^{-1}P_{k-1}\varrho \varrho ^{T}P_{k-1}\nonumber \\ \end{aligned}$$
(39)

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:

$$\begin{aligned}&\widetilde{V}_{k}-\widetilde{V}_{k-1}+P_{k-1}\left\{ a\varrho \widetilde{x}_{1,k+1}^{T}+b\varrho \varrho ^{T}\widetilde{V}_{k}\right\} =0\nonumber \\&\quad \Longrightarrow \left[ I+bP_{k-1}\varrho \varrho ^{T}\right] \widetilde{V} _{k}=\widetilde{V}_{k-1}-P_{k-1}\left\{ a\varrho \widetilde{x}_{1,k+1} ^{T}\right\} \nonumber \\&\quad \Longrightarrow \left[ I+bP_{k-1}\varrho \varrho ^{T}\right] \widetilde{V} _{k}=\left[ I+bP_{k-1}\varrho \varrho ^{T}\right] \widetilde{V}_{k-1}\nonumber \\&\quad P_{k-1}\left\{ a\varrho \widetilde{x}_{1,k+1}^{T}+b\varrho \varrho ^{T}\widetilde{V}_{k-1}\right\} \nonumber \\&\quad \Longrightarrow \widetilde{V}_{k}=\widetilde{V}_{k-1} \nonumber \\&\qquad -\left[ I+bP_{k-1}\varrho \varrho ^{T}\right] ^{-1}P_{k-1}\left\{ a\varrho \widetilde{x}_{1,k+1}^{T}+b\varrho \varrho ^{T}\widetilde{V} _{k-1}\right\} \nonumber \\ \end{aligned}$$
(40)

Thus (11) is derived. From (38) finally gives:

$$\begin{aligned} \Delta L_{k+1}&\le -\frac{\alpha }{2}tr\left\{ \widetilde{x}_{1,k+1} \widetilde{x}_{1,k+1}^{T}\right\} +\frac{c}{2}tr\left\{ \overline{\in }\overline{\in }^{T}\right\} \nonumber \\&-\frac{1}{2}tr\left\{ \left( \widetilde{V}_{k}-\widetilde{V}_{k-1}\right) ^{T}P_{k-1}^{-1}\left( \widetilde{V}_{k}-\widetilde{V}_{k-1}\right) \right\} \end{aligned}$$
(41)

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:

$$\begin{aligned}&L_{T}-L_{1}\le - {\displaystyle \sum \limits _{k=1}^{T}} \frac{\alpha }{2}tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x}_{1,k+1} ^{T}\right\} +T\frac{c}{2}tr\left\{ \overline{\in }\overline{\in }^{T}\right\} \\&\qquad -{\displaystyle \sum \limits _{k=1}^{T}} \frac{1}{2}tr\left\{ \left( \widetilde{V}_{k}-\widetilde{V}_{k-1}\right) ^{T}P_{k-1}^{-1}\left( \widetilde{V}_{k}-\widetilde{V}_{k-1}\right) \right\} \\&\quad \Longrightarrow \underset{T\rightarrow \infty }{\lim }\frac{1}{T} {\displaystyle \sum \limits _{k=1}^{T}} \left[ \frac{1}{2}tr\left\{ \left( \widetilde{V}_{k}-\widetilde{V} _{k-1}\right) ^{T}P_{k-1}^{-1}\left( \widetilde{V}_{k}-\widetilde{V} _{k-1}\right) \right\} \right. \\&\qquad \left. +\frac{\alpha }{2}tr\left\{ \widetilde{x}_{1,k+1}\widetilde{x} _{1,k+1}^{T}\right\} \right] \le L_{1}\underset{T\rightarrow \infty }{\lim }\frac{1}{T}+\frac{c}{2}tr\left\{ \overline{\in }\overline{\in }^{T}\right\} \end{aligned}$$

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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