Understanding Agricultural Heritage Sites as Complex Adaptive Systems: The Challenge of Complexity

In rural life,everything is connected to everything else.Seen as a complex adaptive system,the ＂rural＂ in most regions of the world has evolved over many centuries and is well known to have endured invasive predations and conflicts and to have adapted to changing conditions,both physical and human,many times.Such changes are recorded in the culture and in the landscapes which have continuously evolved and which characterize rural places today.These features of contemporary rural life-economy,culture and landscape-are the key elements of rural systems.Interestingly,they have also become the elements that attract tourists to rural areas.This theoretical paper,starts from the position that the rural world as a whole is complex and that systems adjust in the face of uncertainty,and a type of dynamism that is generated externally in the form of shocks and stresses.Complex Adaptive Systems theory provides an excellent opportunity to examine living systems such as Globally Important Agricultural Heritage Systems （GIAHS） in China that can provide new perspectives on resilience and self-organizing capabilities of the system.The paper suggests that adopting such approaches in contemporary research will produce new insights of whole systems and stem the tide of mainstream scientific research that reduces systems to their component parts and studies them with micro-techniques,while mostly failing to reintegrate the component parts back into the system as a whole.By reviewing this approach in relation to GIAHS and by introducing tourism into the rural village system,as a perturbation,we can create new ways to understand the effects of rural development interventions in ancient landscapes such as those which cover many parts of rural China today.

STUDYING COMPLEX ADAPTIVE SYSTEMS

Complex adaptive systems （cas） - systems that involve many components that adapt or learn as they interact - are at the heart of important contemporary problems. The study of cas poses unique challenges： Some of our most powerful muthemutical tools, particularly methods involivng fixed points, attractors, and the like, are of limited help in understanding the development of cas. This paper suggests ways to modify research methods and tools, with an emphasis on the role of computer-based models, to increase our understanding of cas.

ADAPTIVE SYSTEMS THEORY: SOME BASIC CONCEPTS, METHODS AND RESULTS

The adaptive systems theory to be presented in this paper consists of two closely related parts: adaptive estimation (or filtering, prediction) and adaptive control of dynamical systems. Both adaptive estimation and control are nonlinear mappings of the on-line observed signals of dynamical systems, where the main features are the uncertain-ties in both the system's structure and external disturbances, and the non-stationarity and dependency of the system signals. Thus, a key difficulty in establishing a mathematical theory of adaptive systems lies in how to deal with complicated nonlinear stochastic dynamical systems which describe the adaptation processes. In this paper, we will illustrate some of the basic concepts, methods and results through some simple examples. The following fundamental questions will be discussed: How much information is needed for estimation? How to deal with uncertainty by adaptation? How to analyze an adaptive system? What are the convergence or tracking performances of adaptation? How to find the proper rate of adaptation in some sense? We will also explore the following more fundamental questions: How much uncertainty can be dealt with by adaptation ? What are the limitations of adaptation ? How does the performance of adaptation depend on the prior information ? We will partially answer these questions by finding some 'critical values' and establishing some 'Impossibility Theorems' for the capability of adaptation, for several basic classes of nonlinear dynamical control systems with either parametric or nonparametric uncertainties.

Adaptive Robust Control for A Class of Nonlinear Systems with Unknown Uncertainties

The problem of robust stabilization for nonlinear systems with partially known uncertainties is considered in this paper. The required information about uncertainties in the system is merely that the uncertainties are bounded, but the upper bounds are incompletely known. This paper can be viewed as an extension of the work in reference [1]. To compensate the uncertainties, an adaptive robust controller based on Lyapunov method is proposed and the design algorithm is also suggested. Compared with some previous controllers which can only ensure ultimate uniform boundedness of the systems, the controller given in the paper can make sure that the obtained closed-loop system is asymptotically stable in the large. Simulations show that the method presented is available and effective.

Adaptive Control of MIMO Mechanical Systems with Unknown Actuator Nonlinearities Based on the Nussbaum Gain Approach

This paper investigates MIMO mechanical systems with unknown actuator nonlinearities. A novel Nussbaum analysis tool for MIMO systems is established such that unknown time-varying control coefficients are tackled. In contrast to existing literatures on continuous-time systems, the newly-developed Nussbaum tool focuses on extending the traditional Nussbaum result from one dimensional case to the multiple one. Specifically, not only the multiple unknown input coefficients are extended to the time-varying, but also the limitation of the prior knowledge of coefficients' upper and lower bounds is removed. Furthermore, an adaptive robust controller associated with the proposed tool is presented. The asymptotic tracking of MIMO mechanical systems is guaranteed with the help of the Lyapunov Theorem. Finally, a simulation example is provided to examine the validity of the proposed scheme. © 2014 Chinese Association of Automation.

