multivariable system control

简明释义

多变量系统控制

英英释义

例句

1.The design of a new aircraft requires advanced multivariable system control 多变量系统控制 techniques to ensure stability during flight.

新型飞机的设计需要先进的多变量系统控制 multivariable system control 技术，以确保飞行中的稳定性。

2.The chemical process industry relies heavily on multivariable system control 多变量系统控制 to manage complex reactions.

化学过程工业在很大程度上依赖于多变量系统控制 multivariable system control 来管理复杂的反应。

3.Engineers are developing multivariable system control 多变量系统控制 algorithms to optimize energy consumption in smart grids.

工程师们正在开发多变量系统控制 multivariable system control 算法，以优化智能电网中的能源消耗。

4.In robotics, effective multivariable system control 多变量系统控制 is essential for coordinating multiple joint movements.

在机器人技术中，有效的多变量系统控制 multivariable system control 对于协调多个关节运动至关重要。

5.The simulation software includes features for multivariable system control 多变量系统控制 to analyze dynamic systems.

该仿真软件包括多变量系统控制 multivariable system control 的功能，以分析动态系统。

作文

In today's rapidly advancing technological landscape, the concept of multivariable system control has emerged as a crucial area of study and application. This term refers to the management and regulation of systems that have multiple input and output variables, which can significantly affect the overall performance and stability of the system. Understanding multivariable system control is essential for engineers and researchers who aim to design efficient systems in various fields such as aerospace, robotics, and chemical processing.To grasp the importance of multivariable system control, it is vital to recognize the complexity involved in managing systems with interdependent variables. Unlike single-variable systems, where the relationship between input and output is straightforward, multivariable systems require a more sophisticated approach. For instance, in an aircraft, factors like altitude, speed, and engine thrust are interconnected; adjusting one can impact the others. Therefore, achieving optimal performance necessitates a comprehensive understanding of how these variables interact with one another.One of the primary challenges in multivariable system control is the need for real-time data processing and decision-making. Engineers must develop algorithms that can analyze multiple data streams simultaneously and provide feedback to adjust the system accordingly. This requires advanced mathematical modeling and simulation techniques to predict how changes in one variable will affect the others. The use of control theory, particularly modern control strategies such as state-space representation and robust control, plays a significant role in addressing these challenges.Moreover, the applications of multivariable system control are vast and varied. In the field of robotics, for example, controlling a robotic arm involves managing several joints and motors, each with its own set of variables. Engineers employ multivariable system control techniques to ensure that the arm moves smoothly and accurately, performing tasks such as assembly or surgery with precision. Similarly, in chemical processing, maintaining the right temperature, pressure, and concentration levels simultaneously is critical for producing high-quality products. Here, multivariable system control ensures that all these parameters are optimized for efficiency and safety.Furthermore, as industries increasingly move towards automation and smart technologies, the significance of multivariable system control continues to grow. With the advent of the Internet of Things (IoT) and artificial intelligence (AI), systems are becoming more interconnected and complex. This evolution necessitates advanced control strategies that can handle the intricacies of multiple variables while ensuring system reliability and performance.In conclusion, multivariable system control is an essential concept in modern engineering that addresses the complexities of systems with multiple interacting variables. Its applications span across various industries, making it a critical area of research and development. As technology continues to advance, mastering multivariable system control will be increasingly important for engineers and researchers striving to create efficient, safe, and innovative systems that can meet the demands of the future.

在当今快速发展的技术环境中，多变量系统控制的概念已成为一个关键的研究和应用领域。这个术语指的是管理和调节具有多个输入和输出变量的系统，这些变量会显著影响系统的整体性能和稳定性。理解多变量系统控制对旨在设计高效系统的工程师和研究人员至关重要，这些系统广泛应用于航空航天、机器人和化工等领域。要理解多变量系统控制的重要性，必须认识到管理相互依赖变量的系统所涉及的复杂性。与单变量系统不同，单变量系统中输入和输出之间的关系是简单明了的，而多变量系统则需要更复杂的方法。例如，在飞机中，海拔、高速和发动机推力等因素是相互关联的；调整一个因素可能会影响其他因素。因此，实现最佳性能需要全面了解这些变量之间的相互作用。多变量系统控制中的主要挑战之一是实时数据处理和决策的需求。工程师必须开发能够同时分析多个数据流并提供反馈以调整系统的算法。这需要先进的数学建模和仿真技术，以预测一个变量的变化将如何影响其他变量。控制理论的使用，特别是现代控制策略如状态空间表示和鲁棒控制，在解决这些挑战中发挥了重要作用。此外，多变量系统控制的应用范围广泛而多样。在机器人技术领域，例如，控制机器人手臂涉及管理几个关节和电机，每个都有自己的一组变量。工程师使用多变量系统控制技术确保手臂平稳且准确地移动，执行如装配或手术等任务，达到精确度。同样，在化工处理中，同时维持适当的温度、压力和浓度水平对于生产高质量产品至关重要。在这里，多变量系统控制确保所有这些参数都得到优化，以实现效率和安全。此外，随着各行业越来越向自动化和智能技术发展，多变量系统控制的重要性也在不断增长。随着物联网（IoT）和人工智能（AI）的出现，系统变得更加互联和复杂。这一演变需要能够处理多个变量复杂性的高级控制策略，同时确保系统的可靠性和性能。总之，多变量系统控制是现代工程中的一个基本概念，解决了具有多个相互作用变量的系统的复杂性。它的应用跨越多个行业，使其成为一个关键的研究和开发领域。随着技术的不断进步，掌握多变量系统控制对于工程师和研究人员来说，将越来越重要，他们致力于创建高效、安全和创新的系统，以满足未来的需求。