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MRAC algorythm for a nuclear reactor control system
- Citation Author(s):
- Submitted by:
- Alejandro Paxi Silva
- Last updated:
- Fri, 02/16/2024 - 20:11
- DOI:
- 10.21227/z3k2-js02
- Research Article Link:
- License:
60 Views- Categories:
- Keywords:
Abstract
This code implements an adaptive control system comprising a plant model, reference model, and adaptive controller. The plant and reference models are represented as linear state-space systems, while the adaptive controller employs a nonlinear IO system to adjust control parameters based on input signals and system states. The adaptive controller's internal dynamics are governed by differential equations, facilitating real-time adjustments to the control law coefficients. Through simulation, the effectiveness of the adaptive control system is evaluated under varying feedback gains (gamma), demonstrating its ability to regulate the plant output towards the reference model despite uncertainties and disturbances. Additionally, the evolution of control gains over time is analyzed to validate the adaptability and stability of the control system.
To execute the provided Python code, ensure the required libraries (numpy, scipy, matplotlib, and control) are installed. Import these libraries, define the plant and reference models as linear state-space systems, and set up the adaptive controller with its internal state functions. Configure the parameters for the controller and create a nonlinear input-output system for it. Interconnect the plant, reference model, and controller systems to form the closed-loop control system. Specify simulation parameters including duration and time step, define the control reference input, and plot it. Set initial conditions for the closed-loop system and simulate it with varying parameter values. Plot the system response, including the plant output, reference model output, and control signal, and analyze the evolution of control gains over time. Display the plots to visualize the simulation results.
