Description
Learning Control: Applications in Robotics and Complex Dynamical Systems provides a foundational understanding of control theory while also introducing exciting cutting-edge technologies in the field of learning-based control. State-of-the-art techniques involving machine learning and artificial intelligence (AI) are covered, as are foundational control theories and more established techniques such as adaptive learning control, reinforcement learning control, impedance control, and deep reinforcement control. Each chapter includes case studies and real-world applications in robotics, AI, aircraft and other vehicles and complex dynamical systems. Computational methods for control systems, particularly those used for developing AI and other machine learning techniques, are also discussed at length.
- 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
Table of Contents
- 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
