This article presented a new set theory to deal with ambiguousness, which was entitled as an “Ambiguous Set Theory”. The proposed ambiguous set theory can represent any feature into four degrees of memberships, viz., true, false, ambiguous-true and ambiguous-false. This kind of representation provides granular visualization of features, and helps to model uncertainties very effectively. In this article, initially we discussed the motivation to introduce the theory of ambiguous set. Then, we proposed methodology of ambiguous set by: 1) defining it in a precise way, 2) presenting a mathematical representation for the set, and 3) giving various mathematical definitions for the set. Applications of the proposed ambiguous set were demonstrated in human brain MRI segmentation. Various comparison results demonstrated the effectiveness of the theory over existing well-known approaches of the image segmentation.

A method is presented for scene detection and estimation using high-resolution imagery acquired through autonomous drone navigation aided with landmark detection and recognition. The proposed system comprises a drone platform that facilitates efficient autonomous flight; it can capture images and provide real-time video streaming of the ground cover using a camera equipped with a 14-megapixel CMOS sensor and a fish-eye lens. In addition, landmark detection and recognition was performed by applying the histogram of oriented gradients and linear support vector machine methods on each frame of the video stream. The high spatial resolution of the acquired drone images makes the detection and interpretation of environments less complicated. First, through image processing, orthomosaic images and 3-D environment reconstruction (point clouds) of the scene are generated from a set of drone images by using an automatic photogrammetric technique called “structure from motion.” Subsequently, an unsupervised classification method is used to detect and differentiate environmental classes (scene interpretation) in the target or investigated area by using the high-resolution images. Finally, the results of the proposed method are evaluated by comparing them against ground-truth points.

This study presents a preliminary development of a robotic assistant system with its solutions for combining robotics and health-care devices. The mobile robotic system acts as the sensing equipment for monitoring the heart rate from the continuous electrocardiographic (ECG) waveforms in real-world testing. Routine ECG monitoring can aid clinic diagnoses in cardiac and non-cardiac diseases. In addition, with the aid by technical advancements in information and communication technology (ICT), the designed robot can provide the assistance in the remote symptom monitoring for end users susceptible to heart failure, and advance healthcare in terms of reducing physicians' visiting costs while maintaining the quality of care. To present the planned features of the designed robotic system with ECG monitoring and diagnosis assistance, consider an end user at home, where the experimental environment is deployed.

The mobile-wheeled inverted pendulum (MWIP) robot with dual-arm manipulators is an unstable and nonlinear system with multi-variables and strong coupling. This paper proposes an intelligent motion control system to attain the desired position and direction while keeping the MWIP robot balanced. The control system is comprised of a position controller, a balance control, a direction controller and a COM compensator. A cascade structure, which combines the inner loop of balance control and the outer loop of position control, is illustrated its effectiveness. Since the dual-arm manipulator cannot execute the given motions of the end effector and move the dual-arm stable simultaneously, the COM compensator is designed to eliminate steady state error in the position controller. Finally, the real-time experimental results show that the COM compensator can improve the control performance of station-keeping testing scenario for the MWIP system.

This paper presents a new nonconvex design algorithm for guaranteed cost control of polynomial fuzzy systems that include the Takagi-Sugeno (T-S) fuzzy systems as a special case. We derive a sum-of-squares (SOS) noconvex conditions to achieve guaranteed cost control of polynomial fuzzy systems. Although SOS-based design approaches are regarded as a post LMI-based design approach, there exist some remaining problems to be solved in systems analysis and design. To solve the remaining problems of the SOS-based design approaches, this paper gives new ideas that directly solve nonconvex SOS design conditions in guaranteed cost control using the so-called path-following algorithm. In particular, we bring a new scheme that minimizes the upper bound of a given performance function in parallel with minimizing a parameter of checking non-negativity for SOS design conditions. The two parameters minimization in the path-following algorithm is realized by introducing a double-loop structure. Finally, two complex nonlinear system design examples (a polynomial chaotic system and a complicated nonlinear system) are employed to illustrate the validity and applicability of the proposed nonconvex design algorithm for guaranteed cost control of polynomial fuzzy systems.

