An enhanced coupling nonlinear control method for bridge cranes

Abstract

The present study suggests an enhanced coupling non-linear control approach for both cart positioning and load swing elimination control of underactuated bridge cranes. The significant feature of the proposed method is its superior control performance as well as its strong robustness over different/uncertain cable lengths that are the major factor influencing load swing. Owing to the underactuated nature, the load swing can merely be damped out by the cart motion. Inspired by this inherent mechanism and also motivated by the desire to achieve an improved control performance, we enhance the coupling behaviour between the cart movement and the load swing by introducing a new composite signal, and on this basis, propose a non-linear control law. Lyapunov-based mathematical analysis is implemented to support the theoretical derivations. To verify its control performance, both numerical simulation and hardware experiment results are included to illustrate that the proposed method can achieve superior control results and admit strong robustness with respect to different/uncertain cable lengths and external disturbances.

Finite-Time Trajectory Tracking Control for Overhead Crane Systems Subject to Unknown Disturbances

“…Under Assumption 2, e (T 0 ) = 0 can be obtained. Then, following the Lyapunov theory and the LaSalle extension [53], all the states as t ≥ T 0 are confined in D, which also proves the uniformly ultimately boundedness of e. Theorem 1: Consider the observer error dynamics in (11) and (12) obtained from the linear system 7and (8) and the terminal sliding mode observer in (9) and (10). Choose the observer gains l 1 , l 2 , l 3 , l 4 , and l 5 such that…”

“…The simulation study is divided into two scenarios. More precisely, in the first scenario, the performance of the proposed controller is validated by comparing it with the LQR controller [8], the enhanced coupling (EC) controller [12], and the PD controller [31]. It should be pointed out that the LQR controller, EC controller, and PD controller are designed without considering the unknown disturbances.…”

“…An underactuated overhead crane system shown in Figure 1 is considered, whose dynamics are described as follows [4], [12], [19], [22]:…”

Takagi‐Sugeno Fuzzy Modeling and PSO‐Based Robust LQR Anti‐Swing Control for Overhead Crane

“…In order to reduce the complexity of controller design or stability analysis, these control methods first linearize the complex nonlinear model near the equilibrium point or ignore some specific nonlinear coupling terms. The other is the control method based on nonlinear model, such as nonlinear coupled control method [11], sliding model control [12,13], robust control [14], model predictive control [15][16][17], nonlinear control method for complicated operation duties [18], and so on. Most of these control methods do not consider the uncertainty of the system, and the robustness is relatively poor.…”

Modeling and energy-based fuzzy controlling for underactuated overhead cranes with load transferring, lowering, and persistent external disturbances

“…25 Open-loop control methods, including input shaping, [9][10][11][12] offline motion planning, 13 and optimal control, 14,15 have been proposed for overhead crane systems. Due to the presence of external disturbances, closed-loop control methods are developed to increase the system performance, including sliding-based control, [16][17][18][19] adaptive fuzzy control, [20][21][22] energy/passivity-based control (PBC), 8,[23][24][25][26][27] output feedback control, 28 nonlinear trajectory planning, 29 switching-based emergency control, 30 model predictive control, 31 nested saturation control, 32 and genetic algorithm (GA)-based stable control. 33 Nonetheless, the aforementioned control methods are oriented to constant-cable-length overhead crane systems, and their control performance will probably be degraded in the case of cable length variations.…”