AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
A deep reinforcement learning (DRL)-based PID online gain scheduling control framework for precise tracking of a DC motor achieves higher tracking accuracy and effectively mitigates control signal oscillations, thereby enhancing overall control robustness and practical feasibility.
Quadcopters offer great utility in many applications, but their nonlinear nature and disturbance sensitivity present great control challenges. Basic PID controllers are generally not sophisticated enough to cope with these complexities. This paper suggests a control system that integrates the Asynchronous Advantage Act...
This paper addresses experimental PID tuning for DC motor actuators when an accurate plant model is unavailable or impractical to obtain. Controller tuning is formulated as a constrained closed-loop optimization process in which each PID gain set is deployed on the physical system, produces an experimental dataset, and...
This research introduces an innovative control technique for Series Elastic Actuators (SEAs) that utilizes Reinforcement Learning (RL) to address the shortcomings of previously fixed-gain adaptive controllers, which are a hybrid of State Feedback Control and Model Reference Adaptive Control by using Lyapunov Stability...
Direct current (DC) servo motors are essential in industrial automation and robotics, where precise position and speed control are critical; however, they require high-performance control strategies. This study investigates an experimental framework for the modeling and control of a DC servo motor using system identifi...
The DDPG-DLPID, an adaptive motion controller, including a Deep Deterministic Policy Gradient (DDPG) reinforcement learning that can acquire the parameters of PID controllers is introduced, which achieves faster response and higher tracking accuracy, while substantially reducing tracking errors under interference condi...
This paper addresses a robust tracking problem for linear discrete-time systems by proposing a reinforcement learning (RL) control method based on a diagonal-scaling strategy, offering a solution tailored to the demands of enhanced reliability. To overcome a common limitation in policy-iteration-based RL design, namely...
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.