The current populace of the elderly is apparently abandoned by the younger generations due to their individual circumstances. To heighten the vitality and strengthen the fitness of elders, assisting a home care system can be an admittance that provides comprehensive nursing and monitoring them in the regular interim. To deliver an interactive service supervision platform to the elders a smart environment of various sensors are clubbed together to establish an intuitive platform that can control the home appliances and gadgets within the living space of elders. The proposed system used voice and gesture (MPU6050 accelerometer) to control the home appliances like turning on/off the light, closing/opening of curtains, TV, and fan or AC within the living spaces. The system also monitors the real-time activity like heart rate and body temperature for the elderly citizens. In the case of emergency, for instance, anomalous behaviors like heart stroke occurs, the proposed system set-up triggers an alarm and the emergency bulb will be strikes "on" to alert their kin. This smart environment can set the temperature and help control the living parameters based on the users' comfort and their health conditions. The whole design is to provide modest support systems for the elder to live healthily and safely in an independent living environment.

This paper presents a method that can detect driver's drowsiness by using the wavelet packet transform (WPT) and functional link-based fuzzy neural network (FLFNN) models. Drowsy drivers have been reported to be vulnerable to car accidents. Early detection of drowsiness can help alert drivers or passengers to provide a safety drive on the road. For those old models or cars without equipped with advanced high technologies, there is a dire need to install sensor devices that can effectively detect drowsy status of drivers at an early stage. Photoplethysmography (PPG) is a non-invasive optical technique that measures relative blood volume changes in the blood vessels and has been universally used for research and physiological study. We develop such PPG sensor devices to be installed on the steering wheel to detect the physiological conditions (such as normal to drowsy) by using parameters extracted from the heart rate variability (HRV) obtained from PPG signal calculation. Experimental results revealed that the proposed model is effective in assessing the drowsy levels of drivers.

Eye movements can provide important information for human-computer interactive applications. Due to the progress of computer technology, the detecting accuracy and speed of pattern recognition are promoted. Additionally, according to research advances in embedded systems, the applications of digital cameras, such as internet cameras, smart phones and smart TV, and smart cars, are widely used in nowadays. Therefore, we propose a set of real-time human-eye detection and tracking systems with human-computer interaction applications. This technique can obtain eye movements and can be adopted as interactive control commands on driver assistance systems. The proposed system is implemented on an OMAP4430 for embedded applications, and experimental results show that the proposed architecture is capable of effective and real-time eye position detection and event identification for human-computer interactive applications on driver assistance systems.

This paper proposes the development of an autonomous navigation robot with hand-cleaning service in indoor environments. To navigate in unknown environments and provide service, the robot is with several intelligent behaviors including wall-following, obstacle avoidance, autonomous navigation, and human detection. A laser-sensor-based approach is used in the wall-following and obstacle avoidance behavior controllers. A preliminary map-matching algorithm is applied in the localization strategy of autonomous navigation in which the robot can acquire the current location and then move toward to the target position. In this study a hand-cleaning mechanism is embedded into the robot and the service will activate while a human is recognized within the designated range. The overall robotic system is carried out using a two-wheeled driving mobile robot with LabVIEW as an integration tool. The experimental results demonstrate the practicable application of the proposed approach.

This paper presents the development of a robust active suspension systems based on a quarter-car model with consideration of system nonlinearities. The sliding mode control with a filtered feedback scheme is exploited to design an active suspension controller, which can improve ride comfort and road handling performance simultaneously. Kalman filter is designed to estimate the state responses as required for active force calculation. The designed controller is applied to the CarSim vehicle model in order to evaluate its effectiveness. Besides, the robustness of the proposed approach is also investigated for vehicle parameters variation. The results of time and frequency responses indicate the improved ride comfort without causing any problem in suspension working limits.

