在煤矿巷道掘进过程中，传统“空间重合、时间分离”的掘进作业模式导致了“采掘失衡”问题，长期制约煤炭的高效与安全开采。鉴于此，本项目团队研发的护盾式智能掘进机器人系统通过“空间分离、时间同步”的掘锚协同作业模式，实现了掘进与支护的并行化高效作业。作为系统核心单元的临时支护环节采用欠约束并联机构，然而该机构存在正运动学求解难和运动控制难两大关键难题，因此本文开展欠约束临时支护机器人几何静力耦合模型与控制研究，旨在突破欠约束临时支护机器人的运动学求解瓶颈与复杂工况下的控制鲁棒性难题。本文的主要研究工作如下：

(1) 欠约束临时支护机器人几何静力耦合模型研究。首先，基于矢量封闭原理构建其运动学模型，并结合静力平衡方程，提出几何静力耦合模型，破解欠约束临时支护机器人正运动学求解难题。其次，为克服该耦合模型高维非线性求解问题，本文改进了蜣螂优化算法，通过引入折射反向学习、融合正余弦和自适应t分布扰动优化了算法的全局搜索能力和收敛性能，并通过六个标准测试函数对改进蜣螂优化算法的性能进行了验证。最后，运用改进的蜣螂优化算法对欠约束临时支护机器人几何静力耦合模型进行了求解，为欠约束临时支护机器人的实际应用提供了充分的理论支持。

(2) 欠约束临时支护机器人自由空间轨迹跟踪控制研究。首先，针对欠约束临时支护机器人动力学模型参数的不确定性，提出基于RBF神经网络分块逼近的终端滑模控制方法，通过构建基于自适应律的神经网络权值在线调整机制，实现末端支护平台动力学模型的动态重构，并设计鲁棒补偿项消除模型重构误差与外部扰动影响。其次，针对传统滑模控制抖振问题，引入模糊系统自适应逼近切换增益取代传统鲁棒项，从而实现控制量的平滑输出，并基于Lyapunov稳定性理论证明了闭环系统的稳定性。最后，对所提出的基于RBF神经网络分块逼近的终端滑模控制器进行仿真分析，结果表明：该控制器显著提升了欠约束临时支护机器人的轨迹跟踪精度，增强了临时支护机器人面对外界干扰时的鲁棒性，为自由空间复杂轨迹下临时支护机器人高精度控制提供了理论支撑。

(3) 欠约束临时支护机器人约束空间接触阻抗控制研究。首先，构建了欠约束临时支护机器人力外环-位置内环的双闭环控制模型，推导稳态误差方程并揭示关键参数对控制性能的影响机理。其次，针对传统阻抗控制在环境位置和刚度变化时的性能局限，提出了一种欠约束临时支护机器人的模糊自适应阻抗控制方法，利用接触力误差及其变化率动态调节阻尼与刚度系数。最后，对所提出的模糊自适应阻抗控制方法进行了多场景仿真分析（期望力调整、环境刚度变化、环境位置偏移及多自由度验证）。结果表明：相较于传统方法，所提模糊控制策略显著提升了欠约束临时支护机器人的动态响应速度，有效抑制了稳态误差，并在环境参数波动条件下表现出更强的鲁棒性与适应性，为复杂工况下临时支护机器人阻抗控制提供了理论支持。

(4) 欠约束临时支护机器人虚拟样机仿真。首先，基于SolidWorks建立了欠约束临时支护机器人三维简化模型，通过Adams搭建虚拟样机并设置仿真环境，结合点驱动仿真验证了机器人运动轨迹平滑性与广义力稳定性。其次，将Adams广义力数据导入Simulink进行交叉验证，结果表明数学模型仅存在x方向微小误差，验证了动力学模型的有效性。最后，构建Adams-Simulink联合仿真平台，开展系统级分析，结果表明：轨迹跟踪实验显示控制器在三维空间及姿态控制中具有高精度与强鲁棒性；阻抗控制实验在单自由度恒力场景实现力/位精准跟踪，但变力工况下接触力跟踪存在超调与稳态偏差。研究通过多层次仿真验证了欠约束临时支护机器人动力学模型及控制系统的可靠性，同时为复杂动态场景下的算法优化提供了实验依据。

