Motion simulators have been of significant importance for the aviation sector in training pilots. However, the present boom in the utilization of robotics for virtual reality (VR) gaming has given rise to a new application of motion simulators. Motion cueing algorithms (MCA) play a key role in mapping the motions from a gaming scenario to the workspace of a simulator. This workspace is small (as compared to the gaming world), and on reaching the boundary, it becomes necessary to saturate the motion. Each degree of freedom, in the Cartesian space, is saturated between two fixed extremities. This hampers the perception of motion of a user enjoying the scenario. In order to address this practical problem, we make an attempt to enlarge the workspace and develop a mathematical methodology to prevent the simulator from exiting a non-cuboidal workspace. To do so, we propose sliding mode-based cueing algorithm (SMCA), which makes the simulator to slide in close proximity across the boundary of workspace. We make use of discrete-time models to present this methodology in order to ensure straightforward implementation by researchers in the future. Veracity of SMCA is testified by means of experimentation on SP7 motion simulator. The experimental results give evidence of a 57% increase in the considered sub-workspace, thereby reducing the relative necessity to saturate the motions as compared to classical MCA. This leads to a better experience of a user enjoying the VR scenario. On the other hand, the following drawbacks are reported: (1) necessity to analytically model the workspace boundary and ensuring that it is smooth with nonzero gradient, (2) SMCA parameter selection is more cumbersome than classical MCA, thereby making its utility restricted to recorded scenarios.

运动模拟器对于航空领域的飞行员培训非常重要。但是，目前在虚拟现实（VR）游戏中使用机器人技术的热潮引起了运动模拟器的新应用。动作提示算法（MCA）在将动作从游戏场景映射到模拟器的工作空间中起着关键作用。这个工作区很小（与游戏世界相比），并且到达边界时，有必​​要使运动饱和。在笛卡尔空间中，每个自由度在两个固定的末端之间是饱和的。这妨碍了享受场景的用户的运动感。为了解决这个实际问题，我们尝试扩大工作空间并开发一种数学方法，以防止模拟器退出非立方体的工作空间。为此，我们提出了基于滑动模式的提示算法（SMCA），该算法可使模拟器在工作空间的边界内紧密滑动。我们使用离散时间模型来介绍此方法，以确保将来研究人员可以直接实施。通过在SP7运动模拟器上进行实验，证明了SMCA的准确性。实验结果表明，与传统MCA相比，考虑的子工作空间增加了57％，从而降低了使运动饱和的相对必要性。这导致用户享受VR场景的更好体验。另一方面，报告了以下缺点：（1）必须对工作空间边界进行分析建模，并确保其具有非零梯度的平滑度；（2）SMCA参数选择比传统的MCA更为麻烦，因此其效用仅限于记录的场景。