Adaptive Control of an Inverted Pendulum by a Reinforcement Learningbased LQR Method

Uğur Yıldıran

Makale detayları

Adaptive Control of an Inverted Pendulum by a Reinforcement Learningbased LQR Method

Yazarlar:

Uğur Yıldıran

Yayıncı:

Sakarya University

Ana Başlıklar:

Electrical Engineering

Dergi:

Sakarya University Journal of Science Vol.27 Issue 6, pp.1311-1321

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001304050

pISSN:

2147-835X

Anahtar kelimeler:

Özet:

 Inverted pendulums constitute one of the popular systems for benchmarking control algorithms. Several methods have been proposed for the control of this system, the majority of which rely on the availability of a mathematical model. However, deriving a mathematical model using physical parameters or system identification techniques requires manual effort. Moreover, the designed controllers may perform poorly if system parameters change. To mitigate these problems, recently, some studies used Reinforcement Learning RL based approaches for the control of inverted pendulum systems. Unfortunately, these methods suffer from slow convergence and local minimum problems. Moreover, they may require hyperparameter tuning which complicates the design process significantly. To alleviate these problems, the present study proposes an LQR-based RL method for adaptive balancing control of an inverted pendulum. As shown by numerical experiments, the algorithm stabilizes the system very fast without requiring a mathematical model or extensive hyperparameter tuning. In addition, it can adapt to parametric changes online.

 Inverted pendulums constitute one of the popular systems for benchmarking control algorithms. Several methods have been proposed for the control of this system, the majority of which rely on the availability of a mathematical model. However, deriving a mathematical model using physical parameters or system identification techniques requires manual effort. Moreover, the designed controllers may perform poorly if system parameters change. To mitigate these problems, recently, some studies used Reinforcement Learning RL based approaches for the control of inverted pendulum systems. Unfortunately, these methods suffer from slow convergence and local minimum problems. Moreover, they may require hyperparameter tuning which complicates the design process significantly. To alleviate these problems, the present study proposes an LQR-based RL method for adaptive balancing control of an inverted pendulum. As shown by numerical experiments, the algorithm stabilizes the system very fast without requiring a mathematical model or extensive hyperparameter tuning. In addition, it can adapt to parametric changes online.

 Inverted pendulums constitute one of the popular systems for benchmarking control algorithms. Several methods have been proposed for the control of this system, the majority of which rely on the availability of a mathematical model. However, deriving a mathematical model using physical parameters or system identification techniques requires manual effort. Moreover, the designed controllers may perform poorly if system parameters change. To mitigate these problems, recently, some studies used Reinforcement Learning RL based approaches for the control of inverted pendulum systems. Unfortunately, these methods suffer from slow convergence and local minimum problems. Moreover, they may require hyperparameter tuning which complicates the design process significantly. To alleviate these problems, the present study proposes an LQR-based RL method for adaptive balancing control of an inverted pendulum. As shown by numerical experiments, the algorithm stabilizes the system very fast without requiring a mathematical model or extensive hyperparameter tuning. In addition, it can adapt to parametric changes online.

 Inverted pendulums constitute one of the popular systems for benchmarking control algorithms. Several methods have been proposed for the control of this system, the majority of which rely on the availability of a mathematical model. However, deriving a mathematical model using physical parameters or system identification techniques requires manual effort. Moreover, the designed controllers may perform poorly if system parameters change. To mitigate these problems, recently, some studies used Reinforcement Learning RL based approaches for the control of inverted pendulum systems. Unfortunately, these methods suffer from slow convergence and local minimum problems. Moreover, they may require hyperparameter tuning which complicates the design process significantly. To alleviate these problems, the present study proposes an LQR-based RL method for adaptive balancing control of an inverted pendulum. As shown by numerical experiments, the algorithm stabilizes the system very fast without requiring a mathematical model or extensive hyperparameter tuning. In addition, it can adapt to parametric changes online.

Makale detayları

Adaptive Control of an Inverted Pendulum by a Reinforcement Learningbased LQR Method

Yazarlar:

Uğur Yıldıran

Yayıncı:

Sakarya University

Ana Başlıklar:

Electrical Engineering

Dergi:

Sakarya University Journal of Science Vol.27 Issue 6, pp.1311-1321

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001304050

pISSN:

2147-835X

Reinforcement learning

LQR

Inverted pendulum

Q-learning

adaptive control

Sayı

Makale detayları

Adaptive Control of an Inverted Pendulum by a Reinforcement Learningbased LQR Method

Yazarlar: Uğur Yıldıran

Yayıncı: Sakarya University

Ana Başlıklar: Electrical Engineering

Dergi: Sakarya University Journal of Science Vol.27 Issue 6, pp.1311-1321

Belge Tipi: Makale

Yayın Tarihi: 12/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001304050

pISSN: 2147-835X

Reinforcement learning

LQR

Inverted pendulum

Q-learning

adaptive control

Sayı

Yazarlar:

Uğur Yıldıran

Yayıncı:

Sakarya University

Ana Başlıklar:

Electrical Engineering

Dergi:

Sakarya University Journal of Science Vol.27 Issue 6, pp.1311-1321

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001304050

pISSN:

2147-835X