Learning Control Applications in Robotics and Complex Dynamical Systems
Coordonnateurs : Zhang Dan, Wei Bin
- A high-level design process for neural-network controls through a framework of human personalities
- Cognitive load estimation for adaptive human–machine system automation
- Comprehensive error analysis beyond system innovations in Kalman filtering
- Nonlinear control
- Deep learning approaches in face analysis
- Finite multi-dimensional generalized Gamma Mixture Model Learning for feature selection
- Variational learning of finite shifted scaled Dirichlet mixture models
- From traditional to deep learning: Fault diagnosis for autonomous vehicles
- Controlling satellites with reaction wheels
- Vision dynamics-based learning control
Bin Wei is an Assistant Professor at Algoma University, Ontario, Canada. He received his Ph.D. in robotics from University of Ontario Institute of Technology, Canada, in 2016. He conducts research in the areas of robotics, control theory, and computational mechanics. He has co-edited 5 books on robotic mechanics.
- Provides foundational control theory concepts, along with advanced techniques and the latest advances in adaptive control and robotics
- Introduces state-of-the-art learning-based control technologies and their applications in robotics and other complex dynamical systems
- Demonstrates computational techniques for control systems
- Covers iterative learning impedance control in both human-robot interaction and collaborative robots
Date de parution : 12-2020
Ouvrage de 280 p.
15x22.8 cm
Thèmes de Learning Control :
Mots-clés :
adaptive control; autonomous vehicles; backstepping control; Cognitive load detection; convolutional neural networks; Data clustering; deep autoencoder; deep belief; deep learning; detection; direct adaptive control; eye-tracking; face alignment; face analysis; face attribute estimation; face detection; face frontalization; facial expression recognition; fault diagnosis; feedback linearization; Finite mixture models; gravity compensation; human–computer interface; learning based control; learning control; linear quadratic regulator; Lyapunov; machine learning; Medical applications; nonlinear control; personality theory; pose estimation; psychophysiological signals; pupil dilation; robotic manipulators; Shifted-scaled Dirichlet distributions; signal processing; sliding mode control; Spam detection; super-resolution; trajectory tracking; Unsupervised learning; Variational inference; Vision Dynamics; visual control