DOI : https://dx.doi.org/10.26808/rs.ed.i10v2.07 International Journal of Emerging Trends in Engineering and Development Issue 10, Vol.3 (Apr.-May 2020) Available online on http://www.rspublication.com/ijeted/ijeted_index.htm ISSN 2249-6149 ©2020 RS Publication, rspublicationhouse@gmail.com Page 1 DESIGN AND DEVELOPMENT OF A NEURAL NETWORK (NN) CONTROLLER FOR A ROBOTIC ARM Akaninyene M. Joshua Ph.D and Prof. Eneh I.I. and Ogbu, Mary N. C. Dept of Electrical and Electronics Engineering, Enugu State of Science and Technology, Enugu State. ABSTRACT 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. Keyword: Artificial Neural Networks (ANN), Back Propagation,Simulink model,Root Mean Square Error (RSME). 1.0 Introduction In many cases, when it is difficult to obtain a model structure for a system with conventional system identification techniques, the Artificial Neural Network (ANN), known as the intelligent techniques, is desired since it is capable of describing the system in the best possible way (Mersha, Stramigioli and Carloni, 2014) for further analyses and application possibilities.The artificial neural network systems are commonly used for modeling nonlinear dynamic systems. The main advantages of utilizing neural network for system identification are that they simultaneously evaluate many points in the parameter space and converge towards the global solution (Mersha, Stramigioli, Carloni, 2014).In contrast, neural network approaches for system identification offer many advantages over conventional ones especially in terms of flexibility and hardware realization (Erkaya, 2012). This technique is quite efficient in modeling nonlinear systems or if the system possesses nonlinearities to any degree. Generally, robots are machines that behave and carry out tasks like a human being. Lately, the industry is moving from automation to robot system, to increase productivity, reduce waste and to deliver uniform quality. Robots are mainly deployed to hostile environment such as in atomic plant to handle radioactive material, construct and repair space station and satellites, nursing and aiding patient in medical field, heavy earth moving equipment and many more. The fundamental reason for engaging a robot is to eliminate the human operator. This reason is not just centered on safety but to save labor and reduce cost. Other classes of applications of concern are situations where human interference has negative impact especially on the products such as in food handling, semiconductor handling, pharmaceuticals and so on. Also, there are situations whereby the product has negative impact on the human such as;