Modeling and optimal control of a rotary crane using the straight transfer transformation method

A neuro-controller for vibration control of load in a rotary crane system is proposed involving the rotation about the vertical axis only. As in a nonholonomic system, the vibration control method using a static continuous state feedback cannot stabilize the load swing. It is necessary to design a time-varying feedback controller or a discontinuous feedback controller. We propose a simple three-layered neural network as a controller (NC) with genetic algorithm-based (GA-based) training in order to control load swing suppression for the rotary crane system. The NC is trained by a real-coded GA, which substantially simplifies the design of the controller. It appeared that a control scheme with performance comparable to conventional methods can be obtained by a relatively simple approach.

Vibration control of load for rotary crane system using neural network with GA-based training

The simulation of a jib crane control strategy is reported which transfers a load under the condition of suppressing the load swing both in the transfer process and at the objective position, making the transfer time as short as possible. A non-linear dynamic equation of the load in a jib crane motion is derived. By linearizing the equation and giving a cost function of the second order, the control problem becomes LQ-type with control constraint u. Computer simulations show that this control strategy is able to suppress the load swing by the extending or shrinking motion of the boom.

Jib crane control to suppress load swing

In this paper, we propose a control method for a rotary crane system using Direct Gradient Descent Control (DGDC) optimized by a genetic algorithm (GA). The rotary crane is known to be a class of nonholonomic system and the DGDC method is useful for the nonholonomic system control. However, the control performance of the DGDC strongly depends on the parameters of controller and the value of initial control inputs. Also, for usage of the DGDC, it is required to re-design iteratively the control parameters or the control inputs. We applied GA optimization to determine the sets of control parameters of the DGDC for the crane system. The parameters of the DGDC optimized by GA are not required to be re-designed. Simulation results show that the proposed method has higher control performance and has good robustness with noise and fluctuation of the initial states.

http://www.jstage.jst.go.jp/article/iscie/22/8/22_8_303/_pdf

Takafumi Itoh

Papers

Theoretical Consideration on a Control of Jib-Crane