Event-Triggered Safety-Critical Model Predictive Control for Underactuated Overhead Cranes

In overhead cranes, the inclination angle of the payload must be limited within an acceptable range to ensure safety, and the trolley reaches a desired position simultaneously. However, the payload can be disturbed by strong winds, which poses a certain threat to the safety of overhead crane operation. In addition, when multiple overhead cranes communicate with each other via a shared network, the communication and computational resources available to each overhead crane become constrained. Taking into account the above factors, a novel event-triggered safety-critical model predictive control (ESMPC) algorithm is proposed for underactuated overhead crane systems to achieve satisfactory performance. In the proposed ESMPC algorithm, disturbances acting on the payload are estimated by a discrete-time disturbance observer. Then, the discrete-time predictive control barrier function is devised to guarantee operational safety. Subsequently, the prediction model is derived and the quadratic programming (QP) problem is formulated. The optimal control sequence can be obtained by solving the QP problem at each event-triggering instant. After that, the predicted control inputs are fully exploited and applied to the trolley one by one chronologically. Finally, the experimental results show that the payload swing angle can be limited within the safe range and the trolley can reach the desired position by using fewer communication and computing resources under the proposed ESMPC method.