In this paper, we propose an online observer-based Takagi–Sugeno (T–S) fuzzy-output tracking-control technique and an improved generalized projection-update law for a class of general nonaffine nonlinear systems with unknown functions and external disturbances. First, a T–S fuzzy model and a mean-value estimation technique are adopted to approximate a so-called virtual linearized system (VLS) of a real system and avoiding a high-order derivative problem, respectively. Second, a novel design concept combining the T–S fuzzy controller, observer, and tuning algorithm by neural networks is proposed to improve system performance. After that, we also use improved generalized projection-update laws, which prevent parameters drift and confine adjustable parameters to the specified regions, to tune adjustable parameters. As a result, both the stability guarantee based on strictly positive real (SPR) Lyapunov theory and Barbalat's lemma and the better tracking performance are concluded. To illustrate the effectiveness of the proposed T–S fuzzy controller and observer-design methodology, numerical simulation results are given in this paper.

This paper proposes a novel method of online modeling and control via the Takagi-Sugeno (T-S) fuzzy-neural model for a class of uncertain nonlinear systems with some kinds of outputs. Although studies about adaptive T-S fuzzy-neural controllers have been made on some nonaffine nonlinear systems, little is known about the more complicated uncertain nonlinear systems. Because the nonlinear functions of the systems are uncertain, traditional T-S fuzzy control methods can model and control them only with great difficulty, if at all. Instead of modeling these uncertain functions directly, we propose that a T-S fuzzy-neural model approximates a so-called virtual linearized system (VLS) of the system, which includes modeling errors and external disturbances. We also propose an online identification algorithm for the VLS and put significant emphasis on robust tracking controller design using an adaptive scheme for the uncertain systems. Moreover, the stability of the closed-loop systems is proven by using strictly positive real Lyapunov theory. The proposed overall scheme guarantees that the outputs of the closed-loop systems asymptotically track the desired output trajectories. To illustrate the effectiveness and applicability of the proposed method, simulation results are given in this paper.

An on-line tracking controller design based on using T-S fuzzy-neural modeling for a class of general robot manipulators is investigated in this paper. Also, we use projection update laws to tune adjustable parameters for preventing parameters drift. In addition, stability of the closed-loop systems is proven by using strictly-positive-real (SPR) Lyapunov theory. The proposed overall scheme guarantees that all signals involved are bounded and the outputs of the closed-loop system asymptotically track the desired output trajectories. Finally, an example including two cases confirms the effectiveness of the proposed method.

This paper presents a safe and comfortable longitudinal automation system which incorporates human-in-the-loop technology. The proposed system has a hierarchical structure that consists of an adaptive detection area, a supervisory control, and a regulation control. The adaptive detection area routes the information from on-board sensors to ensure the detection of vehicles ahead, particularly when driving on curves. Based on the recognized target distance from the adaptive detection area, the supervisory control determines the desired velocity for the vehicle to maintain safety and smooth operation in different modes. The regulation control utilizes a soft-computing technique and drives the throttle to execute the commanded velocity from the supervisory control. The feasible detection range is within 45 m, and the high velocity for the system operation is up to 100 km/h. The throttle automation under low velocity at 10-30 km/h can also be well managed by the regulation control. Numerous experimental tests in a real traffic environment exhibit the system's validity and achievement in the desired level of comfort through the evaluation of international standard ISO 2631-1.

Vehicles are not always driven on smooth roads. If parts of the suspension system fail, it becomes an uncertain system. Thus we need an approximator to remodel this uncertain system to maintain good control. In this paper, we propose a new method to on-line identify the uncertain suspension system and design a T-S fuzzy-neural controller to control it. We first use the mean value theorem to transform the active suspension system into a virtual linearized system. In addition, an on-line adaptive T-S fuzzy-neural modeling approach to the design of robust tracking controllers is developed for the uncertain active suspension system. Finally, this paper gives simulation results of an uncertain suspension system with the on-line adaptive T-S fuzzy-neural controller, and is shown to provide good effectiveness under the conditions that parts of the suspension system fail.

