For an example that replaces the PI controller with a neural network controller, see Create Simulink Environment and Train Agent. This example uses a reinforcement learning (RL) agent to compute the gains for a PI controller. To facilitate the controller comparison, both tuning methods use a linear quadratic Gaussian (LQG) objective function. However, RL methods can be more suitable for highly nonlinear systems or adaptive controller tuning. Using the Control System Tuner app to tune controllers in Simulink® requires Simulink Control Design™ software.įor relatively simple control tasks with a small number of tunable parameters, model-based tuning techniques can get good results with a faster tuning process compared to model-free RL-based methods. Pi sym(pi, 'd') if you want it treated as symbolic 3.1415926535897931159979634685442 but in general remember that any numeric value in a symbolic expression might get converted to symbolic form, like sym(sqrt(2)) will be converted to sym(2)(sym(1)/sym(2)). The performance of the tuned controller is compared with that of a controller tuned using the Control System Tuner app. This example shows how to tune a PI controller using the twin-delayed deep deterministic policy gradient (TD3) reinforcement learning algorithm.
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