One of the approaches to sampled-data controller design for nonlinear continuous-time systems consists of obtaining an appropriate model and then proceeding to design a controller for the model. Hence, it is important to derive a good approximate sampled-data model because the exact sampled-data model for nonlinear systems is often unavailable to the controller designers. Recently, Yuz and Goodwin have proposed an accurate approximate model which includes extra zero dynamics corresponding to the relative degree of the continuous-time nonlinear system. Such extra zero dynamics are called sampling zero dynamics. A more accurate sampled-data model is, however, required when the relative degree of a continuous-time nonlinear plant is two. The reason is that the closed-loop system becomes unstable when the more accurate sampled-data model has unstable sampling zero dynamics and a controller design method based on cancellation of the zero dynamics is applied. This paper derives the sampling zero dynamics of the more accurate sampled-data model and shows a condition which assures the stability of the sampling zero dynamics of the model.
where parameters $m_i, k_i$ and $\bar{k} (i=1,2,3)$ are positive constants. The system has the relative degrees $r_1 = 2, r_2 = 2$ and the following zero dynamics.
One of the approaches to sampled-data controller design for nonlinear continuous-time systems consists of obtaining an appropriate model and then proceeding to design a controller for the model. Few studies have been investigated for obtaining sampled-data models of nonlinear multi-input multi-output systems (MIMO system) though we can find studies which consider nonlinear single-input single-output systems. This paper shows a more accurate sampled-data model than the Euler model for nonlinear multi-input multi-output systems and derives sampling zero dynamics of the model.
One of the approaches to sampled-data controller design for nonlinear continuous-time systems consists of obtaining an appropriate model and then proceeding to design a controller for the model. Few studies have been investigated for obtaining sampled-data models of nonlinear multi-input multi-output systems (MIMO system) though we can find studies which consider nonlinear single-input single-output systems. This paper shows a more accurate sampled-data model than the Euler model for nonlinear multi-input multi-output systems and derives sampling zero dynamics of the model.
The inverted pendulum on cart to be controlled is shown in the attached image. Its structure consists of a cart and pendulum where the pendulum is hinged to the cart via a pivot and only the cart is actuated, where:
$\theta$
is the pendulum angle
$rad$
$x$
is the cart position
$m$
$M$
is the mass of the cart
$kg$
$m$
is the mass of the pendulum
$kg$
$l$
is distance from turning center to center of mass of the pendulum
$m$
$f$
is the cart's friction coefficient
$kg/s$
$F$
is force applied to the cart
$N$
Lagrange's equations are applied with respect to $\theta$ and $x$ coordinates. The nonlinear state space model of inverted pendulum on cart system can then be obtained as:
where the state variables are consequently assigned as $x_1 = \theta$, $x_2 = \dot{\theta}$, $x_3 = x$ an $x_4=\dot{x}$, and where the input $u$ is the applied force $F$.
A hybrid controller for swinging up inverted pendulum system is proposed in this paper. The controller composes of two parts. The first part is the PD position control for swinging up the pendulum from the natural pendent position by moving the cart back and forth until the pendulum swings up around the upright position. The second part is a servo state feedback control designed by LQR which will be switched to stabilize the inverted pendulum in its upright position. The effectiveness and reliability of the proposed hybrid controller for swinging up inverted pendulum on cart are also shown by the experimental results
where $x_1$ is the elevator angle, $x_5$ is azimuth angle, and $g(x)=\begin{pmatrix}x_1k_{\psi} + y_{\psi \circ} \\ x_2k_{\phi} + y_{\phi \circ }\end{pmatrix}$
During the last decades a considerable progress has been made in the design of stabilizing controllers for nonlinear systems with known and unknown model. Feedback linearization approach via coordinate transformation is considered to be useful to tackle the control problem. Usually however, feedback linearization control does not guarantee exact linearization and robustness in the presence of uncertainties. Meanwhile most of the results developed are applicable to single-input feedback-linearizable systems. In this paper in order to cope the model uncertainties of a non linearizable MIMO nonlinear system, a robust feedback linearization scheme based on Lyapunov function is proposed. To verify the validity and effectiveness of the designed method, the suggested technique is applied to a twin rotor system