Automatic design and tuning of a fuzzy system for controlling the Acrobot using genetic algorithms, DSFS, and meta-rule techniques

Lee, M.A.; Smith, Michael H.

doi:10.1109/ijcf.1994.375076

Cited by 12 publications

(3 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…He exactly linearized dynamics of either the first or second joint as part of his approach. Lee and Smith [16] also looked at the swing-up problem but used fuzzy control methods instead. Saito et al [22] were able to make a twolink robot swing from rung to rung on a ladder-like apparatus, mimicking monkey locomotion.…”

Section: Introductionmentioning

confidence: 98%

Tracking fast inverted trajectories of the underactuated Acrobot

Berkemeier

Fearing

1999

IEEE Trans. Robot. Automat.

145

View full text Add to dashboard Cite

The control of underactuated robot manipulators provides a significant challenge to the robotics engineer. The Acrobot is a simple underactuated system consisting of a double pendulum with an actuator at only the second joint. Previous work has shown how linearization methods can achieve the tracking of slow inverted trajectories. In this paper we derive a surprising set of exact trajectories of the nonlinear equations of motion, which involve inverted periodic motions. The trajectories can be made arbitrarily fast by appropriate choice of the Acrobot mass and length parameters. Next, we present a nonlinear control law (which has appeared elsewhere) and show how it can be applied to the Acrobot to track these trajectories. In simulations we compare tracking results for our controller and one based on pseudolinearization. The pseudolinearizing controller produces significant error for a 1 Hz trajectory, while ours produces none. Finally, we present experimental results which demonstrate that the assumptions of the theory were not overly restrictive. In particular, peak-to-peak oscillations of joint 2 as large as 85 were obtained, despite real-world effects, such as joint friction, inexact parameter values, and noisy and delayed joint velocity data.

show abstract

Section: Introductionmentioning

confidence: 98%

Tracking fast inverted trajectories of the underactuated Acrobot

Berkemeier

Fearing

1999

IEEE Trans. Robot. Automat.

145

View full text Add to dashboard Cite

show abstract

“…Lee and Smith [8] have described a fuzzy control method that combines genetic algorithms, dynamic switching fuzzy systems and meta-rule techniques for the automatic design and tuning of an acrobot fuzzy control system. The genetic algorithms utilize PD control results.…”

Section: Introductionmentioning

confidence: 99%

“…The model-based fuzzy controller, which uses the Takagi-Sugeno fuzzy model, employs the concept of parallel distributed compensation. Unlike the methods of [8,9], the design is simple, and the stability of the fuzzy control system for balance control is guaranteed by a common symmetric positive matrix, which satisfies linear matrix inequalities (LMIs) and is found by a convex optimization technique. Since the Takagi-Sugeno fuzzy model describes the acrobot with a satisfactory approximated precision over a large region, the model-based fuzzy balancing control law makes the region for balance control larger than it is with LQR.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy control strategy for acrobots combining model-free and model-based control

Lai

She

Ohyama

et al. 1999

IEE Proceedings - Control Theory and Applications

View full text Add to dashboard Cite

This paper describes a fuzzy control strategy for the control of an acrobot. The strategy combines model-free and model-based fuzzy control. The model-free fuzzy controller designed for the upswing ensures that the energy of the acrobot increases with each swing. The control law for the torque is derived directly from the energy of the acrobot. The model-based fuzzy controller, which is based on a Takagi-Sugeno fuzzy model for balancing, employs the concept of parallel distributed compensation. The stability of the fuzzy control system for balance control is guaranteed by a common symmetric positive matrix, which satisfies linear matrix inequalities and is found by a convex optimization technique.

show abstract

Sliding and hopping gaits for the underactuated Acrobot

Berkemeier

Fearing

1998

IEEE Trans. Robot. Automat.

View full text Add to dashboard Cite

Automatic design and tuning of a fuzzy system for controlling the Acrobot using genetic algorithms, DSFS, and meta-rule techniques

Cited by 12 publications

References 6 publications

Tracking fast inverted trajectories of the underactuated Acrobot

Tracking fast inverted trajectories of the underactuated Acrobot

Fuzzy control strategy for acrobots combining model-free and model-based control

Sliding and hopping gaits for the underactuated Acrobot

Contact Info

Product

Resources

About