A menu of designs for reinforcement learning over time
Neural networks for control
Instance-Based Learning Algorithms
Machine Learning
Technical Note: \cal Q-Learning
Machine Learning
An iterative learning control theory for a class of nonlinear dynamic systems
Automatica (Journal of IFAC)
Automatica (Journal of IFAC) - Special issue on statistical signal processing and control
Statistical optimality and canonical variate analysis system identification
Signal Processing - Special issue: subspace methods, part II: system identification
Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
Adaptive-predictive control of a class of SISO nonlinear systems
Dynamics and Control
Iterative Identification and Control: Advances in Theory and Applications
Iterative Identification and Control: Advances in Theory and Applications
Designing guide-path networks for automated guided vehicle system by using the Q-learning technique
Computers and Industrial Engineering
Practical Reinforcement Learning in Continuous Spaces
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Linear and Nonlinear Iterative Learning Control (Lecture Notes in Control and Information Sciences, 291)
Exact and Approximate Modeling of Linear Systems: A Behavioral Approach (Mathematical Modeling and Computation) (Mathematical Modeling and Computation)
Correlation-based tuning of decoupling multivariable controllers
Automatica (Journal of IFAC)
Direct data-driven recursive controller unfalsification with analytic update
Automatica (Journal of IFAC)
A Q-learning approach to derive optimal consumption and investment strategies
IEEE Transactions on Neural Networks
Multi-model unfalsified adaptive switching supervisory control
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
New model for system behavior prediction based on belief rule based systems
Information Sciences: an International Journal
Information Sciences: an International Journal
Information Sciences: an International Journal
Short communication: Model free adaptive control with data dropouts
Expert Systems with Applications: An International Journal
A clustering algorithm for multiple data streams based on spectral component similarity
Information Sciences: an International Journal
Iterative Learning Control: Brief Survey and Categorization
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Brief Direct iterative tuning via spectral analysis
Automatica (Journal of IFAC)
Brief Virtual reference feedback tuning: a direct method for the design of feedback controllers
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Data-Driven Model-Free Adaptive Control for a Class of MIMO Nonlinear Discrete-Time Systems
IEEE Transactions on Neural Networks - Part 2
Guest Editorial Data-Based Control, Modeling, and Optimization
IEEE Transactions on Neural Networks - Part 2
Adaptive fuzzy clustering based anomaly data detection in energy system of steel industry
Information Sciences: an International Journal
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This paper is a brief survey on the existing problems and challenges inherent in model-based control (MBC) theory, and some important issues in the analysis and design of data-driven control (DDC) methods are here reviewed and addressed. The necessity of data-driven control is discussed from the aspects of the history, the present, and the future of control theories and applications. The state of the art of the existing DDC methods and applications are presented with appropriate classifications and insights. The relationship between the MBC method and the DDC method, the differences among different DDC methods, and relevant topics in data-driven optimization and modeling are also highlighted. Finally, the perspective of DDC and associated research topics are briefly explored and discussed.