Dynamic Fuzzy Controlled RWA Algorithm for IP/GMPLS over WDM Networks

This paper proposes a dynamic RWA scheme using fuzzy logic control on IP/GMPLS over WDM networks to achieve the best quality of network transmission. The proposed algorithm dynamically allocates network resources and reserves partial bandwidth based on the current network status, which includes the request bandwidth, average utilization for each wavelength and its coefficient of variance (C.V.) of data traffic, to determine whether the connection can be set up. Five fuzzy sets for request bandwidth, average rate and C.V. of data traffic are used to divide the variable space: very large (LP), large (SP), normal (ZE), small (SN), and very small (LN). Setting the fuzzy limit is a key part in the proposed algorithm. The simulation of scenarios in this paper has two steps. In the first step, the adaptive fuzzy limits are evaluated based on average transmission cost pertaining to ten network statuses. The second step is to compare the proposed algorithm with periodic measurement of traffic (PMT) in ATM networks in six network situations to show that the proposed FC-RWA algorithm can provide better network transmission.     

Scaling CMOS devices through alternative structures

The conventional wisdom holds that CMOS devices cannot be scaled much further from where they are today because of several device physics limitations such as the large tunneling current in very thin gate dielectrics. It is shown that alternative device structures can allow CMOS transistors to scale by another 20 times. That is as large a factor of scaling as what the semiconductor industry accomplished in the past 25 years. There will be many opportunities and challenges in finding novel device structures and new processing techniques, and in understanding the physics of future devices.     

Research on vacuum gripper based on fuzzy control for micromanipulators

This paper presents a vacuum gripper (as an actuator of an intelligent micromanipulator) for micro objects (with a diameter of 100～300?μm) assembly tasks.The gripper is composed of a vacuum unit and a control unit.The vacuum unit with a proportional valve and a pressure sensor,and the control unit with a PC+MCU two-layered control architecture are designed.The mechanical structure,workflow and major programs of the micro-gripper are presented. This paper discusses the major components of the adhesion force acting on micro objects.Some equations of the operation conditions in three phases of pick,hold and place are derived by mechanics analysis.The pneumatic system's pressure loss is inevitable.There are some formulas for calculating the amount of the pressure loss,but parameters in formulas are difficult to be quantified and evaluated.To control the working pressure accurately,a pressure controller based on fuzzy logic is designed.With MATLAB's fuzzy logic toolbox,simulation experiments are performed to validate the performance of the fuzzy PD controller. The gripper is characterized by a steady and reliable performance and a simple structure,and it is suitable for handling micro objects with a sub-millimeter size.     

Research on vacuum gripper based on fuzzy control for micromanipulators

This paper presents a vacuum gripper （as an actuator of an intelligent micromanipulator） for micro objects （with a diameter of 100 - 300μm） assembly tasks. The gripper is composed of a vacuum unit and a control unit. The vacuum unit with a proportional valve and a pressure sensor, and the control unit with a PC ＋ MCU two-layered control architecture are designed. The mechanical structure, workflow and major programs of the micro-gripper are presented. This paper discusses the major components of the adhesion force acting on micro objects. Some equations of the operation conditions m three phases of pick, hold and place are derived by mechanics analysis. The pneumatic system＇s pressure loss is inevitable. There are some formulas for calculating the amount of the pressure loss, but parameters in formulas are diffficult to be quantified and evaluated. To control the working pressure accurately, a pressure controller based on fuzzy logic is designed. With MATLAB＇s fuzzy logic toolbox, simulation experiments are performed to validate the performance of the fuzzy PD controller. The gripper is characterized by a steady and reliable performance and a simple structure, and it is suitable for handling micro objects with a sub-millimeter size.     

Recent advances on dynamic learning from adaptive NN control

Notice that intelligent control is developed based on the learning and control abilities of human beings. As such, an ideal intelligent control system should not only have the ability to adapt to complex dynamic environments, but also the ability to imitate human learning. The learning ability means to acquire the knowledge of uncertain closed-loop dynamics from dynamic environments, and use the stored experience knowledge to improve control performance [1,2]. Such an autonomous learning has been viewed as one of the most important and indispensable capabilities to achieve a high degree of system autonomy. The existing intelligent control schemes, including adaptive neural control and adaptive fuzzy control, are usually exclusively on the system stability by the use of online adjustment parameters to estimate the unknown dynamics, in which the exponential convergence of the estimated parameters is not a major concern. As a result, most of existing intelligent control schemes still need to readjust the controller parameters for the same or similar control tasks. In this sense, as shown in \cite{3}, the learning ability of intelligent control is actually very limited and still needs the further research. However, it is very challenging to acquire and express the knowledge of unknown dynamics in a nonstationary or dynamic environment....     