This paper proposes anti-lock braking system to integrate with active suspensions system applied in a quarter vehicles model, and can use a road estimate to get the road condition. This estimate is based on the LuGre friction model with a road condition parameter, and can transmit a reference slip ration to slip ratio controller through a mapping function considering the effect of road characteristics. In the controller design, an observer-based direct adaptive fuzzy-neural controller (DAFC) for an ABS is developed. After, this paper will discuss that active suspension system influence on ABS. Active suspension systems are not ideal, unchanging, and certain, as many control systems assume. If parts of the suspension system fail, it becomes an uncertain system. In such cases, we need an approximator to remodel this uncertain system to maintain good control. We propose a new method to on-line identify the uncertain active suspension system and design a T-S fuzzy-neural controller to control it. Finally, integrating algorithm is constructed to coordinate these two subsystems. Simulation results of the ABS with active suspension system, and is shown to provide good effectiveness under varying conditions.

This paper proposes a radial basis function neural network adaptive backstepping controller (RBFNN_ABC) for multiple-input multiple-output (MIMO) nonlinear systems in block-triangular form. The control scheme incorporates the adaptive neural backstepping design technique with a first-order filter at each step of the backstepping design to avoid the higher-order derivative problem, which is generated by the backstepping design. This problem may create an unpredictable and unfavorable influence on control performance because higher-order derivative term errors are introduced into the neural approximation model. Finally, simulation results demonstrate that the output tracking error between the plant output and the desired reference can be made arbitrarily small.

This paper investigates a geometric property of time-optimal problem in the Takagi-Sugeno (T-S) fuzzy model via Lie algebra. We will focus on the existence of a time-optimal solution, singularity of switching function, and number of switching. These inherent problems are considered because of their rich geometric properties. The sufficient condition for the existence of a time-optimal solution reveals the controllability of T-S fuzzy model that can be found by the generalized rank condition. The time-optimal controller can be found as the bang-bang type with a finite number of switching by applying the maximum principle. In the study of the singularity problem, we will focus on the switching function whenever it vanishes over a finite time interval. Finally, we show that the bounded number of switching can be found if the T-S model (also a nonlinear system) is solvable.

Automated vehicle control systems are a key technology for intelligent vehicle highway systems (IVHSs). This paper presents an automated vehicle control algorithm for combined longitudinal and lateral motion control of highway vehicles, with special emphasis on front-wheel-steered four-wheel road vehicles. The controller is synthesized using an online neural-estimator-based control law that works in combination with a lateral velocity observer. The online adaptive neural-estimator-based design approach enables the controller to counteract for inherent model discrepancies, strong nonlinearities, and coupling effects. The neurocontrol approach can guarantee the uniform ultimate bounds (UUBs) of the tracking and observer errors and the bounds of the neural weights. The key design features are (1) inherent coupling effects will be taken into account as a result of combining of the two control issues, viz., lateral and longitudinal control;(2) rather ad hoc numerical approximations of lateral velocity will be avoided via a combined controller-observer design; and (3) closed-loop stability issues of the overall system will be established. The algorithm is validated via a formative mathematical analysis based on a Lyapunov approach and numerical simulations in the presence of parametric uncertainties as well as severe and adverse driving conditions.

This paper proposes an adaptive wavelet neuro control (AWNC) system, which is composed of a neural controller and a tangent controller. The neural controller utilizes a wavelet neural network to mimic an ideal controller and the tangent controller is designed to compensate for the approximation error between the ideal controller and the neural controller with using a hyperbolic tangent function. The main advantage of the proposed AWNC is that the weights are tuned on-line, and the uniformly ultimately bounded stability of the system can be guaranteed in the Lyapunov sense. Finally, to show the effectiveness of the proposed AWNC, it is applied to control a chaotic dynamic system. Simulation results verify that the proposed AWNC system can achieve favorable tracking performance. Since the developed AWNC system has no chattering phenomena in the control efforts, it is suitable for practical applications.

