Non-Linear Control for Underactuated Mechanical Systems
Non-Linear Control for Underactuated Mechanical Systems
Nonlinear control of underactuated mechanical systems with application to robotics and aerospace vehicles
Hybrid Control for an Autonomous Wheeled Mobile Robot Under Perturbed Torques
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Information Sciences: an International Journal
Adaptive fuzzy logic control of dynamic balance and motion for wheeled inverted pendulums
Fuzzy Sets and Systems
Exploration and exploitation balance management in fuzzy reinforcement learning
Fuzzy Sets and Systems
Sliding-mode velocity control of mobile-wheeled inverted-pendulum systems
IEEE Transactions on Robotics
H∞ and mixed H2/H∞ control of discrete-time T--S fuzzy systems with local nonlinear models
Fuzzy Sets and Systems
Velocity and position control of a wheeled inverted pendulum by partial feedback linearization
IEEE Transactions on Robotics
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In this paper, we propose synthesized design of a fuzzy logic controller (FLC) for control of an underactuated unicycle system. The FLC objective is velocity control of the wheel while keeping the pendulum upright, which is an unstable equilibrium. The synthesized design consists of three phases. First, the FLC structures including the number of rules, membership functions, inference and parametric relations are chosen based on heuristic knowledge about the unicycle. Second, on the basis of a linearized model and linear feedback, the FLC output parameters are determined quantitatively for stabilization of the unicycle. Third, the FLC output parameters are tuned using an iterative learning tuning (ILT) algorithm, which minimizes an objective function that specifies the desired unicycle performance. The rationale for the synthesized FLC design is full utilization of the available information, which is achieved by combining model-based and model-free designs, and hence improves the FLC performance. We minimize the number of FLC rules and fuzzy labels. Six rules are used for regulation or setpoint tasks, whereas 10 rules are used with extra integral control to eliminate steady-state errors induced by system uncertainties and disturbances. Only two fuzzy labels are adopted for each fuzzy variable. The ILT process consists of two phases, exploration for stabilization and exploitation for better performance. The effectiveness of the proposed FLC is validated using intensive simulations and comparisons.