Nonlinear adaptive switching control for a class of non-affine nonlinear systems

An improved nonlinear adaptive switching control method is presented to relax the assumption on the higher order nonlinear terms of a class of discrete-time non-affine nonlinear systems. The proposed control strategy is composed of a linear adaptive controller, a neural network(NN) based nonlinear adaptive controller and a switching mechanism. An incremental model is derived to represent the considered system and an improved robust adaptive law is chosen to update the parameters of the linear adaptive controller. A new performance criterion of the switching mechanism is designed to select the proper controller. Using this control scheme, all the signals in the system are proved to be bounded. Numerical examples verify the effectiveness of the proposed algorithm.

Synchronization of hyperchaotic Chen systems:a class of the adaptive control approach

The synchronization of hyperchaotic Chen systems is considered. An adaptive synchronization approach and a cascade adaptive synchronization approach are presented to synchronize a drive system and a response system. By utilizing an adaptive controller based on the dynamic compensation mechanism, exact knowledge of the systems is not necessarily required, and the synchronous speed is controllable by tuning the controller parameters. Sufficient conditions for the asymptotic stability of the two synchronization schemes are derived. Numerical simulation results demonstrate that the adaptive synchronization scheme with four control inputs and the cascade adaptive synchronization scheme with only one control signal are effective and feasible in chaos synchronization of hyperchaotic systems.

Adaptive fuzzy tracking control for a class of switched MIMO nonlinear systems

An adaptive fuzzy tracking control scheme is presented for a class of switched multi-input-multi-output (MIMO) nonlinear systems with disturbances under arbitrary switching. Adaptive fuzzy systems are employed to approximate the unknown functions on line,and a systematic framework for adaptive fuzzy tracking controller design is given,where the dynamic surface control (DSC) approach is used to solve the problem of "explosion of complexity"in the backstepping design procedure. According to the common Lyapunov function theory,it is proved that the proposed controller can guarantee the boundedness of all signals in the closed loop system. Finally,the simulation results demonstrate the validity of the control approach.

Experimental Studies of Adaptive Conscious Systems

Human consciousness is the most interesting and mysterious phenomenon in the world.In this paper, the results of the computational studying and simulation of the conscious behaviour,such as the learning of language and image patterns, traditional conditioning, association, imagination and dream, have been presented. Based on these results, an experimental conscious systemCONSCITRON, has been developed. Further discussion on development of adaptive conscioussystems is also provided.

Noise-tolerate and adaptive coefficient zeroing neural network for solving dynamic matrix square root

The solving of dynamic matrix square root(DMSR)problems is frequently encountered in many scientific and engineering fields.Although the original zeroing neural network is powerful for solving the DMSR,it cannot vanish the influence of the noise perturbations,and its constant-coefficient design scheme cannot accelerate the convergence speed.Therefore,a noise-tolerate and adaptive coefficient zeroing neural network(NTACZNN)is raised to enhance the robust noise immunity performance and accelerate the conver-gence speed simultaneously.Then,the global convergence and robustness of the pro-posed NTACZNN are theoretically analysed under an ideal environment and noise-perturbed circumstances.Furthermore,some illustrative simulation examples are designed and performed in order to substantiate the efficacy and advantage of the NTACZNN for the DMSR problem solution.Compared with some existing ZNNs,the proposed NTACZNN possesses advanced performance in terms of noise tolerance,solution accuracy,and convergence rate.

ADAPTIVE PREDICTIVE CONTROL OF NEAR-SPACE VEHICLE USING FUNCTIONAL LINK NETWORK

A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle （NHV） in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive controller （OGPC） and the functional link network （FLN） direct adaptive law. OGPC is a continuous-time nonlinear predictive control law. The FLN adaptive law is used to offset the unknown uncertainties and disturbances in a flight through the online learning. The learning process does not need any offline training phase. The stability analyses of the NHV close-loop system are provided and it is proved that the system error and the weight learning error are uniformly ultimately hounded. Simulation results show the satisfactory performance of the con- troller for the attitude tracking.

