Fault-Tolerant Control of Double-Rope Winding Hoists Combining Neural-Based Adaptive Technique and Iterative Learning Method

Double-rope winding mine hoist (DRWMH) is a vital coal mining equipment whose performance directly impacts the safe and efficient exploitation of the deep earth resources. Unfortunately, the actuator faults and disturbances can greatly affect the effect of the DRWMH. This paper deals with the problem of tension coordination control against actuator faults and disturbances for a DRWMH driven by multiple permanent magnet synchronous motors (PMSMs) using the neural-based adaptive method and iterative learning scheme. To improve the control performance and to maintain the stability of the DRWMH under faulty conditions, in this approach, a novel neural-based adaptive control strategy is developed to implement the input control assignment of the actuators. An iterative learning controller is used to achieve the compensation of the faults and uncertainties. The stability of both control subsystems has been proven. A series of experimental results are illustrated to demonstrate the validity of the proposed fault tolerant control for the DRWMH, which also reveal that a superior control effect is achieved in contrast with traditional controllers. Furthermore, the strategy proposed in this paper can be applied to other mechanical systems driven by multiple PMSMs that require high safety.