Optimization Strategies for Real-Time Control of an Autonomous Melting Probe
This work addresses the challenge of precise control for autonomous melting probes in glaciological applications, representing an incremental advancement in domain-specific control methods.
The authors tackled the problem of real-time trajectory planning and control for a maneuverable melting probe with many binary control variables by developing an optimization-based approach, which was tested on a glacier and used to improve the model through automated parameter identification.
We present an optimization-based approach for trajectory planning and control of a maneuverable melting probe with a high number of binary control variables. The dynamics of the system are modeled by a set of ordinary differential equations with a priori knowledge of system parameters of the melting process. The original planning problem is handled as an optimal control problem. Then, optimal control is used for reference trajectory planning as well as in an MPC-like algorithm. Finally, to determine binary control variables, a MINLP fitting approach is presented. The proposed strategy has recently been tested during experiments on the Langenferner glacier. The data obtained is used for model improvement by means of automated parameter identification.