Special Issue on New Trends in Theory and Applications of Complex Control Systems

Complexity is one of the leading features of modem control systems. It is caused by the complex properties of controlled plants and the varied requirements in controller designs. In dealing with the control problems with complexity, in the past two decades, a lot of papers have been published that reported fruitful results on different theoretical backgrounds and with different methodologies, such as differential geometry-based design methods, hybrid system theory, switching control, neural network-based intelligent control, etc.

Low-Cost Embedded Controller for Complex Control Systems

Because of limited resource of embedded platforms, the computational complexity of advanced control algorithms raises significant challenges for the use of embedded systems in complex control field. A Scilab/Scicos based embedded controller is developed on which various control software can be easily modeled, simulated, implemented, and evaluated to meet the ever-expanding requirements of industrial control applications. Built on the Cirrus Logic EP9315 ARM systems-on-chip board, this embedded controller is possible to develop complex embedded control systems that employ advanced control strategies in a rapid and cost-efficient fashion. Due to the free and open source nature of the software packages used, the cost of the embedded controller is minimized.

The Similar Structures and Control Problems of Complex Systems

In this paper, the naturally evolving complex systems, such as biotic and social ones, are considered. Focusing on their structures, a feature is noteworthy, i.e., the similarity in structures. The relations between the functions and behaviors of these systems and their similar structures will be studied. Owing to the management of social systems and the course of evolution of biotic systems may be regarded as control processes, the researches will be within the scope of control problems. Moreover, since it is difficult to model for biotic and social systems, it will start with the control problems of complex systems, possessing similar structures, in engineering. The obtained results show that for either linear or nonlinear systems and for a lot of control problems similar structures lead to a series of simplifications. In general, the original system may be decomposed into reduced amount of subsystems with lower dimensions and simpler structures. By virtue of such subsystems, the control problems of original system can be solved more simply. At last, it turns round to observe the biotic and social systems and some analyses are given.

Structure identification of an uncertain network coupled with complex-variable chaotic systems via adaptive impulsive control

In this paper, structure identification of an uncertain network coupled with complex-variable chaotic systems is in- vestigated. Both the topological structure and the system parameters can be unknown and need to be identified. Based on impulsive stability theory and the Lyapunov function method, an impulsive control scheme combined with an adaptive strategy is adopted to design effective and universal network estimators. The restriction on the impulsive interval is relaxed by adopting an adaptive strategy. Further, the proposed method can monitor the online switching topology effectively. Several numerical simulations are provided to illustrate the effectiveness of the theoretical results.

A Novel Evolutionary-Fuzzy Control Algorithm for Complex Systems

This paper presents an adaptive fuzzy control scheme based on modified genetic algorithm. In the control scheme, genetic algorithm is used to optimze the nonlinear quantization functions of the controller and some key parameters of the adaptive control algorithm. Simulation results show that this control scheme has satisfactory performance in MIMO systems, chaotic systems and delay systems.

Safety control program for complex system based on behavior science

To control complex system's safety effectively,safety control program was supported based on the principles of behavioral science that shapes organizational be- havior,and organizational behavior produced individual behavior.The program can be structured into a model that consists of three modules including individual behavior rectifi- cation,organization behavior diagnosis and model of safety culture.The research result not only reveals the deep cause of complex system accidents but also provides structural descriptions with the accidents cause.

Control parameter optimal tuning method based on annealing-genetic algorithm for complex electromechanical system

A new searching algorithm named the annealing-genetic algorithm(AGA) was proposed by skillfully merging GA with SAA. It draws on merits of both GA and SAA ,and offsets their shortcomings.The difference from GA is that AGA takes objective function as adaptability function directly,so it cuts down some unnecessary time expense because of float-point calculation of function conversion.The difference from SAA is that AGA need not execute a very long Markov chain iteration at each point of temperature, so it speeds up the convergence of solution and makes no assumption on the search space,so it is simple and easy to be implemented.It can be applied to a wide class of problems.The optimizing principle and the implementing steps of AGA were expounded. The example of the parameter optimization of a typical complex electromechanical system named temper mill shows that AGA is effective and superior to the conventional GA and SAA.The control system of temper mill optimized by AGA has the optimal performance in the adjustable ranges of its parameters.

