Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Signals & systems (2nd ed.)
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Principles of Dynamic Programming: Basic Analytical and Computational Methods
Principles of Dynamic Programming: Basic Analytical and Computational Methods
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Optimal greenhouse control of tomato-seedling crops
Computers and Electronics in Agriculture
Neural network-based model reference adaptive control system
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neural networks for advanced control of robot manipulators
IEEE Transactions on Neural Networks
Helicopter trimming and tracking control using direct neural dynamic programming
IEEE Transactions on Neural Networks
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This work proposes a neuro-dynamic programming-based optimal controller to guide the growth of tomato seedling crops by manipulating its environmental conditions in a greenhouse. The neurocontroller manages the growth development of the crop, while minimizing a predefined cost function that considers the operative costs and the final state errors under physical constraints on process variables and actuator signals. The aim is to guide the growth of tomato seedlings by controlling the microclimate of the greenhouse. The design process of the neurocontroller considers the nonlinear dynamic behavior of the crop-greenhouse system model and the real climate data. Simulations of the proposed approach allow for contrasting its performance against those of other strategies for tomato seedling crop development subject to various climatic conditions.