Adaptive base-isolation of civil structures using variable amplification

Semi-active dampers are used in base-isolation to reduce the seismic response of civil engineering structures. In the present study, a new semi-active damping system using variable amplification will be investigated for adaptive baseisolation. It uses a novel variable amplification device （VAD） connected in series with a passive damper. The VAD is capable of producing multiple amplification factors, each corresponding to a different amplification state. Forces from the damper are amplified to the structure according to the current amplification state, which is selected via a semi-active control algorithm specifically tailored to the system＇s tmique damping characteristics. To demonstrate the effectiveness of the VAD-damper system for adaptive base-isolation, numerical simulations are conducted for three and seven-story base-isolated buildings subject to both far and near-field ground motions. The results indicate that the system can achieve significant reductions in response compared to the base-isolated buildings with no damper. The proposed system is also found to perform well compared to a typical semi-active damper.

Distributed Adaptive Resource Allocation:An Uncertain Saddle-Point Dynamics Viewpoint

This paper addresses distributed adaptive optimal resource allocation problems over weight-balanced digraphs.By leveraging state-of-the-art adaptive coupling designs for multiagent systems,two adaptive algorithms are proposed,namely a directed-spanning-tree-based algorithm and a node-based algorithm.The benefits of these algorithms are that they require neither sufficiently small or unitary step sizes,nor global knowledge of Laplacian eigenvalues,which are widely required in the literature.It is shown that both algorithms belong to a class of uncertain saddle-point dynamics,which can be tackled by repeatedly adopting the Peter-Paul inequality in the framework of Lyapunov theory.Thanks to this new viewpoint,global asymptotic convergence of both algorithms can be proven in a unified way.The effectiveness of the proposed algorithms is validated through numerical simulations and case studies in IEEE 30-bus and 118-bus power systems.

Optimal Constrained Self-learning Battery Sequential Management in Microgrid Via Adaptive Dynamic Programming

This paper concerns a novel optimal self-learning battery sequential control scheme for smart home energy systems. The main idea is to use the adaptive dynamic programming U+0028 ADP U+0029 technique to obtain the optimal battery sequential control iteratively. First, the battery energy management system model is established, where the power efficiency of the battery is considered. Next, considering the power constraints of the battery, a new non-quadratic form performance index function is established, which guarantees that the value of the iterative control law cannot exceed the maximum charging/discharging power of the battery to extend the service life of the battery. Then, the convergence properties of the iterative ADP algorithm are analyzed, which guarantees that the iterative value function and the iterative control law both reach the optimums. Finally, simulation and comparison results are given to illustrate the performance of the presented method. © 2017 Chinese Association of Automation.

Modeling and Robust Backstepping Sliding Mode Control with Adaptive RBFNN for a Novel Coaxial Eight-rotor UAV

This paper focuses on the robust attitude control of a novel coaxial eight-rotor unmanned aerial vehicles (UAV) which has higher drive capability as well as greater robustness against disturbances than quad-rotor UAV. The dynamical and kinematical model for the coaxial eight-rotor UAV is developed, which has never been proposed before. A robust backstepping sliding mode controller (BSMC) with adaptive radial basis function neural network (RBFNN) is proposed to control the attitude of the eightrotor UAV in the presence of model uncertainties and external disturbances. The combinative method of backstepping control and sliding mode control has improved robustness and simplified design procedure benefiting from the advantages of both controllers. The adaptive RBFNN as the uncertainty observer can effectively estimate the lumped uncertainties without the knowledge of their bounds for the eight-rotor UAV. Additionally, the adaptive learning algorithm, which can learn the parameters of RBFNN online and compensate the approximation error, is derived using Lyapunov stability theorem. And then the uniformly ultimate stability of the eight-rotor system is proved. Finally, simulation results demonstrate the validity of the proposed robust control method adopted in the novel coaxial eight-rotor UAV in the case of model uncertainties and external disturbances. © 2014 Chinese Association of Automation.

Composite Control of Linear/Adaptive Variable Structure Control

This paper first researches the system's response-feature due to the sliding regime related to the slope of a switching line, and then makes an investigation on the existence condition and hitting condition. Based on these conditions, the paper proposes a switching function that can realize an error adaptive variable structure control (AVSC) successfully. For eliminating the chattering of the sliding regime, this VSS introduces a dead zone, in which the PID control is applied. The composition of the PID control and the AVSC is called composite control of linear / AVSC. When the control signal is a large one, the AVSC is applied in the majority, and when the signal is a low one, the PID control is applied. Finally, an example of overload control of an anti-ship aerodynamic missile is illustrated to show the application of the composite control.