Lyapunov-Based Dynamic Neural Network for Adaptive Control of Complex Systems

In this paper, an adaptive neuro-control structure for complex dynamic system is proposed. A recurrent Neural Network is trained-off-line to learn the inverse dynamics of the system from the observation of the input-output data. The direct adaptive approach is performed after the training process is achieved. A lyapunov-Base training algorithm is proposed and used to adjust on-line the network weights so that the neural model output follows the desired one. The simulation results obtained verify the effectiveness of the proposed control method.

Decentralized Variable Structure Control of Complex Giant Singular Uncertainty Systems

This paper, at the first time, considers the problem of decentralized variable structure control of complex giant singular uncertainty systems by using the property of diagonally dominant matrix and Frobenius-Person theorem. The splendid selection of switching manifold for each subsystem makes the design relatively straightforward and can be easily realized. An illustrate example is given.

Kolmogorov-Chaitin Complexity of Digital Controller Implementations

The complexity of linear, fixed-point arithmetic digital controllers is investigated from a Kolmogorov-Chaitin perspective. Based on the idea of Kolmogorov-Chaitin complexity, practical measures of complexity are developed for statespace realizations, parallel and cascade realizations, and for a newly proposed generalized implicit state-space realization. The complexity of solutions to a restricted complexity controller benchmark problem is investigated using this measure. The results show that from a Kolmogorov-Chaitin viewpoint, higher-order controllers with a shorter word-length may have lower complexity and better performance, than lower-order controllers with longer word-length.

Fairness in Olympic sports:How can we control the increasing complexity of doping use in high performance sports?

With the Rio Olympics just a few months past,we remember the excitement,the incredible performances,and the controversies.As happens so often,the Olympic Games focus attention not only on athletic achievements but also on how athletes may reach ever-increasing levels of performance.Doping,medical

Computation Method for the Real-Time Control Problems in Differential-Algebraic Systems

In this paper, a real-time computation method for the control problems in differential-algebraic systems is presented. The errors of the method are estimated, and the relation between the sampling stepsize and the controlled errors is analyzed. The stability analysis is done for a model problem, and the stability region is ploted which gives the range of the sampling stepsizes with which the stability of control process is guaranteed.

Controlled unknown quantum operations on hybrid systems

Any unknown unitary operations conditioned on a control system can be deterministically performed if ancillary subspaces are available for the target systems [Zhou X Q, et al. 2011 Nat. Commun. 2 413]. In this paper, we show that previous optical schemes may be extended to general hybrid systems if unknown operations are provided by optical instruments. Moreover, a probabilistic scheme is proposed when the unknown operation may be performed on the subspaces of ancillary high-dimensional systems. Furthermore, the unknown operations conditioned on the multi-control system may be reduced to the case with a control system using additional linear circuit complexity. The new schemes may be more flexible for different systems or hybrid systems.

Optimal Multiperiodic Control for Inventory Coupled Systems: A Multifrequency Second-Order Test

A complex autonomous inventory coupled system is considered. It can take, for example, the form of a network of chemical or biochemical reactors, where the inventory interactions perform the recycling of by-products between the subsystems. Because of the flexible subsystems interactions, each of them can be operated with their own periods utilizing advantageously their dynamic properties. A multifrequency second-order test generalizing the p-test for single systems is described. It can be used to decide which kind of the operation (the static one, the periodic one or the multiperiodic one) will intensify the productivity of a complex system. An illustrative example of the multiperiodic optimization of a complex chemical production system is presented.

Pinning control of complex Lur’e networks

This paper addresses the control problem of a class of complex dynamical networks with each node being a Lur＇e system whose nonlinearity satisfies a sector condition, by applying local feedback injections to a small fraction of the nodes. The pinning control problem is reformulated in the framework of the absolute stability theory. It is shown that the global stability of the controlled network can be reduced to the test of a set of linear matrix inequalities, which in turn guarantee the absolute stability of the corresponding Lur＇e systems whose dimensions are the same as that of a single node. A circle-type criterion in the frequency domain is further presented for checking the stability of the controlled network graphically. Finally, a network of Chua＇s oscillators is provided as a simulation example to illustrate the effectiveness of the theoretical results.

