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    Deep Reinforcement Learning-Based Control of Stewart Platform With Parametric Simulation in ROS and Gazebo
    (Asme, 2023) Yadavari, Hadi; Aghaei, Vahid Tavakol; Ikizoglu, Serhat
    The Stewart platform is an entirely parallel robot with mechanical differences from typical serial robotic manipulators, which has a wide application area ranging from flight and driving simulators to structural test platforms. This work concentrates on learning to control a complex model of the Stewart platform using state-of-the-art deep reinforcement learning (DRL) algorithms. In this regard, to enhance the reliability of the learning performance and to have a test bed capable of mimicking the behavior of the system completely, a precisely designed simulation environment is presented. Therefore, we first design a parametric representation for the kinematics of the Stewart platform in Gazebo and robot operating system (ROS) and integrate it with a Python class to conveniently generate the structures in simulation description format (SDF). Then, to control the system, we benefit from three DRL algorithms: the asynchronous advantage actor-critic (A3C), the deep deterministic policy gradient (DDPG), and the proximal policy optimization (PPO) to learn the control gains of a proportional integral derivative (PID) controller for a given reaching task. We chose to apply these algorithms due to the Stewart platform's continuous action and state spaces, making them well-suited for our problem, where exact controller tuning is a crucial task. The simulation results show that the DRL algorithms can successfully learn the controller gains, resulting in satisfactory control performance.
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    Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm
    (Elsevier Sci Ltd, 2023) Aghaei, Vahid Tavakol; Agababaoglu, Arda; Bawo, Biram; Naseradinmousavi, Peiman; Yildirim, Sinan; Yesilyurt, Serhat; Onat, Ahmet
    This study focuses on the numerical analysis and optimal control of vertical-axis wind turbines (VAWT) using Bayesian reinforcement learning (RL). We specifically address small-scale wind turbines, which are well-suited to local and compact production of electrical energy on a small scale, such as urban and rural infrastructure installations. Existing literature concentrates on large scale wind turbines which run in unobstructed, mostly constant wind profiles. However urban installations generally must cope with rapidly changing wind patterns. To bridge this gap, we formulate and implement an RL strategy using the Markov chain Monte Carlo (MCMC) algorithm to optimize the long-term energy output of a wind turbine. Our MCMC-based RL algorithm is a model-free and gradient-free algorithm, in which the designer does not have to know the precise dynamics of the plant and its uncertainties. Our method addresses the uncertainties by using a multiplicative reward structure, in contrast with additive reward used in conventional RL approaches. We have shown numerically that the method specifically overcomes the shortcomings typically associated with conventional solutions, including, but not limited to, component aging, modeling errors, and inaccuracies in the estimation of wind speed patterns. Our results show that the proposed method is especially successful in capturing power from wind transients; by modulating the generator load and hence the rotor torque load, so that the rotor tip speed quickly reaches the optimum value for the anticipated wind speed. This ratio of rotor tip speed to wind speed is known to be critical in wind power applications. The wind to load energy efficiency of the proposed method was shown to be superior to two other methods; the classical maximum power point tracking method and a generator controlled by deep deterministic policy gradient (DDPG) method.
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    Sand cat swarm optimization-based feedback controller design for nonlinear systems
    (Cell Press, 2023) Aghaei, Vahid Tavakol; SeyyedAbbasi, Amir; Rasheed, Jawad; Abu-Mahfouz, Adnan M.
    The control of the open loop unstable systems with nonlinear structure is challenging work. In this paper, for the first time, we present a sand cat swarm optimization (SCSO) algorithm-based state feedback controller design for open-loop unstable systems. The SCSO algorithm is a newly proposed metaheuristic algorithm with an easy-to-implement structure that can efficiently find the optimal solution for optimization problems. The proposed SCSO-based state feedback controller can successfully optimize the control parameters with efficient convergence curve speed. In order to show the performance of the proposed method, three different nonlinear control systems such as an Inverted pendulum, a Furuta pendulum, and an Acrobat robot arm are considered. The control and optimization performances of the proposed SCSO algorithm are compared with well-known metaheuristic algorithms. The simulation results show that the proposed control method can either outperform the compared metaheuristic-based algorithms or have competitive results.