A pure neural network controller for double‐pendulum crane anti‐sway control: Based on Lyapunov stability theory

Abstract

Crane systems have been widely applied in logistics due to their efficiency of transportation. The parameters of a crane system may vary from each transport, therefore the anti-sway controller should be designed to be insensitive to the variation of system parameters. In this paper, we focus on pure neural network adaptive tracking controller design issue that does not require the parameters of crane systems, i.e. the trolley mass, the payload mass, the cable lengths, and etc. The proposed neural network controller only requires the output feedback signals of the trolley, i.e. the position and the velocity, which means no sway measuring equipment is needed. The Lyapunov method is utilized to design the weights update law of neural network, and the robustness of the proposed controller is proved by the Lyapunov stability theory. The results of numerical simulations show that the proposed neural network controller has excellent performance of trolley position tracking and payload anti-sway controlling. KEYWORDS adaptive control, anti-sway control, double-pendulum crane systems, Lyapunov stability theory, neural network control 1 | INTRODUCTION Crane systems with cable hoisting mechanism are typically under-actuated systems. They play an important role in logistics and are widely used in container harbors, railway container yards, and other industrial factories. As cable hoisting mechanism is adopted, the weight of the crane is reduced, but the payload swings unavoidably during the transportation as well as reaching the desired position. The sway of the payload decreases the efficiency of crane's transportation and may result in hazards, such as collision, tip over, and etc. To improve the effectiveness and the safety of crane, researchers proposed many anti-sway control strategies to position the trolley accurately and eliminate the payload sway. All the anti-sway control strategies aim to: i) move the trolley safely, fast, and accurately to the desired position, and ii) decrease the sway of the payload as much as possible during the transportation and eliminate the residual sway when reaching the desired position. TIf the parameters of crane systems are exactly known, open-loop control strategies can work effectively. Singhose et al. reviewed the command shaping/input shaping [1] and implemented the method on doublependulum crane systems [2,3]. It shows that the method

Partial state feedback sliding mode control for double-pendulum overhead cranes with unknown disturbances

“…Assumption 1. Reference 18,25,37 The payload and the hook are both considered mass points. The cables are rigid and massless, and their length is constant.…”

“…According to the above assumption and using the Lagrange’s equations, one can derive the dynamic equations of the double-pendulum crane system as follows 18,25,37 …”

Anti-swing radial basis neuro-fuzzy linear quadratic regulator control of double link rotary pendulum

“…The FLQR controller is a combination of the optimal control approach (LQR) and the fuzzy logic controller (FLC) method. [11][12][13][14][15][16][17][18][19][20][21] The multiple variables are transformed into error (e) and error derivative ( _ e) which simplifies the FLC controller. e and _ e are the summing of positions and velocities of state variables multiplied by their LQR gains, respectively.…”

“…[13][14][15] In order to examine the stability of the radial basis function neural network (RBFNN) controllers at anti-swing control of the pendulum crane system, the Lyapunov stability theorem was explained thoroughly in previous studies. [16][17][18][19] In recent works, the use of the linear quadratic regulator (LQR)-based neuro-fuzzy model has been suggested to improve the performance of the controller. 20 In this article, a novel radial basis neuro-fuzzy linear quadratic regulator (RBNFLQR) controller is developed for an anti-swing control of a double pendulum system.…”

An energy-time optimal autonomous motion control framework for overhead cranes in the presence of obstacles

“…The main drawback of these open-loop techniques is that they cannot cope with various external disturbances that exist in the actual operational environment. Hence, closed-loop control techniques such as proportional integral derivative (PID) and its invariants, 14–16 model predictive control (MPC), 17–19 sliding mode control (SMC), 20–22 adaptive control, 23–25 intelligent control 14,26,27 and instantaneous optimal control, 28 are more practical. Input saturation, as well as constraints on trolley velocity and sway angles for safety consideration, 9,19,29 are well-considered in the above controllers.…”