Intelligent motion control for electromechanical servos using evolutionary learning and adaptation mechanisms
Intelligent motion control for electromechanical servos using evolutionary learning and adaptation mechanisms
复制标题
使用进化学习和适应机制的机电伺服系统智能运动控制
DOI：
10.1109/acc.2001.946326
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发表时间：
2001
期刊：
影响因子：
--
通讯作者：
M. Safonov
中科院分区：
文献类型：
--
作者：
S. Lyshevski;M. Safonov
机电伺服集成伺服电机，电源转换器，传感器，集成电路和控制器。在传统的应用中，广泛使用模拟和数字比例-积分-微分控制器，并且可以通过利用电磁和能量转换现象来直接控制电伺服马达。高性能伺服系统的设计必须在扩展的操作范围内达到指定的标准和标准。这些要求可以通过实施应用新颖控制方法设计的先进控制算法来保证。论文强调需要设计智能系统来解决智能运动控制问题。智能运动控制可以通过实现学习和自适应机制来实现，最终目标是实现最佳的整体性能。使用测量的控制，参考和输出向量，是系统性能变量的性能功能进行评估。它表明，使用进化学习和自适应，智能运动控制问题可以解决没有语言或机电伺服系统的数学模型。特别是，不可伪造的和溢价控制律的设计，实验结果有据可查。
Electromechanical servos integrate servomotors, power converters, sensors, integrated circuits, and controllers. In conventional applications, analog and digital proportional-integral-derivative controllers are widely used, and electric servomotors can be straightforwardly controlled by making use of the electromagnetic and energy conversion phenomena. High-performance servos must be designed to achieve specified criteria and standards in expanded operating envelopes. These requirements can be guaranteed implementing advanced control algorithms designed applying novel control methods. The paper stresses the need to design intelligent systems to solve the intelligent motion control problem. Intelligent motion control can be achieved by implementing learning and adaptation mechanisms with the ultimate objective being to attain the optimal overall performance. The performance functional is evaluated using measured control, reference, and output vectors that are the system performance variables. It is demonstrated that using the evolutionary learning and adaptation, the intelligent motion control problem can be solved without linguistic or mathematical models of electromechanical servos. In particular, unfalsified and premium control laws are designed, and experimental results are documented.