Abstract
This paper considers the trouble of the usage of approximate strategies for realizing the neural controllers for nonlinear SISO systems. In this paper, we introduce the nonlinear autoregressive moving average (NARMA-L2) model which might be approximations to the NARMA model. The nonlinear autoregressive moving average (NARMA-L2) model is an precise illustration of the input–output behavior of finite-dimensional nonlinear discrete time dynamical systems in a neighborhood of the equilibrium state. However, it isn't always handy for purposes of neural networks due to its nonlinear dependence on the manipulate input. In this paper, nerves system based arm position sensor device is used to degree the precise arm function for nerve patients the use of the proposed systems. In this paper, neural network controller is designed with NARMA-L2 model, neural network controller is designed with NARMA-L2 model system identification based predictive controller and neural network controller is designed with NARMA-L2 model based model reference adaptive control system. Hence, quite regularly, approximate techniques are used for figuring out the neural controllers to conquer computational complexity. Comparison were made among the neural network controller with NARMA-L2 model, neural network controller with NARMA-L2 model system identification based predictive controller and neural network controller with NARMA-L2 model reference based adaptive control for the preferred input arm function (step, sine wave and random signals). The comparative simulation result shows the effectiveness of the system with a neural network controller with NARMA-L2 model based model reference adaptive control system. Index Terms--- Nonlinear autoregressive moving average, neural network, Model reference adaptive control, Predictive controller DOI: 10.7176/JIEA/10-3-03 Publication date: April 30 th 2020
Highlights
The neural network pattern tins be used in dominion strategies that require a global creation of the diagram forward or inverse dynamics, and these form are available in the example of neural networks, which have been trained using neural based design discovery techniques
3.5 m while the desired arm position is 3.5 m. This show us that the neural network controller with nonlinear autoregressive-moving average (NARMA)-L2 model based model reference adaptive control gives the exact nerve impulse voltage to the nerve of the arm
Three different systems are proposed which are a neural network controller is designed with NARMA-L2 model, neural network controller is designed with NARMA-L2 model system identification based predictive controller and neural network controller is designed with NARMA-L2 model based model reference adaptive control system
Summary
The neural network pattern tins be used in dominion strategies that require a global creation of the diagram forward or inverse dynamics, and these form are available in the example of neural networks, which have been trained using neural based design discovery techniques. The generalized teaching dresser attempts to crops the inverse of a fortification over the entire kingdom crack using off-line training while in the specialized configuration the convention is on-line and uses incorrectness back dispersal through the movement to learn the protocol inverse liveliness over a small operating region. The global firmness of the closed-loop response design is guaranteed provided the arrangement of the robot-manipulator action phrase is exact. Generalization of the director over the desired path breach has been established using an on-line weight education scheme. The advantage of a neuron-adaptive hybrids mastery scheme is the high accuracy and computationally less intensive proficiency scheme
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