Neuro-fuzzy and model-based motion control for mobile manipulator among dynamic obstacles

This paper focuses on autonomous motion control of a nonholonomic platform with a robotic arm, which is called mobile manipulator. It serves in transportation of loads in imperfectly known industrial environments with unknown dynamic obstacles. A union of both procedures is used to solve the general problems of collision-free motion. The problem of collision-free motion for mobile manipulators has been approached from two directions, Planning and Reactive Control. The dynamic path planning can be used to solve the problem of locomotion of mobile platform, and reactive approaches can be employed to solve the motion planning of the arm. The execution can generate the commands for the servo-systems of the robot so as to follow a given nominal trajectory while reacting in real-time to unexpected events. The execution can be designed as an Adaptive Fuzzy Neural Controller. In real world systems, sensor-based motion control becomes essential to deal with model uncertainties and unexpected obstacles.     

Adaptive type-2 fuzzy sliding mode controller for SISO nonlinear systems subject to actuator faults

In this paper, an adaptive type-2 fuzzy sliding mode control to tolerate actuator faults of unknown nonlinear systems with external disturbances is presented. Based on a redundant actuation structure, a novel type-2 adaptive fuzzy fault tolerant control scheme is proposed using sliding mode control. Two adaptive type-2 fuzzy logic systems are used to approximate the unknown functions, whose adaptation laws are deduced from the stability analysis. The proposed approach allows to ensure good tracking performance despite the presence of actuator failures and external disturbances, as illustrated through a simulation example.     

A framework for multi-robot motion planning from temporal logic specifications

We propose a framework for the coordination of a network of robots with respect to formal requirement specifications expressed in temporal logics.A regular tessellation is used to partition the space of interest into a union of disjoint regular and equal cells with finite facets,and each cell can only be occupied by a robot or an obstacle.Each robot is assumed to be equipped with a finite collection of continuous-time nonlinear closed-loop dynamics to be operated in.The robot is then modeled as a hybrid automaton for capturing the finitely many modes of operation for either staying within the current cell or reaching an adjacent cell through the corresponding facet.By taking the motion capabilities into account,a bisimilar discrete abstraction of the hybrid automaton can be constructed.Having the two systems bisimilar,all properties that are expressible in temporal logics such as Linear-time Temporal Logic,Computation Tree Logic,and μ -calculus can be preserved.Motion planning can then be performed at a discrete level by considering the parallel composition of discrete abstractions of the robots with a requirement specification given in a suitable temporal logic.The bisimilarity ensures that the discrete planning solutions are executable by the robots.For demonstration purpose,a finite automaton is used as the abstraction and the requirement specification is expressed in Computation Tree Logic.The model checker Cadence SMV is used to generate coordinated verified motion planning solutions.Two autonomous aerial robots are used to demonstrate how the proposed framework may be applied to solve coordinated motion planning problems.     

Adaptive sliding mode control for MIMO nonlinear systems based on fuzzy logic scheme

In this study an indirect adaptive sliding mode control (SMC) based on a fuzzy logic scheme is proposed to strengthen the tracking control performance of a general class of multi-input multi-output (MIMO) nonlinear uncertain systems. Combining reaching law approach and fuzzy universal approximation theorem, the proposed design procedure combines the advantages of fuzzy logic control, adaptive control and sliding mode control. The stability of the control systems is proved in the sense of the Lyapunov second stability theorem. Two simulation studies are presented to demonstrate the effectiveness of our new hybrid control algorithm.     

Observer-based adaptive fuzzy control for a class of nonlinear time-delay systems

An observer-based adaptive fuzzy control is presented for a class of nonlinear systems with unknown time delays. The state observer is first designed, and then the controller is designed via the adaptive fuzzy control method based on the observed states. Both the designed observer and controller are independent of time delays. Using an appropriate Lyapunov-Krasovskii functional, the uncertainty of the unknown time delay is compensated, and then the fuzzy logic system in Mamdani type is utilized to approximate the unknown nonlinear functions. Based on the Lyapunov stability theory, the constructed observer-based controller and the closed-loop system are proved to be asymptotically stable. The designed control law is independent of the time delays and has a simple form with only one adaptive parameter vector, which is to be updated on-line. Simulation results are presented to demonstrate the effectiveness of the proposed approach.     

