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Everything about Machine learning control
Subfield of machine learning, intelligent control, and control theory
Machine learning control (MLC) is a subfield of machine learning, intelligent control, and control theory which aims to solve optimal control problems with machine learning methods. Key applications are complex nonlinear systems for which linear control theory methods are not applicable. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Machine learning controlA 5-minute orientation
- Skim a recent paper: Intelligent control of nanoparticle synthesis through machine learningInfluential work since 2022
- Pick up a classic book: Advanced Computing and Systems for SecurityThe most-reprinted book on the subject
- Read the landmark paper: Approximate model predictive building control via machine learningCited 236 times
- Go sideways: Gekko (optimization software)A closely related topic
- Go sideways: Value functionA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Approximate model predictive building control via machine learning
Machine Learning for Soft Robotic Sensing and Control
Closed-loop separation control using machine learning
Machine learning for microfluidic design and control
Machine learning-based automatic control of tunneling posture of shield machine
Machine Learning Algorithms in Bipedal Robot Control
Explainable machine learning for project management control
Jet mixing optimization using machine learning control
🆕 Influential since 2022
Intelligent control of nanoparticle synthesis through machine learning
Electrospinning Technology, Machine Learning, and Control Approaches: A Review
Multibody dynamics and control using machine learning
Enhancing quality control in bioprinting through machine learning
Machine learning for power system stability and control
📚 Books worth reading
Advanced Computing and Systems for SecurityRituparna Chaki, Agostino Cortesi · 2015- 📕Data Driven Approaches for Health CareChengliang Yang, Chris Delcher · 2019
Recent Advances in Robot LearningSebastian Thrun · 1996
Machine Learning and Deep Learning Techniques in Wireless and Mobile Networking SystemsK. Suganthi, R. Karthik · 2021
Iterative Learning Control for Multi-agent Systems CoordinationShiping Yang, Jian-Xin Xu · 2017
Enhancing Software Fault Prediction with Machine LearningEkbal Rashid · 2017
Reinforcement learning and approximate dynamic programming for feedback controlFrank L. Lewis, Derong Liu · 2012
Robot Learning Human Skills and Intelligent Control DesignChenguang Yang, Chao Zeng · 2021
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Gekko (optimization software) The GEKKO Python package solves large-scale mixed-integer and differential algebraic equations with nonlinear programming solvers (IPOPT, APOPT, BPOPT…
- Value function The value function of an optimization problem gives the value attained by the objective function at a solution, while only depending on the parameters…
- Reinforcement learning In machine learning and optimal control, reinforcement learning (RL) (also known as approximate dynamic programming) is concerned with how an intelligent…
- Model predictive control Model predictive control (MPC) is an advanced method of process control that is used to control a process while satisfying a set of constraints. Model…
- Ant colony optimization algorithms In computer science and operations research, the ant colony optimization algorithm (ACO) is a probabilistic technique for solving computational problems…
- Nonlinear system identification System identification is a method of identifying or measuring the mathematical model of a system from measurements of the system inputs and outputs. The…
- Optimal control Optimal control theory is a branch of control theory that deals with finding a control for a dynamical system over a period of time such that an objective…
- Estimation of distribution algorithm Estimation of distribution algorithms (EDAs), sometimes called probabilistic model-building genetic algorithms (PMBGAs), are stochastic optimization methods…