Process knowledge-based random forest regression for model predictive control on a nonlinear production process with multiple working conditions

In process industry, predictive control approaches have been widely used for nonlinear production processes. Practically, the predictor in a predictive controller is extremely important since it provides future states for the optimization problem of controllers. The conventional predictive controller with precise mathematical predictors approximating the state space of physical systems is difficult and time-consuming for nonlinear production processes, and it performs poorly over a wide range of working conditions and with significant disturbances. To address the challenges, the trend of applying artificial intelligence emerges. However, the industrial process-specific knowledge is ignored in most cases. In this study, a predictive controller with a control process knowledge-based random forest (RF) model is proposed. Specifically, working data are clustered at first to handle diverse working conditions. Then, a process knowledge-based forest predictor, namely MIW-RF model with a redesigned cascading RF structure, is proposed to incorporate control process knowledge into modeling. Thus, future states of controlled variables could be more accurately acquired for the optimizer. A simplified version of the predictive model is also developed with quick model training and updating. The proposed predictive methods are finally introduced into the controller design. According to the empirical results, the proposed methods deliver a better control performance against benchmarks, including more accurate anticipated controlled-variable responses, better set-point tracking and disturbance rejection capability.     

Double-layer fuzzy adaptive NMPC coordinated control method of energy management and trajectory tracking for hybrid electric fixed wing UAVs

The energy management and trajectory tracking control are crucial to realize long-endurance autonomous flight for hybrid electric UAVs. This study aims to comprehensively consider energy management and trajectory tracking for hybrid electric fixed wing UAVs with photovoltaic panel/fuel cell/battery. A double-layer fuzzy adaptive nonlinear model predictive control method (DFNMPC) is proposed. Separated by the surplus demand power, energy management and trajectory tracking problem are decoupled into the high-layer fuzzy adaptive nonlinear model predictive controll problem (H-FNMPC) and low-layer fuzzy adaptive nonlinear model predictive controll problem (L-FNMPC). H-FNMPC solves the trajectory tracking and navigation control probelm for the greatest benefit of solar energy. L-FNMPC solves the power allocation problem of hybrid energy system for minimum equivalent hydrogen consumption. A fuzzy adaptive prediction horizon adjustment method based on UAV maneuvering degree is proposed to effectively improve proposed method adaptability to different mission profiles. Analogously, a fuzzy adaptive equivalent hydrogen consumption factor adjustment method in L-FNMPC is proposed to ensure the flexible utilization of battery. In addition, an equivalent hydrogen flow rate calculation method based on the real-time current ratio is proposed for PV/FC/Battery hybrid energy system. Numerical simulation results including a spiral trajectory tracking and a quadrilateral trajectory tracking, demonstrate that DFNMPC can simultaneously handle energy management and trajectory tracking problem for hybrid electric UAVs. Compared to hierarchical fuzzy state machine strategy, DFNMPC can save 13.3% hydrogen for the spiral trajectory tracking, and 56.9% for the quadrilateral trajectory tracking. It indicates that the energy efficiency can be improved from both levels of energy management and flight motion. The proposed method prospected for exploring high-energy-efficiency autonomous flight of hybrid electric UAVs in the future.     

预应力在锚杆支护中的作用

在分析目前复杂困难条件下锚杆支护存在问题的基础上，论述锚杆支护系统刚度，特别是预应力对支护效果的重要性.采用有限差分数值计算软件分析了不同预应力下锚杆、锚索产生的应力场分布特征，以及钢带对锚杆预应力扩散的作用.提出锚杆主动支护系数、强度利用系数、预应力长度系数、有效压应力区、预应力扩散系数、有效压应力区骨架网状结构及临界支护刚度等概念.井下试验表明，大幅度提高锚杆预应力可显著减小巷道围岩变形，有效控制顶板离层；钢带在预应力支护系统中起非常重要的作用；锚杆预应力存在临界值，达到或超过临界值后锚杆支护可有效控制围岩变形与破坏.     

并联复合隔震支座性能的试验研究

利用拟静力试验对并联复合隔震支座的恢复力特性进行了研究。并联复合隔震支座由夹层橡胶垫和摩擦滑移支座并联复合组成。分别调整橡胶垫和摩擦滑移支座上竖向力的大小，对三种支座的恢复力特性进行了分析比较。结果表明，并联复合隔震支座充分融合了橡胶垫隔震支座和摩擦滑移隔震支座的优点，改善了采用单一隔震支座的缺点，大大提高了结构的抗震能力。     

金川不良岩体巷道支护技术可行性研究

金川不良岩体中巷道支护一直是困扰矿山生产的重大难题。由于矿区特殊的地质构造 ,形成很大的地质构造应力 ,岩体结构发育 ,巷道支护相当困难。借助于FLAC有限差分软件 ,进行模拟分析和方案优选 ,调整支护强度及刚度 ,限制围岩变形 ,以达到支护稳定的目的     

市场经济下金属露天矿边界品位的优化与控制

随着我国经济体制的转变，我国矿物产成品逐渐与国际矿物市场接轨，这导致矿物产成品价格随着市场供需关系的变化而波动。在此情况下，金属露天矿存在着如何有效控制开采边界品位，以争取最大经济效益问题。为此，建立了生产金属产品全过程即与采、选、冶相关的边界品位优化模型，讨论了不同条件下的边界品位控制策略。     

高强度高刚度强力锚固支护体系在深部高应力软岩巷道的应用研究

随着开采深度的不断增加,巷道围岩表现出的高应力软岩特征越来越明显。通过对深井高应力软岩岩石变形特征和机理的研究,提出了采用高阻、让压、强力锚杆锚索一次支护,取代目前新汶矿区普遍采用的多次让压支护的思路,并将该思路成功地应用于千米深井巷道支护中,取得了良好的支护效果。为我国深井软岩巷道支护提供了一条可行的途径。     

顶板岩梁结构的稳定性与支护系统刚度

借助结构稳定理论，分析了工作面顶板“三铰拱”结构、“砌体梁”结构的变形失稳机理，建立了顶板变形失稳的几何、载荷参数条件，提出了确定合理支架刚度的标准及计算公式。同时指出，顶板岩层流变会降低顶板结构承载能力，促使变形失稳发生。     

液压支架管料代用的可行性分析

用薄壁管料代替液压支架厚壁管料 ,通过计算验证材料的强度和刚度 ,并分析其安全系数和成本     

底层框架抗震墙砖房设计中若干问题的探讨

通过对底层框架抗震墙砖房结构设计中的受力变形特性、结构选型、抗震墙的布置、底层现浇楼板的厚度及配筋、第二层与底层的侧移刚度比、地震力增大系数、地震剪力在抗震墙和框架柱间的分配、底层框架梁上竖向荷载的取值等问题进行了探讨,供设计参考.