Editorial: Special issue on control theory and technologies, in honor of the 70th birthday of Professor Frank L. Lewis

Control theory is the field that is deeply connected with mathematics. To control a physical process or a dynamical system, the control law, so-called control algorithm, is usually deduced in mathematical way. This leads a lot of control theorist to work on “pure control theory” and leave from the physical background. The gap between control theory and practice is sometimes pointed out by the engineers, since a lot of advanced control methods provided by the research community of control theory cannot be directly implemented for solving many industrial problems. Benchmark problem plays the role of bridge between the theoretical research and industrial practice. Usually, a benchmark problem is proposed by academic community or jointly with industrial community to highlight challenging issues in a focused problem with common interested practical background. For control problem, many technical committees of international federation of automatic control (IFAC) proposed a lot of benchmark problems in the past. These benchmark problems attracted attention from both of theorist and control practitioners, especially younger researchers and students, and pushed control theorist to challenge practical problem and innovation in industrial control technology. This special issue focused on the benchmark problems in automotive system control which are mainly organized by IFAC technical committee and the related community such as the society of instrument and control engineers (SICE), Japan....     

Fuzzy Logic Based Behavior Fusion for Navigation of an Intelligent Mobile Robot

This paper presents a new method for behavior fusion control of a mobile robot in uncertain environments.Using behavior fusion by fuzzy logic,a mobile robot is able to directly execute its motion according to range information about environments,acquired by ultrasonic sensors,without the need for trajectory planning.Based on low-level behavior control,an efficient strategy for integrating high-level global planning for robot motion can be formulated,since,in most applications,some information on environments is prior knowledge.A global planner,therefore,only to generate some subgoal positions rather than exact geometric paths.Because such subgoals can be easily removed from or added into the plannes,this strategy reduces computational time for global planning and is flexible for replanning in dynamic environments.Simulation results demonstrate that the proposed strategy can be applied to robot motion in complex and dynamic environments.     

Approximation-based adaptive fuzzy control for a class of non-strict-feedback stochastic nonlinear systems

This paper considers the problem of adaptive fuzzy control of a class of single-input/single-output （SISO） nonlinear stochastic systems in non-strict-feedback form. Fuzzy logic systems are used to approximate the uncertain nonlinearities and backstepping technique is utilized to construct an adaptive fuzzy controller. The proposed controller guarantees that all the signals in the resulting closed-loop system are bounded in probability. The main contribution of this note lies in providing a control strategy for a class of nonlinear systems in non- strict-feedback form. Simulation result is used to test the effectiveness of the suggested approach.     

Constraint-Based Fuzzy Models for an Environment with Heterogeneous Information-Granules

A novel framework for fuzzy modeling and model-based control design is described. Based on the theory of fuzzy constraint processing, the fuzzy model can be viewed as a generalized Takagi-Sugeno （TS） fuzzy model with fuzzy functional consequences. It uses multivariate antecedent membership functions obtained by granular-prototype fuzzy clustering methods and consequent fuzzy equations obtained by fuzzy regression techniques. Constrained optimization is used to estimate the consequent parameters, where the constraints are based on control-relevant a priori knowledge about the modeled process. The fuzzy-constraint-based approach provides the following features. 1） The knowledge base of a constraint-based fuzzy model can incorporate information with various types of fuzzy predicates. Consequently, it is easy to provide a fusion of different types of knowledge. The knowledge can be from data-driven approaches and/or from controlrelevant physical models. 2） A corresponding inference mechanism for the proposed model can deal with heterogeneous information granules. 3） Both numerical and linguistic inputs can be accepted for predicting new outputs. The proposed techniques are demonstrated by means of two examples： a nonlinear function-fitting problem and the well-known Box-Jenkins gas furnace process. The first example shows that the proposed model uses fewer fuzzy predicates achieving similar results with the traditional rule-based approach, while the second shows the performance can be significantly improved when the control-relevant constraints are considered.     

