Abstract
This paper develops a new adaptive control system based on the immersion and invariance (I&I) theory for the wing rock motion control using state variable feedback. An I&I-based adaptive control system is designed for the regulation of the roll angle trajectory to a nonlinear terminal manifold. The trajectory evolving on this manifold converges to the origin in a finite time. The control system includes a control module and a parameter estimator which can be tuned independently for shaping the responses. A special feature of this adaptive law is that once the estimated parameters coincide with the actual values, then subsequently these parameter estimates remain frozen, and adaptation stops. Furthermore the trajectories of the closed-loop system eventually evolve on a manifold in an extended state space. Simulation results are presented which show that the adaptive controller suppresses the wing rock motion of delta wing at different angles of attack, despite uncertainties.
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Abbreviations
- \(a_i,\hat{a}_i\) :
-
Wing rock model parameters
- \(b_\mathrm{s}, b\) :
-
Span[m], control effectiveness gain
- d :
-
Disturbance input (\(\hbox {rad/s}^2\))
- \(I_{xx}\) :
-
Moment of inertia about roll axis (kg \(\hbox {m}^2\))
- \(k_1, k_2, \mu \) :
-
Feedback gains
- \(m_\mathrm{c}\) :
-
Net control moment, \(bI_{xx}u\) (N m)
- s :
-
Function defining nonlinear terminal manifold
- \(t_\mathrm{s}\) :
-
Reference time, \(b_\mathrm{s}/2V_f\) (s)
- u :
-
Control input \((\hbox {rad/s}^2)\)
- \(V_\mathrm{f}\) :
-
Airspeed (m/s)
- V, \(V_1, V_2\) :
-
Lyapunov functions
- \(w, w_\mathrm{n},w_\mathrm{s}\) :
-
Control signals
- \(x_1, x_2\), x :
-
State variables, state vector
- \(z_1, z_2\), z :
-
Parameter errors, parameter error vector
- \(\alpha \) :
-
Angle of attack (deg)
- \(\beta _1(\phi , \dot{\phi }, t), \beta _2(\phi , \dot{\phi },t, \hat{\Theta }_1)\) :
-
Nonlinear functions of parameter estimates
- \(\gamma _i, \varGamma \) :
-
Gains in adaptation law
- \(\lambda _\mathrm{max}(\varGamma ^{-1}), \lambda _\mathrm{min}(\varGamma ^{-1})\) :
-
Minimum (maximum) eigenvalue of \(\varGamma ^{-1}\)
- \(\lambda , \nu \) :
-
Parameters of terminal manifold
- \(\phi \), \(\dot{\phi }\), \(\phi _\mathrm{r}\) :
-
Roll angle, roll rate, reference roll angle
- \(\varPsi _1, \varPsi \) :
-
Regressor vectors
- \(\Theta _1\), \(\theta _2\), \(\Theta , \hat{\Theta }_1, \hat{\theta }_2, \hat{\Theta }\) :
-
Parameter vectors, partial parameter estimates
- \( \varOmega \) :
-
Region in the state space
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Appendix
Appendix
1.1 1. Computation of \(\beta _1\) for \(\phi _\mathrm{r}\ne 0\)
For \(\phi _\mathrm{r} \ne 0\), (29) gives
Note \(\dot{\phi }\) has been replaced by variable \(\xi \) within the integrand. Integral of each term in (60) is obtained now.
Using equations for \(B_i\), one obtains \(\beta _1\) of the form
The partial derivatives of \(\beta _1\) with respect to \(\hat{\Theta }\) and \(\phi \) required to compute the update law for \(\hat{\Theta }\) are
This completes the derivation of \(\beta _1\).
1.2 2. Computation of \(\beta _2\) for \(\phi _\mathrm{r}=0\)
Now the computation of \(\beta _2\) is considered. As noted earlier, closed-loop responses are satisfactory for the choice of \(\phi _\mathrm{r}=0\), in the sequel computation of \(\beta _2\) is given by setting \(\phi _\mathrm{r}=0\) for simplicity. Equation (29) gives
with
Now we can get \(\beta _2\) by collecting all.
Computing \(A_i\), \(i=1,\ldots , 5\), gives
Now \(\beta _2\) is obtained by adding the expressions for \(A_i\).
This completes computation of \(\beta _2\).
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Lee, K.W., Singh, S.N. Immersion and invariance-based adaptive wing rock control with nonlinear terminal manifold. Nonlinear Dyn 88, 955–972 (2017). https://doi.org/10.1007/s11071-016-3287-y
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DOI: https://doi.org/10.1007/s11071-016-3287-y