Recent advances on micro-control for near-critical complex systems

摘要:

Near-critical systems are a class of complex systems that are “close” to criticality and possess potentials to be induced or manipulated to become critical [1,2]. Such systems demonstrate advantages in multi-disciplinary application scenarios. For example, when a biological predator-prey system is near critical, the escape probability of the prey is the highest and the whole system possesses a highly dynamical and flexible structure [3]. When a real-time computing network is near critical, it can perform complex computations on time series [4]. Actually, dynamical systems that are capable of fulfilling complex computational tasks often operate near the edge of chaos. To fully take advantage of near criticality, how to estimate and control of near-critical complex systems becomes a pressing issue. Classical control theory, whichmainly includes linear systems theory, sampled-data control theory, stochastic control theory, nonlinear systems theory and so on, concentrates on various topics such as stability of dynamical systems, time-domain and frequency-domain analysis, synthesis and compensation of controllers. However, most of them are inapplicable to the control of complex systems, mainly due to the following reasons. The classical control theory is model based, whereas in most case, we cannot obtain a mathematical model of a complex system, let alone an accurate one, since such a complex system is mostly featured with highly nonlinear dynamics, emergent properties, self-adaption and self-organization. For example, the classical frequency-domain methods, such as frequency response analysis and root locus methods, are inapplicable without a model. The control for complex systems is usually considered on a case by case basis and realized by inputting noise [5] and micro-control by constructing weak feedback loop [6] in some scenerios, as opposed to the model-based control theory for general linear or nonlinear dynamical systems....

Abstract:

Near-critical systems are a class of complex systems that are “close” to criticality and possess potentials to be induced or manipulated to become critical [1,2]. Such systems demonstrate advantages in multi-disciplinary application scenarios. For example, when a biological predator-prey system is near critical, the escape probability of the prey is the highest and the whole system possesses a highly dynamical and flexible structure [3]. When a real-time computing network is near critical, it can perform complex computations on time series [4]. Actually, dynamical systems that are capable of fulfilling complex computational tasks often operate near the edge of chaos. To fully take advantage of near criticality, how to estimate and control of near-critical complex systems becomes a pressing issue. Classical control theory, whichmainly includes linear systems theory, sampled-data control theory, stochastic control theory, nonlinear systems theory and so on, concentrates on various topics such as stability of dynamical systems, time-domain and frequency-domain analysis, synthesis and compensation of controllers. However, most of them are inapplicable to the control of complex systems, mainly due to the following reasons. The classical control theory is model based, whereas in most case, we cannot obtain a mathematical model of a complex system, let alone an accurate one, since such a complex system is mostly featured with highly nonlinear dynamics, emergent properties, self-adaption and self-organization. For example, the classical frequency-domain methods, such as frequency response analysis and root locus methods, are inapplicable without a model. The control for complex systems is usually considered on a case by case basis and realized by inputting noise [5] and micro-control by constructing weak feedback loop [6] in some scenerios, as opposed to the model-based control theory for general linear or nonlinear dynamical systems....