Intelligent systems engineering theory for complex dynamical systems with composite disturbances/uncertainties

Systems engineering, which can be traced back to the 1940s and has achieved great success, is an integrative methodology that focuses on the holistic optimization of a system throughout its life cycle. However, most of the resulting engineering plants are confined to “ideal environments”, “preset tasks”, and “deterministic modes”. In this paper, on the basis of dynamic closed-loop uncertainty quantification, we propose the fundamental research framework of the intelligent systems engineering (ISE) theory for complex dynamical systems with composite disturbances and uncertainties. The ISE theory covers the overall stages of modeling and quantitative analysis, intelligent perception and control, and testing and evolutionary design, which can be regarded as an intelligent version of systems engineering for complex unmanned systems and equipment. Basic concepts and methodologies of the ISE theory are presented, together with some aerospace and robotic applications. The goal of the ISE is to recognize, resist, and govern disturbances and uncertainties by “learning while operating” and “lifelong learning”, to endow systems with safety, green, and evolution characteristics, and finally to adapt to complex environments, time-varying missions, and flexible modes.