基于硬注意力机制下的鱼群涌现自动建模方法

生物集群的协同智能可用于启发人工复杂系统调控,但是现有的自动建模方法往往不符合生物集群信息的处理特点,导致单体的信息交互建模仍充满挑战。不失一般性,借助红鼻剪刀鱼的集群运动数据设计符合生物硬注意力机制的深度网络模型,该结构能强制单体考虑至多两个以内的邻居信息,并能显现出高影响力邻居经常出没的隐藏位置,说明硬注意力模型符合生物集群的信息处理机制。实验结果表明:所提硬注意力模型具有较为良好的稀疏信息解耦能力、较为鲁棒的集群运动指标以及较为优秀的集群规模泛化性能,为复杂系统的多层次行为分析提供了有力的工具支撑,该方法对集群机器人的分布式控制具有较强的启发意义。

Comparison between pinning control of different chaotic complex dynamical networks

The stabilization properties of various typical complex dynamical networks composed of chaotic nodes are theoretically investigated and numerically simulated in detail. Some local stability properties of such pinned networks are derived and the valid stability regions are estimated based on eigenvalue analysis. Numerical simulations of such networks are given to explain why significantly less local controllers are needed by the specifically pinning scheme, which pins the most highly connected nodes in scale-free networks, than that required by the randomly pinning scheme. Also, it is explained why there is no significant difference between the two schemes for controlling random-graph networks and small-world networks.

基于Vogit力学模型的照明系统节能控制研究

采用Vogit力学模型,构建照明系统离散动力学方程,分析照明系统状态空间变化特征,估计照明系统负载;引入电流灵敏度参量,结合照明系统电磁耦合特性,构建照明系统节能控制模型;通过自适应迭代处理实现自适应寻优,获取照明系统内环控制输出增益;根据照明系统节能控制带恒功率负载等效原理消除复阻抗,达到节能控制稳定的目的。以照明系统作为实验对象进行实验,结果表明该方法的节能控制效果较好,能够有效确保照明系统节能控制的稳定性。

基于强化学习的柴油机调速算法研究

为了更好地调节柴油机转速,提出一种强化学习–比例积分微分(proportional integral derivative, PID)控制器,并应用到了柴油机转速控制中。基于连续动作空间的柔性动作–评价(soft actor-critic, SAC)算法,结合连续型PID控制器,设计了一种强化学习–PID控制器,可代替传统PID控制的转速环。优化设计了基于演员–评论家(actor-critic)框架的输入输出和奖励函数以匹配柴油机特性,采用随机动作增加寻优效率,形成SAC-PID控制柴油机转速的网络交互结构,达到快速调整转速,减小稳定时间的效果。构建了以柴油机D6114为原型机的MATLAB/Simulink平均值模型,并利用试验数据验证了仿真模型的有效性。利用平均值模型,仿真验证了该控制算法效果。经过仿真验证本算法使柴油机转速响应曲线超调量更小,响应时间更快,鲁棒性更强,SAC-PID控制负载瞬态调速率和稳定时间均已达到1级精度指标。仿真对比验证了SAC算法的联合控制效果,结果表明其较其他算法更佳。

贝叶斯推断视角的系统调控内涵与策略研究

系统调控思想是融合一般系统理论及复杂系统自组织等理论,倡导通过建构与演化结合,对系统整体进行调控与管理,从而达至系统目标及实现系统和谐有序的一种理论。系统调控思想在认知及推断角度与贝叶斯推断有异曲同工之妙。贝叶斯推断的认识论意蕴及演绎与归纳确证逻辑从逻辑角度分析并论证了系统调控思想中建构及演化策略结合的必要性,为系统调控思想提供了理论基础。基于贝叶斯推断视角,文中给出增强先验知识、加强过程学习等5类调控策略。