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 a precise illustration of the input–output behavior of finite-dimensional nonlinear discrete time dynamical systems in a neighborhood of the equilibrium state. However, it is not 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.
Table of Contents
I. INTRODUCTION
II. SYSTEM DISCRIPTION
A. Nerves System Based Arm Position Sensor System Description
B. Design of NARMA-L2 Neural Network Controller
1) Identification of the NARMA-L2 Model:
C. Design of NARMA-L2 Model Controller Using System Identification
1) System Identification:
2) Predictive Control:
D. Design of NARMA-L2 Model Controller Using Adaptive Control
D. Numerical Value Comparison of the proposed systems
III. RESULT AND DISSCUSION
A. Comparison of the Proposed Controllers using step input signal
B. Comparison of the Proposed Controllers using Sine Wave Input Signal
C. Comparison of the Proposed Controllers using Random Input Signal
IV. CONCLUSION
Research Objectives and Topics
This paper aims to improve the control of nonlinear arm nerve simulator systems for nerve patients by developing and comparing three distinct neural network-based control strategies, specifically evaluating their ability to achieve precise arm position regulation through adjusted impulse voltage signals.
- Nonlinear Autoregressive Moving Average (NARMA-L2) control
- Model Reference Adaptive Control (MRAC)
- System identification and predictive control techniques
- Neural network training for complex system dynamics
- Comparative analysis of controller performance across various input signals
Excerpt from the Book
B. Design of NARMA-L2 Neural Network Controller
The neuro controller described on this phase is cited through two different names: response linearization control and NARMA-L2 manipulate. It is known as comments linearization when the plant shape has a specific form (associate form). It is known as NARMA L2 manipulate while the fortification mold may be approximated by using the same form. The vital principle of this type of control is to convert nonlinear design system into linear dynamics with the aid of canceling the nonlinearities. This phase starts off evolved with the aid of submitting the associate system form and presentation how you may use a neural community to become aware of this model. Then it describes how the identified neural network model may be used to broaden a controller.
Summary of Chapters
I. INTRODUCTION: Outlines the application of neural networks in control strategies for nonlinear systems and the motivation behind the research.
II. SYSTEM DISCRIPTION: Details the mathematical and physical structure of the arm nerve simulator system and describes the design of the three proposed neural network controllers.
III. RESULT AND DISSCUSION: Presents the comparative simulation results of the three controllers using step, sine wave, and random input signals to evaluate their performance.
IV. CONCLUSION: Summarizes the findings and confirms that the NARMA-L2 model based model reference adaptive control system provides the most accurate performance.
Keywords
Nonlinear control, NARMA-L2, Neural networks, Model reference adaptive control, Predictive controller, Arm position sensor, Nerve modulation, System identification, Nonlinear SISO systems, Simulink, Control engineering, Adaptive control, Signal processing, Dynamic systems, Nerve stimulation.
Frequently Asked Questions
What is the primary focus of this research?
This research focuses on the development and comparison of three different neural network-based controllers intended to regulate the arm position of nerve patients via an arm nerve simulator system.
What are the main control approaches explored?
The study examines controllers based on the NARMA-L2 model, NARMA-L2 model system identification based predictive control, and NARMA-L2 model based model reference adaptive control.
What is the ultimate goal of the proposed controllers?
The goal is to determine the most effective method for generating appropriate electrical impulse voltages that cause the arm to reach and maintain a target position accurately.
Which scientific methodology is utilized?
The methodology involves mathematical modeling of the nerve system, system identification using neural networks, and extensive simulation testing under various input conditions (step, sine wave, and random signals) within a Simulink environment.
What does the main body of the paper cover?
The main body covers the mathematical formulation of the nerve system, the structural design of the neural network controllers, and the comparative analysis of their outputs against desired trajectories.
Which keywords characterize this paper?
The paper is characterized by terms such as Nonlinear control, NARMA-L2, Neural networks, Adaptive control, and Nerve stimulation.
Why is the NARMA-L2 model based model reference adaptive control considered the best?
Simulation results demonstrate that this specific model achieves the target position with the highest precision and no overshoot compared to the other two evaluated methods.
How is the performance of the controllers evaluated?
Performance is evaluated by measuring the accuracy of the arm position output relative to a desired input signal and observing the behavior of the impulse voltage across different waveform types.
- Citar trabajo
- Mustefa Jibril (Autor), 2020, Nonlinear Autoregressive Moving Average- L2 Model Based Adaptive Control Of Nonlinear Arm Nerve Simulator System, Múnich, GRIN Verlag, https://www.grin.com/document/542072