Observer-Based Adaptive Fuzzy Tracking Control Using Integral Barrier Lyapunov Functionals for A Nonlinear System With Full State Constraints

A new fuzzy adaptive control method is proposed for a class of strict feedback nonlinear systems with immeasurable states and full constraints.The fuzzy logic system is used to design the approximator,which deals with uncertain and continuous functions in the process of backstepping design.The use of an integral barrier Lyapunov function not only ensures that all states are within the bounds of the constraint,but also mixes the states and errors to directly constrain the state,reducing the conservativeness of the constraint satisfaction condition.Considering that the states in most nonlinear systems are immeasurable,a fuzzy adaptive states observer is constructed to estimate the unknown states.Combined with adaptive backstepping technique,an adaptive fuzzy output feedback control method is proposed.The proposed control method ensures that all signals in the closed-loop system are bounded,and that the tracking error converges to a bounded tight set without violating the full state constraint.The simulation results prove the effectiveness of the proposed control scheme.     

CAutoCSD – Evolutionary Search and Optimisation Enabled Computer Automated Control System Design

This paper attempts to set a unified scene for various linear time-invariant (LTI) control system design schemes, by transforming the existing concept of "computer-aided control system design" (CACSD) to novel "computer-automated control system design" (CAutoCSD). The first step towards this goal is to accommodate, under practical constraints, various design objectives that are desirable in both time and frequency domains. Such performance-prioritised unification is aimed at relieving practising engineers from having to select a particular control scheme and from sacrificing certain performance goals resulting from pre-commitment to such schemes. With recent progress in evolutionary computing based extra-numeric, multi-criterion search and optimisation techniques, such unification of LTI control schemes becomes feasible, analytical and practical, and the resultant designs can be creative. The techniques developed are applied to, and illustrated by, three design problems. The unified approach automatically provides an integrator for zero-steady state error in velocity control of a DC motor, and meets multiple objectives in the design of an LTI controller for a non-minimum phase plant and offers a high-performance LTI controller network for a non-linear chemical process.     

Spatio-Temporal Video Motion Detection Using Marked-Watershed and Morphological Operations

In this article, we describe a moving object detection method developed based on spatio-temporal information and marked-watershed for extracting the moving objects from a video sequence. The algorithm begins with difference image between two adjacent frames and, using the Canny operator on the difference image, determines the initial edge mask for the object in motion Morphological operators are applied to the initial edge map to obtain a temporal segmentation mask of the moving object and binary marker image of the foreground and background, which is subject to the watershed thresholding. The markers are used to modify multi-scale morphological gradient image of the current frame. Finally, the watershed algorithm is performed on the modified gradients to locate the non-stationary objects accurately in the spatial domain of motion frames. Experimental results show that the proposed technique can overcome the shortcoming of over-segmentation of the watershed algorithm. In computationally efficient way, it segments and extracts semantically meaningful objects, which are in slow or fast motion from the video frames of scenes involving complex background. Performance evaluation yields that the detection accuracy can be as high as 98% to 99% for different video sequences.     

Adaptive output consensus for heterogeneous nonlinear multi‑agent systems with multi‑type input constraints under switching‑directed topologies

This study concentrates on solving the output consensus problem for a class of heterogeneous uncertain nonstrict-feedback nonlinear multi-agent systems under switching-directed communication topologies, in which all followers are subjected to multi-type input constraints such as unknown asymmetric saturation, unknown dead-zone and their integration. A unified representation is presented to overcome the difficulties originating from multi-agent input constraints. Moreover, the uncertain system functions in a non-lower triangular form and the interaction terms among agents are dealt with by exploiting the fuzzy logic systems and their special property. Furthermore, by introducing a nonlinear filter to alleviate the problem of “explosion of complexity” during the backstepping design, a distributed common adaptive control protocol is proposed to ensure that the synchronization errors converge to a small neighborhood of the origin despite the existence of multiple input constraints and arbitrary switching communication topologies. Both stability analysis and simulation results are conducted to show the effectiveness and performance of the proposed control methodology.     

Backstepping adaptive fuzzy control of uncertain nonlinear systems against actuator faults

A class of unknown nonlinear systems subject to uncertain actuator faults and external disturbances will be studied in this paper with the help of fuzzy approximation theory. Using backstepping technique, a novel adaptive fuzzy control approach is proposed to accommodate the uncertain actuator faults during operation and deal with the external disturbances though the systems cannot be linearized by feedback. The considered faults are modeled as both loss of effectiveness and lock-in-place （stuck at some unknown place）. It is proved that the proposed control scheme can guarantee all signals of the closed-loop system to be semi-globally uniformly ultimately bounded and the tracking error between the system output and the reference signal converge to a small neighborhood of zero, though the nonlinear functions of the controlled system as well as the actuator faults and the external disturbances are all unknown. Simulation results demonstrate the effectiveness of the control approach.