Skip to content

Control of 2 DOF Arm Robot using Adaptive Sliding Mode Controller on Matlab Simulink

Notifications You must be signed in to change notification settings

zaki-x86/ASMC_2DOF_ARM

Repository files navigation

Adaptive Sliding Mode Control of 2 DOF Robotic Arm

2DOF Robot Arm Model on Simulink

$$ \ddot{x} = H^{-1} (\tau - C \dot{x} - F_c sgn(\dot{x}) - V \dot{x}) $$

Where:

$$ \tau = \begin{bmatrix} \tau_1 & \tau_2 \end{bmatrix}^T $$

$$ x = \begin{bmatrix} x_1 & x \end{bmatrix}^T $$

$$ \ddot{x} = \frac{d}{dx}\dot{x} = \frac{d^2}{dx}x $$

$$ H = \begin{bmatrix} M_{11} & M_{12} \\ M_{21} & M_{22} \end{bmatrix} $$

where:

$$ M_{11} = a_1 + 2 a_3 cos(x_2) - 2 a_4 sin(x_2) $$ $$ M_{12} = M_{21} = a_2 + a_3 cos(x_2) + a_4 cos(x_2) $$ $$ M_{22} = a_2 $$

$$ C = \begin{bmatrix} -c\dot{x}_2 & -c(\dot{x}_1 + \dot{x_2}) \\ c\dot{x}_1 & 0 \end{bmatrix} $$

where:

$$ c = a_3 sin(x_2) - a_4 cos(x_2) $$

Estimated parameters:

$$ a = \begin{bmatrix} a_1 \\ a_2 \\ a_3 \\ a_4 \end{bmatrix} = \begin{bmatrix} 3.3 \\ 0.97 \\ 1.4 \\ 0.6 \end{bmatrix} $$

Friction terms:

$$ F_c = \begin{bmatrix} F_{c1} & 0 \\ 0 & F_{c2} \end{bmatrix} $$

where:

$$ F_{c1} = F_{c2} = 5$$

and

$$ V = \begin{bmatrix} V_{1} & 0 \\ 0 & V_{2} \end{bmatrix} $$

where:

$$ V_1 = 5.5 $$ and $$ V_2 = 2.7 $$

Given initial conditions:

$$\begin{aligned} x_1 = 0.3 \space rad \\ x_2 = 0.5 \space rad \end{aligned}$$

alt text

System Signals

$$ Y = \begin{bmatrix} y_{11} & y_{12} & y_{13} & y_{14} \\ y_{21} & y_{22} & y_{23} & y_{24} \end{bmatrix} $$

where:

$$\begin{aligned} y_{11} &= \ddot{x}_{r1} \\\ y_{12} &= \ddot{x}_{r2} \\\ y_{21} &= 0 \\\ y_{22} &= \ddot{x}_{r1} + \ddot{x}_{r2} \\\ y_{13} &= (2\ddot{x}_{r1} + \ddot{x}_{r2}) \cos(x_{2}) - (\dot{x}_{2} \dot{x}_{r1} + \dot{x}_{1} \dot{x}_{r2} + \dot{x}_{2} \dot{x}_{r2}) \sin(x_{2}) \\\ y_{14} &= (2\ddot{x}_{r1} + \ddot{x}_{r2}) \sin(x_{2}) - (\dot{x}_{2} \dot{x}_{r1} + \dot{x}_{1} \dot{x}_{r2} + \dot{x}_{2} \dot{x}_{r2}) \cos(x_{2}) \\\ y_{23} &= \ddot{x}_{r1} \cos(x_2) + \dot{x}_1 \dot{x}_{r1} \sin(x_2) \\\ y_{24} &= \ddot{x}_{r1} \sin(x_2) + \dot{x}_1 \dot{x}_{r1} \cos(x_2) \end{aligned}$$

Control Law

Estimated values of unmodeled dynamics:

$$ b^{-1} = 5 H $$ $$ f = 5 (\ddot{x}_d - 2 \lambda \dot{e} - \lambda^{2} e) $$

Adaptive sliding surface definition

$$ s = \dot{e} + 2 \lambda e + \lambda^{2} \int^{t}_{0}{e} $$

Given control law:

$$ u = u_a + u_{eq} $$

such that:

$$ u_a = \hat{b}^{-1} (Y \hat{a} - k_D s) $$ $$ u_{eq} = \hat{b}^{-1} (\ddot{x}_d - \hat{f} - 2 \lambda \dot{e} - \lambda^{2} e) $$

The update of estimated parameters:

$$ \dot{\hat{a}} = - \Gamma Y^{T} s $$

therefore:

$$ a = \int{\dot{\hat{a}}}\space dt $$

Numerical Considerations

Integrators

Used limited output integrators in the arm model to be between $$ -pi $$ and $$ +pi $$.

Safe Matrix Inverse

Used a small regularization term:

epsilon = 1e-6; % Small regularization term
H_reg = H + epsilon * eye(size(H));
H_inv = inv(H_reg);

In addition to using pseudoinverse if matrix is close to singular or badly scaled.

Handling NaN and inf Values

Implemented an algorithm to detect NaN and inf values, by using these replacements:

nan_replacement = eps; % A very small positive number
inf_replacement = realmax; % The largest positive floating-point number

About

Control of 2 DOF Arm Robot using Adaptive Sliding Mode Controller on Matlab Simulink

Resources

Stars

Watchers

Forks

Releases

No releases published

Languages