Genetic control of a ball-beam system

Marra, M.A.; Boling, Bryan; Walcott, B.L.

doi:10.1109/cca.1996.558930

Cited by 7 publications

(5 citation statements)

References 12 publications

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“…(Lai et al [1994]), proposed a tracking controller based on approximate backstepping (aBS) that has better steady state error than other approximation methods. Other approaches for the B&B control problem include a fuzzy controller (Yi et al [1996]) and a genetic controller (Marra et al [1996]). This paper tries to generalize the control problem to non-regular systems.…”

Section: Fig 1 the Ball Beam Systemmentioning

confidence: 99%

See 1 more Smart Citation

Sliding Mode Control and Feedback Linearization for Non-regular Systems

Zhang

Fernandez

Mosterman

et al. 2008

IFAC Proceedings Volumes

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This paper presents an approach for sliding mode control(SMC) and feedback linearization(FBL) of systems with relative order singularities. Traditionally, SMC and FBL are designed by taking derivatives of the output until the control signal appears(at the r th 1 derivative). When a system does not have a well-defined relative degree (r 1 ), the coefficient that multiplies u vanishes for some region of the state-space S 1 . In this instance, conventional SMC and FBL techniques fail. The presented approach differentiates further the output until the control input appears again (the secondary relative degree, r 2 ) and a differential equation in u is acquired. It may be possible to solve for a dynamic compensator, or in the neighborhood of the singularity, N 1 , the equations degenerate to a polynomial form. Preliminary results show that at the singularity region, S 1 , the control-derivative term disappears and the differential equation is degenerated to a center manifold defined by a polynomial (quadratic in general) equation on u. The solution to the quadratic equations is discussed. When this equation has only real roots, the system is well defined at the singularity. A switching controller can be designed to switch from the r th 1 controller when system is far away from the singularity to the r th 2 controller when the system is in the neighborhood of the singularity. We demonstrate the controller applied to the ball and beam system.

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Section: Fig 1 the Ball Beam Systemmentioning

confidence: 99%

“…1) is one of the most popular models for studying control systems because of its simplicity and yet the control techniques that can be studied cover many important control methods (Barbu et al [1997], Hauser et al [1992], Lai et al [1994], Leith and Leithead [2001], Marra et al [1996], Tomlin and Sastry [1997], Yi et al [1996]). …”

Section: Introductionmentioning

confidence: 99%

Sliding Mode Control and Feedback Linearization for Non-regular Systems

Zhang

Fernandez

Mosterman

et al. 2008

IFAC Proceedings Volumes

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“…To stabilize the ball, a feedback control system that measures the position of the ball and adjusts the beam accordingly must be designed. The control strategies for the BBS involve different techniques such as PID control [1], [2], state feedback control [3], fuzzy control [4]- [6], neural network control [7], fuzzy neural control [8], sliding mode control [9], and genetic algorithm control [10].…”

Section: Introductionmentioning

confidence: 99%

Augmented state estimation and LQR control for a ball and beam system

Pang

Zhang

Luo³

2011

2011 6th IEEE Conference on Industrial Electronics and Applications

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Based on the strong tracking filter (STF), a STF-LQR control approach is presented for the control of a ball and beam system (BBS). The simple linearized mathematical model of the BBS is first obtained according to physics laws. In order to model the BBS accurately, an additional control action is introduced as an augmented state variable to compensate for the unmodeled dynamics, such as friction, nonlinearity, disturbance and time-varying characteristics. All the states are adaptively estimated by using the STF, and the controller is designed by employing the discrete-time LQR technique. A BBS control test rig is built based on the NetCon system. Practical experiments have been carried out to demonstrate the feasibility and effectiveness of the proposed approach.

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“…Evolutionary computation techniques have been used successfully to solve benchmark control problems including the inverted pendulum [21] and the ball and beam [16] problems. In addition, they have been used in real world applications such assearching through a space of plans generated from a planning algorithm to yield good control policies in a planetary rover control problem [9] and using "sub-populations" to control rockets [11] .…”

Section: Introductionmentioning

confidence: 99%

A neuro-evolutionary approach to control surface segmentation for micro aerial vehicles

Salichon

Tumer

2013

International Journal of General Systems

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This paper addresses control surface segmentation in Micro Aerial Vehicles (MAVs) by leveraging neuro-evolutionary techniques that allow the control of a higher number of control surfaces. Applying classical control methods to MAVs is a difficult process due to the complexity of the control laws with fast and highly non-linear dynamics. These methods are mostly based on models that are difficult to obtain for dynamic and stochastic environments. Moreover, these problems are exacerbated when both the number of control surfaces increases and the model's accuracy in determining the impact of each control surface decreases. Instead, we focus on neuro-evolutionary techniques that have been successfully applied in many domains with limited models and highly non-linear dynamics. Wind tunnel simulations with AVL show that MAV performances are improved both in terms of reduced deflection angles and reduced drag (up to 5%) over a simplified model in two sets of experiments with different objective functions. We also show robustness to actuator failure with desired roll moment values still attained with failed actuators in the system through the neuro-controller.

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Genetic control of a ball-beam system

Cited by 7 publications

References 12 publications

Sliding Mode Control and Feedback Linearization for Non-regular Systems

Sliding Mode Control and Feedback Linearization for Non-regular Systems

Augmented state estimation and LQR control for a ball and beam system

A neuro-evolutionary approach to control surface segmentation for micro aerial vehicles

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