Fault Tolerant Control Using Reinforcement Learning and Particle Swarm Optimization
نویسندگان
چکیده
منابع مشابه
Particle swarm optimization for generating interpretable fuzzy reinforcement learning policies
Fuzzy controllers are efficient and interpretable system controllers for continuous state and action spaces. To date, such controllers have been constructed manually or trained automatically either using expert-generated problem-specific cost functions or incorporating detailed knowledge about the optimal control strategy. Both requirements for automatic training processes are not found in most...
متن کاملOptimal Rotor Fault Detection in Induction Motor Using Particle-Swarm Optimization Optimized Neural Network
This study examined and presents an effective method for detection of failure of conductor bars in the winding of rotor of induction motor in low load conditions using neural networks of radial-base functions. The proposed method used Hilbert method to obtain the stator current signal push. The frequency and signal amplitude of the push stator were used as the input of the neural network and th...
متن کاملParallel Fault Tolerant Multi-Agent Reinforcement Learning
Reinforcement learning is a powerful tool for training an agent in a sequential decision based environment and has been successful in many simulated [6] as well as practical [5] domains. In this paper we investigate methods of strengthening the rate of convergence of a single agent RL learner by sharing observations with other independent agents. In contrast to multi-agent reinforcement methods...
متن کاملFrequency Control of Isolated Hybrid Power Network Using Genetic Algorithm and Particle Swarm Optimization
This paper, presents a suitable control system to manage energy in distributed power generation system with a Battery Energy Storage Station and fuel cell. First, proper Dynamic Shape Modeling is prepared. Second, control system is proposed which is based on Classic Controller. This model is educated with Genetic Algorithm and particle swarm optimization. The proposed strategy is compared with ...
متن کاملFeedback learning particle swarm optimization
In this paper, a feedback learning particle swarm optimization algorithm with quadratic inertia weight (FLPSOQIW) is developed to solve optimization problems. The proposed FLPSO-QIW consists of four steps. Firstly, the inertia weight is calculated by a designed quadratic function instead of conventional linearly decreasing function. Secondly, acceleration coefficients are determined not only by...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.3022893