A confined space is an area in an industrial facility that has limited access and allows only restricted movement due to physical constraints. Confined spaces often require special safety precautions and may be subject to specific regulations to ensure the well-being of workers. Flexible manufacturing cells typically work in confined spaces in order to increase efficiency and decrease cost. The operation can be further complicated if uncertainty is involved. We propose an adaptive robust controller for uncertain mechanical systems in a confined space to enhance system performance while ensuring system safety. The design procedure consists of five phases: constraint-following formulation, fuzzy uncertainty prescription, diffeomorphism transformation, adaptive robust control design, and Nash game-based optimization. The effectiveness of the control scheme is demonstrated by numerical simulation experiments for a humanoid robot arm.

A Stackelberg game-based design for an adaptive robust control for the fuzzy uncertain underactuated mechanical systems (UMSs) is proposed. The emphasis is on fuzzy-based uncertainty and inequality constraint. The uncertainty is time varying and bounded within a prescribed fuzzy set. For the inequality constraint, we creatively have it merge into constraint-following performance by a diffeomorphism technique. An adaptive robust control strategy is then proposed. Deterministic performance is guaranteed provided the control design parameters are within feasible regions. To further enhance the performance, we introduce a two-player Stackelberg game setting. The optimal choice of design parameters can be solved. The feasibility of this design is demonstrated on an autonomous wheeled mobile robot (AWMR), which is confined in a bounded space.

As an important component of the chassis system, the vehicle suspension plays a significant role in vehicle comfort, stability, and safety. In dynamic modeling of a semi-active suspension, traditional method is a complicated process, the actuator force is difficult to solve. In the process of model simplification, the influence of the vehicle-axle and balanced shift quality on the vehicle dynamics is not considered. For this reason, applying the Udwadia–Kalaba approach to dynamic modeling and control for a three-axle semi-active truck suspension can establish a simple and accurate equivalent discrete system. By fixing the displacement and acceleration of the body, the simulation is conducted with real excitations between the road and six wheels, and the actuator control forces of the semi-active suspension are obtained under the constraint by applying the Udwadia–Kalaba approach. The contrast in the analysis results from the Udwadia–Kalaba approach and the traditional Lagrange multiplier method indicate that the Udwadia–Kalaba equation can obtain the semi-active suspension actuator forces without introducing additional Lagrange multipliers and that the actuator forces acquired with the Udwadia–Kalaba approach are equal to those of the traditional Lagrange multiplier method.

Human–machine systems (HMSs) are dedicated to integrating intelligent human decisions with machine operations to achieve synergistic operational functionality. We focus on constraint-following control within the HMS, considering potential uncertainties, environmental disturbances, and limited operational space. A hierarchical hybrid control scheme is proposed, consisting of a preemption algorithm and a human decision algorithm. Specifically, the preemption algorithm relies on online state feedback from mechanical system signals, such as position and velocity, which can be implemented in hardware or software; the human decision algorithm takes inputs from electrophysiological signals or language commands. In this development, a Lagrangian density function is constructed that integrates optimal decision making with a uniformly bounded threshold. The intelligent decision-making problem in the HMS is creatively analyzed and mathematically solved leveraging variational calculus, resulting in the analytical expression of the optimal membership function associated with human decisions. Furthermore, a series of numerical simulation experiments are conducted using a bionic upper-limb prosthetic system as an example, and the comparison results demonstrate the superiority and effectiveness of the proposed method.