Editorial

摘要:

Nowadays control systems can find applications in many areas, like aerospace, motion tracking, chemical engineering, physics, biology, economics. To improve control performance is a perpetual goal. With recent progresses of computing technologies, better control performance can be achieved by more judicious control strategies based on more precise and more complicated, such as time-varying, nonlinear, models. Another trend to improve control performance is built upon the divide-and-conquer philosophy, i.e., a complicated control task is cooperatively accomplished by multiple controllers/agents, instead of a single super-powerful controller. This trend is made possible due to great advances in communication, which enable the information exchange among agents and may unite the less powerful agents. The cost for such control performance improvement is higher spatial complexity of control systems. Although high model complexity and spatial complexity can be physically handled by current powerful controllers/agents, the lack of efficient analysis and synthesis methods prevents the performance improvement of control systems with high model or/and spatial complexity. As we know, conventional control methods are mainly developed under the assumptions of linear, time-invariant and centralized models. In order to resolve the issues due to the aforementioned two types of complexities, new methods are expected, which is exactly the major aim of the present special issue.

Abstract:

Nowadays control systems can find applications in many areas, like aerospace, motion tracking, chemical engineering, physics, biology, economics. To improve control performance is a perpetual goal. With recent progresses of computing technologies, better control performance can be achieved by more judicious control strategies based on more precise and more complicated, such as time-varying, nonlinear, models. Another trend to improve control performance is built upon the divide-and-conquer philosophy, i.e., a complicated control task is cooperatively accomplished by multiple controllers/agents, instead of a single super-powerful controller. This trend is made possible due to great advances in communication, which enable the information exchange among agents and may unite the less powerful agents. The cost for such control performance improvement is higher spatial complexity of control systems. Although high model complexity and spatial complexity can be physically handled by current powerful controllers/agents, the lack of efficient analysis and synthesis methods prevents the performance improvement of control systems with high model or/and spatial complexity. As we know, conventional control methods are mainly developed under the assumptions of linear, time-invariant and centralized models. In order to resolve the issues due to the aforementioned two types of complexities, new methods are expected, which is exactly the major aim of the present special issue.