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Reinforcement learning control
Current Opinion in Neurobiology, 1994Reinforcement learning refers to improving performance through trial-and-error. Despite recent progress in developing artificial learning systems, including new learning methods for artificial neural networks, most of these systems learn under the tutelage of a knowledgeable 'teacher' able to tell them how to respond to a set of training stimuli ...
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Study of Learning Fuzzy Controllers
Expert Systems, 2001This paper compares two types of learning fuzzy controllers, the self‐organizing fuzzy (SOF) controller and the hybrid self‐organizing fuzzy proportional–integral–derivative (SOF‐PID) controller. The SOF is an extension of the rule‐based fuzzy controller, with additional rule creation and rule modification mechanisms.
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Neighbor Learning Control: Learning Control for Multiple Subsystems
Volume 9: Mechanical Systems and Control, Parts A, B, and C, 2007With the rise of smart material actuators, it has become possible to design and build systems with a large number of small actuators. Many of these actuators exhibit a host of nonlinearities including hysteresis. Learning control algorithms can be used to guarantee good convergence of these systems even in the presence of the nonlinearities.
Manas C. Menon, H. Harry Asada
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On the P-type learning control
IEEE Transactions on Automatic Control, 1994Sufficient conditions for the robustness and convergence of P-type learning control algorithms for a class of time-varying, nonlinear systems are presented. The authors prove the uniform boundedness of the system state and the input control with respect to the existence of errors of initialization, measurement noises, and fluctuations of system ...
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Integrated Learning: Controlling Explanation
Cognitive Science, 1986Similarity‐based learning, which involves largely structural comparisons of instances, and explanation‐based learning, a knowledge‐intensive method for analyzing instances to build generalized schemata, are two major inductive learning techniques in use in Artificial Intelligence.
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Learning force control with position controlled robots
Proceedings of IEEE International Conference on Robotics and Automation, 2002The paper applies a previously presented method for accurate tracking of paths to force control. This approach is very simple since it does not require a joint torque/motor current interface but only a positional interface. It can be applied with elastic end-effectors (sensors) as well as with stiff environments where most elasticity is in the robot ...
Lange, F., Hirzinger, G.
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Learning to Coordinate Controllers - Reinforcement Learning on a Control Basis. [PDF]
, 1997 Autonomous robot systems operating in an uncertain environment have to be reactive and adaptive in order to cope with changing environment conditions and task requirements. To achieve this, the hybrid control architecture presented in this paper uses reinforcement learning on top of a Discrete Event Dynamic System (DEDS) framework to learn to supervise
Huber, Manfred, Grupen, Roderic
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Bilateral Transfer for Learning to Control Timing but Not for Learning to Control Fine Force
Perceptual and Motor Skills, 2014This study examined the characteristics of bilateral transfer of learning to control timing and fine force from a dominant limb to a nondominant limb. 20 right-handed college students (12 women, 8 men; M age = 21.5 yr., SD = 2.3) learned a sequential task consisting of timing and force control.
Wan X, Yao +4 more
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2018
This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement ...
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This chapter discusses basic concepts from control theory and machine learning to facilitate a formal understanding of animal learning and motor control. It first distinguishes between feedback and feed-forward control strategies, and later introduces the classification of machine learning applications into supervised, unsupervised, and reinforcement ...
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Machine learning for microbiologists
Nature Reviews Microbiology, 2023Francesco Asnicar +2 more
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