Artificial Neural Networks (ANN) is an intelligent agent capable of being used in the control of nonlinear motions such as motions of a robot arm manipulator. ANN is capable of providing better control ability than traditional methods. The proposed controller has the ability to effectively utilize a large number of sensory information, can process data collectively and is adaptive by default. Using Back Propagation, the ANN is trained to imbibe the parameters of the robot arm manipulators for improved robot stability and suppressed vibration during robot operation. Mathematical models of the ANN are presented. Developed Simulink model is simulatedand simulation result analyzed. Training performance result of 0.024 Root Mean Square Error (RSME) reduction at epoch 2 was achieved. The result show that trained network robot controller is capable of minimizing the system error to almost zero. A hybrid arrangement could be more responsive for better stability as robot arm manipulator controller.
The main targets in designing control systems are stability, good disturbance rejection, and small tracking error. Several industrial robot manipulators are controlled by linear methodologies such as Proportional-Derivative (PD) controller, Proportional-Integral (PI) controller or Proportional-Integral-Derivative (PID) controllers. In this work, a Tunable Proportional Integral Derivative (TPID) controller is proposed for the control of a robotic arm. This tuning method is an attempt to obviate the shortcomings of the conventional PID controller where the proportional gain KP, integral gain KI and derivative gain KD, are fixed. The proposed controller provides opportunity for tuning the PID to be able to control the nonlinear movements and operations of a robot. The robot arm manipulator can be tuned as desired to provide control measures to enhance stability and suppress vibrations arising from robot arm operation. Simulation results showed an improvement over conventional PID controller for robotic arm manipulator.
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