Comparing dynamical systems concepts and techniques for biomechanical analysis

Traditional biomechanical analyses of human movement are generally derived from linear mathematics.While these methods can be useful in many situations,they do not describe behaviors in human systems that are predominately nonlinear.For this reason,nonlinear analysis methods based on a dynamical systems approach have become more prevalent in recent literature.These analysis techniques have provided new insights into how systems(1) maintain pattern stability,(2) transition into new states,and(3) are governed by short-and long-term(fractal) correlational processes at different spatio-temporal scales.These different aspects of system dynamics are typically investigated using concepts related to variability,stability,complexity,and adaptability.The purpose of this paper is to compare and contrast these different concepts and demonstrate that,although related,these terms represent fundamentally different aspects of system dynamics.In particular,we argue that variability should not uniformly be equated with stability or complexity of movement.In addition,current dynamic stability measures based on nonlinear analysis methods(such as the finite maximal Lyapunov exponent) can reveal local instabilities in movement dynamics,but the degree to which these local instabilities relate to global postural and gait stability and the ability to resist external perturbations remains to be explored.Finally,systematic studies are needed to relate observed reductions in complexity with aging and disease to the adaptive capabilities of the movement system and how complexity changes as a function of different task constraints.

Complex Systems Theory: Some Considerations for Sociology

This essay presents a reflection on the main implications of Complexity Theory for science in general, redefining and dispelling myths of traditional science, and Sociology in particular, suggesting a redefinition of Parsons’ classic concept of Social System, articulated around the property of self-maintenance of order rather than on its possible discontinuity and instability. It argues that Complexity Theory has established the limits of Classic Science, leading to a more realistic awareness of working and evolution mechanisms of Natural and Social Systems and showing the limits of our capacity to predict and control events. Dissipative structures have shown the creative role of time. Instability, emergence, surprise, unpredictability are the rule rather than the exception when systems move away from equilibrium (entropy), even if these processes are generated from a system’s deterministic working mechanisms. Therefore, we have come to realize how constructive the contribution of Complexity is, in regards to the long lasting problem of the relationship between order and disorder. Today, the terms of this relationship have been re-specified in its new configuration of inter-relationship link, according to a unicum which finds its synthesis in self-organization and deterministic chaos concepts. From this perspective, as Prigogine suggested, studies on Complex Systems are heading toward a historical, biological conception of Physics, and a new alliance between natural systems and living, social systems. Non-linearity, far from equilibrium self-organization, emergence and surprise meet at all levels, as this paper attempts to highlight. In Sociology, insights of Complexity Theory have contributed to a new way of thinking about social systems, by re-addressing some fundamental issues starting to social system, emergence and change concepts. The current social system conception as complex dynamical systems is supported by a profitable use of non-liner models (in particular, the Logistic map) in the study of social processes.

DESIGN OF RECONFIGURABLE MANUFACTURING SYSTEMS WITH STRONGLY COUPLED NATURE

Today＇s manufacturing cnvironmem forces manufacturing companies to make as many product variations as possible at affordable costs within a short time. Mass customisation is one of most important technologies for companies to achieve their objectives. Efforts to mass customisation should be made on two aspects： （1） To modularize products and make them as less differences as possible; （2） To design manufacturing resources and make them provide as many processes variations as possible. This paper reports our recent work on aspect （2）, i.e. how to design a reconfignrable manufacturing system （RMS） so that it can be competent to accomplish various processes optimally; Reconfignrable robot system （RRS） is taken as an example. RMS design involves architecture design and configuration design, and configuration design is further divided in design analysis and design synthesis. Axiomatic design theory （ADT） is applied to architecture design, the features and issues of RRS configuration design are discussed, automatic modelling method is developed for design analysis, and concurrent design methodology is presented for design synthesis.

Design and Test of Multibus Adapter System on a Chip for Fault Tolerant Computer Systems

In order to improve the system reliability and performance and to reduce the system cost, volume and weight, we have designed, fabricated and tested the multibus adapter system of a trimodular redundant fault tolerant computer system on a single chip of 5000 gate CMOS gate array. The design, fabrication and test of this single chip system will be discussed..