Model description:
Consider the following relative degree two MIMO nonlinear systems:
$$\begin{align*} \dot x_1 &= x_2 + \vartheta _1 x_1 \sin \left(t \right) + \Delta _1 \left({x_1 } \right) \\ \dot x_2 &= u + \vartheta _2 \left[{\matrix{{\left({x_1 + x_{2,1} } \right)\sin ^3 \left(t \right)} \cr {x_{2,1} + 2x_{2,2} } \cr}} \right] + \left[{\matrix{1 \cr {x_1 + x_{2,2} } \cr}} \right]\Delta _2 \left({x_{2,1} } \right) \\ y &= \left[{x_1,x_{2,2} } \right]^{\mathrm T}, \end{align*}$$
where $\Delta_1(x_1)=d_1\sin{(r_1x_1)}$ and $\Delta_2(x_{2,1})=d_2\tan{(r_2,x_{2,1})}.$ $\vartheta_1, \vartheta_2,\Delta_1,\Delta_2$ satisfy
$\displaylines{2 \le \vartheta _1 \le 4, - 4 \le \vartheta _2 \le - 1, \left\vert {\Delta _1 \left({x_1 } \right)} \right\vert \le \delta _1 = 40\cr \left\vert {\Delta _2 \left({x_{2,1} } \right)} \right\vert \le \delta _2 = 20. }$
The initial conditions are assumed to be $x_1(0)=0.5,x_{2,1}(0)=0$ and $x_{2,2}(0)=0.2$.
The actual plant parameters are
$\theta _1 = 3$, $\theta _2 = - 3$, $d_1 = - 30$, $d_2 = - 15$, $r_1 = 2$, $r_2 = 0.05.$
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Publication details:
| Title | Robust Adaptive Fuzzy Control by Backstepping for a Class of MIMO Nonlinear Systems |
| Publication Type | Journal Article |
| Year of Publication | 2010 |
| Authors | Lee, Hyeongcheol |
| Journal | IEEE Transactions on Fuzzy Systems |
| Volume | 19 |
| Issue | 2 |
| Pagination | 265 - 275 |
| Date Published | 11/2010 |
| ISSN | 1063-6706 |
| Accession Number | 11903670 |
| Keywords | adaptive control, feedback, fuzzy control, MIMO systems, nonlinear control systems, robust control |
| Abstract | This paper presents a robust adaptive control method for a class of multi-input-multi-output (MIMO) nonlinear systems that are transformable to a parametric-strict-feedback form which has couplings among input channels and the appearance of parametric uncertainties in the input matrices. The proposed approach effectively combines the design techniques of robust adaptive control by backstepping and adaptive fuzzy-logic control in order to remove the matching-condition requirement and to provide boundedness of tracking errors, even under dominant model uncertainties and poor parameter adaptation. Unlike previous robust adaptive fuzzy controls of MIMO nonlinear systems, this research introduces the robustness terms explicitly in the controller structure to counteract the effects of model uncertainties and parameter-adaptation errors. Uniform boundedness of the MIMO nonlinear control system is proved, and simulation results further validate the effectiveness and performance of the proposed control method. |
| DOI | 10.1109/TFUZZ.2010.2095859 |
