Driving simulators are effective tools for training, virtual prototyping, and safety assessment which can minimize the cost and maximize road safety. Despite the aim of a realistic motion generation for the impression of real-world driving, motion simulators are bound in a limited workspace. Motion cueing algorithms (MCAs) aim to plan an acceptable motion feeling for drivers, without infringing the simulated boundaries. Recently, model predictive control (MPC) has been widely used in MCAs; however, the tuning process for finding the best weights of the MPC optimization is still a challenge. As there are several objectives for the optimization without any standard weighting for solution evaluations, a nonbiased scalarization of solutions for the purpose of comparison is impossible. In this paper, a clear method for obtaining the best MPC weighting has been proposed. This method searches for the best tune of MPC cost function weights, reduces the user burden for weight tuning while receiving feedback from the user satisfaction. The MPC-based MCA weights are optimized using a multiobjective genetic algorithm (GA) considering objectives, such as minimization of motion inputs (linear acceleration and angular velocity), input rates, output displacements and the sensed motion errors. Any process based on trial-and-error has been omitted. The adjusted weights have to satisfy a set of predefined conditions related to maximum tolerated error and maximum displacement. The obtained Pareto-front is used for decision making via an interactive GA (IGA), aiming for maximization of the decision maker's satisfaction. A Web interface is developed to interact with the IGA and to influence the region of searching. Simulation results show the superiority of the proposed method compared with the previous empirical tuning method. The sensed motion error is minimized using the proposed method and with the same available workspace, a more realistic motion can be rendered to the driver.

Nowadays, classical washout filters are extensively used in commercial motion simulators. Even though there are several advantages for classical washout filters, such as short processing time, simplicity and ease of adjustment, they have several shortcomings. The main disadvantage is the fixed scheme and parameters of the classical washout filter cause inflexibility of the structure and thus the resulting simulator fails to suit all circumstances. Moreover, it is a conservative approach and the platform cannot be fully exploited. The aim of this research is to present a fuzzy logic approach and take the human perception error into account in the classical motion cueing algorithm, in order to improve both the physical limits of restitution and realistic human sensations. The fuzzy compensator signal is applied to adjust the filtered signals on the longitudinal and rotational channels online, as well as the tilt coordination to minimize the vestibular sensation error below the human perception threshold. The results indicate that the proposed fuzzy logic controllers significantly minimize the drawbacks of having fixed parameters and conservativeness in the classical washout filter. In addition, the performance of motion cueing algorithm and human perception for most occasions is improved.

The human vestibular system is a sensory and equilibrium system that manages and controls the human sense of balance and movement. It is the main sensor humans use to perceive rotational and linear motions. Determining an accurate mathematical model of the human vestibular system is significant for research pertaining to motion perception, as the quality and effectiveness of the motion cueing algorithm (MCA) directly depends on the mathematical model used in its design. This paper describes the history and analyses the development process of mathematical semicircular canal models. The aim of this review is to determine the most consistent and reliable mathematical semicircular canal models that agree with experimental results and theoretical analyses, and offer reliable approximations for the semicircular canal functions based on the existing studies. Selecting and formulating accurate mathematical models of semicircular canals are essential for implementation into the MCA and for ensuring effective human motion perception modeling.

The hexapod manipulator is the most common motion platform, which is widely used as a simulation-based motion platform (SBMP). As the hexapod manipulator has a limited workspace, it is not physically possible to regenerate the real vehicle motion signals using the SBMP. The motion cueing algorithm (MCA) is responsible for regenerating a realistic vehicle motion sensation for the user when the SBMP operates within its physical and dynamical limitations. Recently, model predictive control (MPC) has been introduced to extract the optimal input motion signals while considering the SBMP limitations in the Cartesian coordinate space, which leads to a linear time-invariant (LTI) MPC-based MCA methods. Unfortunately, the existing LTI MPC-based MCA methods are still not able to consider the parameters of the SBMP's hexapod mechanisms inside their models. In general, the current studies only consider the constraints in the Cartesian coordinate system of the hexapod mechanism, instead of its design parameters. This consideration results in a poor usage of the hexapod workspace due to the conservative assumptions; consequently, the SBMP users do not experience realistic motions. The main contribution of this article is to take the SBMP's physical limitations into account in the MPC model such that more precise motion cues can be extracted for the users. A linear time-varying (LTV) MPC-based MCA method is designed for the first time in this article to consider the parameters of the hexapod mechanism in the MPC model. The proposed model (LTV MPC-based MCA) is validated using the MATLAB software, and the results depict better motion sensation with more accurate motion signals as compared with those from the existing LTI MPC-based MCA methods.