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The FPGAs characterized by its fast and the rate of
control loop is only limited by actuators, sensors, and I/O
modules [1]. Other features make the FPGAs the best choice
to implement different digital controllers are data parallel
processing and width of configurable word [2]. System Gen-
erator is a modeling tool of system level which simplifies the
design of FPGA hardware. The designer which has a less
knowledge about the hardware of FPGA can use this tool and
develops Simulink in different ways to obtain the modeling
environment which may be suitable for hardware design [3].
II. LITERATURE REVIRW
There are many studies to deal with controlling of robot
manipulators. PID Control method is one of most control
used scheme in the world because of its simplicity to control
many applications. This controller consists from three param-
eters (Proportional, Integral and Derivative) which tuned to
achieve better control performance. In [4], the dual PID con-
trol method was designed and found that it was an effective
scheme to minimize the overshoot and save electrical energy
consumption in tracking the path of robot. The PID controller
was proposed in [5] to control the robot manipulator, where
the gains of PID was optimized via using whale algorithm.
The simulation results demonstrated the superiority of the
optimal controller with a good settling time. The controlling
of input factors and tracking error had been employed to ex-
amine the robustness of PID control strategy in controlling the
trajectory of (2 Links) robot manipulator, the results achieved
the stability requirements [6]. In the literature [7], the Fuzzy strated that the designed controller was fast, accurate and
Sliding Mode Control (SMC) parameters in the sliding surface
were obtained by using developed Particle Swarm Optimiza-
tion (PSO). The control requirements were achieved to track
the robots, where the gains of state feedback calculated via
using the linear quadratic regular method. An optimal SMC
was introduced to track the biped robot, the controller was
tuned with PSO. The results explained that the convergence
range for the proposed controller were enhanced simultane-
ously [8]. In the literature [9], Linear Quadratic Regulator
(LQR) control was suggested to simulate the correction of
inputs and predict the successful rate for the robot manipulator strong second-order control method and has many features,
to pick up its target through the motion and the sensor errors.
The simulation and experiments outcomes demonstrated the
effectiveness of the proposed method. A multi group PSO
algorithm was proposed to track the motion planning of robot
manipulators. Some of best particles were chosen from the
branch groups instead of bad particles from elite group. Simu-
lation results shown that the suggested algorithm was superior
with other algorithms and it was converged toward the opti-
mum [10]. In [11], SMC scheme was designed to achieve
anti-interference, vibration suppression and the tracking con-
trol for (2-DOF) joint rigid coupling manipulator under the
Abdul Zahra & Wali
effect of bounded disturbance at the torques of input. The
article proved the stability requirements by using Galerkin
approximation sequences. In the literature [12], a new method
of fuzzy logic controller proposed to track the path of (2-DoF)
manipulator robot. The simulation results were developed by
MatLab/Simulink software and compared with performance
of conventional PID control method. The combination of the
fuzzy variable acceleration improved the response of system.
The tracking control for (2 - links) robot manipulators was
presented in [13]. The robust tracking was achieved using
Super Twisting SMC (STSMC), where the system stability
was proved by depending on the theorem of Lyapunov. The
Social Spider (SSO) algorithm employed to find the best opti-
mal values for suggested method. The quality of the designed
optimal control method was obtained and verified by using
MATLAB software. The experimental outcomes conducted
depending on (LabVIEW 2019) software. A Fuzzy, artificial
immune system and Fractional Order (FOPID) control struc-
ture was designed in [14] for controlling the movements of
(2-DOF) robotic manipulator. The traditional clonal selec-
tion algorithm had been utilized to optimize the links motion.
The proposed control strategy gave good outcomes. Authors
in [15], proposed Clonal Selection algorithm to recognize the
outline in mobile robots and based on the machine learning
method of Artificial Immune Systems. The results shown that
the robot was trained successfully. In the literature [16], a
study combined both SMC and fuzzy system to improve the
stability conditions and achieve the high-accuracy to control
the (4 DoF) manipulator. The experimental results demon-
stable in controlling the robots of service. By increasing the
manipulator links in the system, usefulness and efficacy were
enhanced with the complexity. FOPID method was used to
track the motion of (3 DoF) robot manipulator system. The
outcomes explained the quality of the prepared controller [17].
In [18], adaptive fractional high order terminal SMC was
proposed to track the motion of robotic manipulators under
the effect of varying loads with external disturbances and
uncertainties. The simulation results explained the robust-
ness of the suggested scheme. The (STSMC) represents a
like [19], [20]:
• The influences of chattering problem at the response
output using STSMC will be avoided.
• The STSMC method needs only the sliding surface
variable, and don’t require its derivative.
• Within a finite time, the states of system will reach to
the equilibrium point by using STSMC scheme.
• Finally, the STSMC satisfies exact convergence.
This work involves the control law development of the
STSMC method depending on (2-DOF) manipulator. The