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This paper presents the validation and the comparative study of a shared control concept for a large vehicle manipulator (LVM). The state-of-the-art controlling a LVM is manual control: The operator controls the manipulator to carry out a specific task and keeps the vehicle on the road. Easing the work for the operator, an automatic lane-keeping of the vehicle can be taken into account: An automation of the vehicle which keeps it on its reference, but without taking into consideration of the manipulator's specific task. However, the operator has his specific task with the manipulator, and therefore, such automation may not be satisfying. Therefore, this paper presents the validation and compares the Limited Information Shared Controller (LISC) proposed previously with the manual control mode. This step is crucial, showing the concept's applicability and benefits compared to the state-of-the-art solution. Thus, the LISC is compared with a non-cooperative controller (NCC) and the manual mode on a real-time simulator with test subjects. It has a more realistic experimental setup than in other studies because there is no predefined manipulator reference. The study results indicate that the NCC can lead to undesired motions of the overall system because the test subjects cannot carry out their specific task. On the other hand, the proposed the LISC of the vehicle can reduce the working load while supporting the operator in carrying out the manipulator's specific task.

In this paper, we propose a new algorithm to solve the Inverse Stochastic Optimal Control (ISOC) problem of the linear-quadratic sensorimotor (LQS) control model. The LQS model represents the current state-of-the-art in describing goal-directed human movements. The ISOC problem aims at determining the cost function and noise scaling matrices of the LQS model from measurement data since both parameter types influence the statistical moments predicted by the model and are unknown in practice. We prove global convergence for our new algorithm and at a numerical example, validate the theoretical assumptions of our method. By comprehensive simulations, the influence of the tuning parameters of our algorithm on convergence behavior and computation time is analyzed. The new algorithm computes ISOC solutions nearly 33 times faster than the single previously existing ISOC algorithm.