Robust Adaptive Control of 3D Overhead Crane System

Abstract

In this chapter an adaptive anti-sway controller for uncertain overhead cranes is proposed. The system model including the system uncertainties and disturbances is introduced firstly. Next, the adaptive controller which can guarantee tracking the desired position of the trolley as well as the anti-sway of the load cable is established. In this chapter, the system is proven to be input-to-state stable (ISS) which is supported by Lyapunov technique. The proposed algorithm is verified by using Matlab/Simulink simulation tool. The simulation results shown that the presented controller gives the good performances (i.e., fast transient response, position tracking, and low swing angle) when there exist system parameters variation as well as input disturbances.

Vision-based Fuzzy Proportional–Integral–Derivative Tracking Control Scheme for Gantry Crane System

“…(4) To improve the performance of gantry crane systems, an appropriate control system design is usually applied. Many control methods have been successfully utilized in gantry crane control systems, such as proportional-integral-derivative (PID) control, (5) fuzzy control, (6) geneticalgorithm-based linear-quadratic regulator (LQR), (7) model reference adaptive control, (8) nonlinear feedback control, (9) passivity-based control, (10) robust adaptive control, (11) sliding mode control, (12,13) neural network PID control, (14) and vision-based control. (5,6,9,15,16) Because of the simple structure and the convenience of adjustment, PID control was the first practical controller to be developed and is still widely used.…”

Robust qLPV Tracking Fault-Tolerant Control of a 3 DOF Mechanical Crane

“…Due to this, it is essential to design control schemes that consider the under-actuation, a large number of linearities, possible faults, and robustness to the payload oscillations. In the literature, this problem has been approached by considering different methods, e.g., sliding modes [23], LQ controllers [24], particle swarm optimization [25], adaptive control [26,27], among others. Regarding safety systems, sliding mode differentiators can be consulted in [28] and fault-tolerant control in [29,30].…”