An introduction to fuzzy control
An introduction to fuzzy control
An introduction to intelligent and autonomous control
An introduction to intelligent and autonomous control
Adaptive fuzzy systems and control: design and stability analysis
Adaptive fuzzy systems and control: design and stability analysis
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Robot Dynamics and Control
Intelligent Control Systems: Theory and Applications
Intelligent Control Systems: Theory and Applications
Dynamically focused fuzzy learning control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Brief paper: Sliding mode control of a class of underactuated systems
Automatica (Journal of IFAC)
Restricted gradient-descent algorithm for value-function approximation in reinforcement learning
Artificial Intelligence
Singularity avoidance for acrobots based on fuzzy-control strategy
Robotics and Autonomous Systems
On the Continuous Control of the Acrobot via Computational Intelligence
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
International Journal of Robotics Research
Comprehensive unified control strategy for underactuated two-link manipulators
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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The acrobot is an underactuated two-link planar robot that mimics the human acrobat who hangs from a bar and tries to swing up to a perfectly balanced upside-down position with his/her hands still on the bar. In this paper we develop intelligent controllers for swing-up and balancing of the acrobot. In particular, we first develop classical, fuzzy, and adaptive fuzzy controllers to balance the acrobot in its inverted unstable equilibrium region. Next, a proportional-derivative (PD) controller with inner-loop partial feedback linearization, a state-feedback, and a fuzzy controller are developed to swing up the acrobot from its stable equilibrium position to the inverted region, where we use a balancing controller to ‘catch’ and balance it. At the same time, we develop two genetic algorithms for tuning the balancing and swing-up controllers, and show how these can be used to help optimize the performance of the controllers. Overall, this paper provides (i) a case studyof the development of a variety of intelligent controllers for a challenging application, (ii) a comparative analysis of intelligent vs. conventional control methods (including the linear quadratic regulator and feedback linearization) for this application, and (iii) a case study of the development of genetic algorithms for off-line computer-aided-design of both conventional and intelligent control systems.