Control of Two-Wheel Self-Balancing Robot: LQR and MPC Performance Analysis
- Resource Type
- Conference
- Authors
- Mishra, Ashutosh; Bansal, Kritika
- Source
- 2024 IEEE International Students' Conference on Electrical, Electronics and Computer Science (SCEECS) Electrical, Electronics and Computer Science (SCEECS), 2024 IEEE International Students' Conference on. :1-6 Feb, 2024
- Subject
- Aerospace
Bioengineering
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Engineering Profession
Fields, Waves and Electromagnetics
General Topics for Engineers
Nuclear Engineering
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Computer science
Regulators
Robust stability
Simulation
Predictive models
Robustness
Mathematical models
Linear Quadratic Control
Model Predictive Control
Control Strategies
Stability
Self-balancing Robot
- Language
- ISSN
- 2688-0288
This paper presents the design and comparative analysis of Linear Quadratic Control and Model Predictive Control strategies for a two-wheel self-balancing robot. A detailed mathematical model of the two-wheel self-balancing is provided. After that, the Linear Quadratic Regulator and Model Predictive Control law are designed. In Order to compare the performances of both the controllers, various performance indices such as settling time and peak overshoot value are evaluated. The robustness of these methods is tested against impulse and sinusoidal disturbance. The control signals from linear quadratic regulator and model predictive controllers are also examined. The simulation results are obtained using MATLAB wherein it is observed that model predictive control performed better in both tracing the set points and generating minimal control signal.