During the process of coal mine roadway tunneling, the traditional operation mode of “spatial coincidence and time separation” has led to the problem of “imbalance between coal mining and excavation”, which has long restricted the efficient and safe mining of coal. In response to this situation, our team has developed a shield-type intelligent tunneling robot system. Through the collaborative operation mode of “spatial separation and time synchronization”, this system can achieve parallel and efficient operations of roadway tunneling and support. As the core unit of the proposed system, the temporary support of the roadway adopts an under-constrained parallel mechanism. However, this mechanism has two key difficulties: it is difficult to solve the forward kinematics and it is difficult to control the motion of the terminal support platform. Therefore, this paper focuses on the geometric-static coupling model and control of the under-constrained temporary support robot, aiming to break through the bottleneck of the kinematics and the robustness of the control of the robot under complex working conditions. The main research work of this paper is as follows:

(1) Research on the geometric-static coupling model of under-constrained temporary support robots. First, based on the vector closure principle, its kinematic model is constructed. By combining the kinematic model with the static equilibrium equations, a geometric-static coupling model is proposed, solving the challenging forward kinematics problem of the under-constrained temporary support robot. Secondly, to address the high-dimensional nonlinear solving issues of the coupling model, this paper improves the Dung Beetle Optimizer (DBO) algorithm. By introducing refractive opposition-based learning, incorporating sine-cosine strategy, and applying adaptive t-distribution perturbation, the global search capability and convergence performance of the algorithm are optimized. The effectiveness of the improved DBO algorithm is validated through six benchmark test functions. Finally, the improved DBO is employed to solve the geometric-static coupling model of the under-constrained robot, providing substantial theoretical support for its practical application.

(2) Research on free-space trajectory tracking control of under-constrained temporary support robots. First, aiming at the parameter uncertainty of the dynamic model for under-constrained temporary support robots, a terminal sliding mode control method based on RBF neural network block approximation is proposed. By constructing an online adjustment mechanism for neural network weights based on adaptive law, dynamic reconstruction of the end support platform's dynamic model is realized. A robust compensation term is designed to eliminate the influence of model reconstruction errors and external disturbances. Second, addressing the chattering problem in traditional sliding mode control, a fuzzy system is introduced to adaptively approximate the switching gain, replacing the traditional robust term to achieve smooth output of control quantities. The stability of the closed-loop system is proven based on Lyapunov stability theory. Finally, simulation analysis is conducted on the proposed terminal sliding mode controller with RBF neural network block approximation. The results show that the controller significantly improves the trajectory tracking accuracy of under-constrained temporary support robots and enhances their robustness against external disturbances, providing theoretical support for high-precision control of temporary support robots under complex free-space trajectories.

(3) Research on contact impedance control of under-constrained temporary support robots in constrained space. First, a dual closed-loop control model of force outer loop - position inner loop for the under-constrained temporary support robot is constructed, and furthermore, the steady-state error equation is derived. Meanwhile the influence mechanism of the key parameters on the control performance is revealed. Secondly, aiming at the performance limitations of traditional impedance control when the environmental position and stiffness change, a fuzzy adaptive impedance control method for the under-constrained temporary support robot is proposed, which can use the contact force error and its rate of change to dynamically adjust the damping and stiffness coefficients. Finally, the proposed fuzzy adaptive impedance control method for the robot is simulated in multiple scenarios (expected force adjustment, environmental stiffness change, environmental position offset and multi-degree of freedom verification), and the results show that compared with traditional methods the proposed fuzzy control strategy significantly improves the dynamic response speed of the under-constrained temporary support robot and effectively suppresses the steady-state error, while it shows stronger robustness and adaptability under fluctuating environmental parameters. The obtained results provide theoretical support for impedance control of the temporary support robot under complex working conditions.

(4) Virtual prototype simulation of the under-constrained temporary support robot. First, a three-dimensional simplified model of the under-constrained temporary support robot is established based on SolidWorks. Moreover, a virtual prototype of the robot is built through Adams and the simulation environment is set up. Then, the smoothness of the robot’s motion trajectory and the stability of the generalized force are verified with simulations. Secondly, the general force data is imported into Simulink for cross-validation, and the results show that there is only a small error in the x-direction of the mathematical model. This verifies the effectiveness of the dynamic model for the robot. Finally, an Adams-Simulink joint simulation platform is constructed to carry out system-level analysis. The obtained results show that the controller has high precision and strong robustness in three-dimensional space and attitude control; the impedance control experiment achieves accurate force/position tracking in the single degree constant force scenario, but there are overshoots and steady-state deviations in the variable force working condition. The research has verified the reliability of the dynamic model and control system of the under-constrained temporary support robot through multi-level simulation, and at the same time provided an experimental basis for the optimization of the algorithm under complex dynamic scenarios.

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