A comparative study between static and dynamic neural networks for robotic systems control is considered. So, two approaches of neural robot control were selected, exposed, and compared. One uses a static neural network; the other uses a dynamic neural network. Both compensate the nonlinear modeling and uncertainties of robotic systems. The first approach is direct; it approximates the nonlinearities and uncertainties by a static neural network. The second approach is indirect; it uses a dynamic neural network for the identification of the robot state. The neural network weight tuning algorithms, for the two approaches, are developed based on Lyapunov theory. Simulation results show that the system response, equipped by dynamic neural network controller, has better tracking performance, has faster response time, and is more reliable to face disturbances and robotic uncertainties. 1. Introduction Several orders of neural robot control approaches have been proposed in the literature. These approaches are classified into two main classes: direct and indirect neural controls. If it requires prior identification of the controlled process model, it is called indirect control; otherwise it is called direct control. For the direct one, many architectures of control are mentioned in the literature [1–5]. For the second class, we cite neural control via dynamic neural network [6, 7], Model Reference Adaptive Control (MRAC) [8–10], Internal Model Control (IMC) [11–13], and predictive neural control [14, 15]. Both of these control classes are robust thanks to their ability to overcome the nonlinearities and uncertainties in the robot dynamics. In this paper, the aim is to compare the performance of static neural networks to dynamic neural networks in robotic systems control. For this, two types of control, from the already mentioned, are selected, presented, and tested for a two-link robot. One uses a static neural network; the other uses a dynamic neural network. The first approach is a direct neural control for improvement of a classic controller proportional derivative (PD), proposed by Lewis [1]; it manages to approximate the nonlinearities and uncertainties in the robot dynamics by a static neural network. The second approach is an indirect neural control via a high-order dynamic neural network, proposed by Sanchez et al. [7], which manages to use a dynamic neural network for a dynamic identification of the robot state. Based on simulation results, a comparative study between these two approaches is presented using different performance criteria. The rest of
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