Antilock braking system (ABS) controls the slip of each wheel of a vehicle to prevent it from locking such that a high friction is achieved and steerability is maintained. It is designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this study, a neurocontroller system, which is composed of a computation controller and a compensation controller, is developed for ABS. The computation controller containing a radial basis function neural network uncertainty observer is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. The Lyapunov stability theory is utilized to derive the parameter tuning algorithm, so that the uniformly ultimately bound stability of the closed-loop system can be achieved. Simulations are performed to demonstrate the effectiveness of the proposed neurocontroller system under various road conditions.

Vehicle automation is an important research topic of advanced vehicle systems (AVS). Taiwan iTS-1 is the first smart car with autonomous driving in Taiwan. In this paper, an embedded driving-assistance system is presented. The definition of hierarchical-control structure is necessary in the system to deal with sensorial inputs and environmental and procedural knowledge to manage vehicle actuators in order to accomplish various driving tasks. Upper-level control perceives road environment and determines the proper and safe operation modes including lane-keeping, lane-change, cruise control, adaptive cruise control, and stop-and-go. In each mode, the desired-velocity and reference-trajectory are primarily determined, and then are forwarded to vehicle-body control. To incorporate well driver behavior into our system, vehicle-body control utilizes the fuzzy control technique to manage the fundamental actuators of vehicle, steering wheel, throttle, and brake, to adapt to the desired command (velocity and trajectory). The core controller is built-in on a DSP-based embedded computing platform. The aim of our system is to provide the driving-assistance in the same way human drivers do.

A novel prediction scheme is proposed for real-time MPEG video to predict the burst and long-range dependent traffic. The trend and periodic characteristics of MPEG video traffic are fully captured by a proposed stochastic state-space dynamic model. Then a recursive Hinfinfiltering algorithm is proposed to estimate traffic for long-range prediction. Simulation results based on real MPEG traffic data show that the proposed scheme has superior performance and lower complexity than some adaptive neural network methods, such as TDNN, NARX, and Elman neural networks.

In this paper, an intelligent automated lane-keeping system is proposed and implemented on our vehicle platform, i.e., TAIWAN i TS-1. This system challenges the online integrating heterogeneous systems such as a real-time vision system, a lateral controller, in-vehicle sensors, and a steering wheel actuating motor. The implemented vision system detects the lane markings ahead of the vehicle, regardless of the varieties in road appearance, and determines the desired trajectory based on the relative positions of the vehicle with respect to the center of the road. To achieve more humanlike driving behavior such as smooth turning, particularly at high levels of speed, a fuzzy gain scheduling (FGS) strategy is introduced to compensate for the feedback controller for appropriately adapting to the SW command. Instead of manual tuning by trial and error, the methodology of FGS is designed to ensure that the closed-loop system can satisfy the crossover model principle. The proposed integrated system is examined on the standard testing road at the Automotive Research and Testing Center (ARTC)1and extra-urban highways.

An intelligent tracking control using a dynamic fuzzy neural network (DFNN) is proposed in this paper. The intelligent tracking control system is comprised of a computation controller and a robust controller. The computation controller containing a DFNN identifier is the principal controller, and the robust controller is designed to achieveL2tracking performance. The DFNN identifier uses the structure and parameter learning phases to online estimate the unknown control dynamics equation. Finally, the proposed intelligent tracking control system is applied to control a second-order chaotic circuit system. The simulation results show that the proposed intelligent tracking control system can achieve favorable tracking performance by incorporating of DFNN identification, sliding-mode control and robust control techniques.

In this paper, an integrated headway adaptation (HA) with collision avoidance (CA) system is proposed to be associated with a human factor aspect for driving assistance. The maneuver in the CA strategy is developed based on the idea that warning or potential crash information generally can be graded such that the driver need not react to a discrete on/off warning. The control law for the HA strategy is designed by using a modified sliding surface to achieve driving comfort requirement and also stability in car-following. To simulate the speed controlling of human drivers, the throttle and brake actuation is directed by the regulation control which is designed based on a human reasoning approach. A description of an experimental vehicle and real traffic testing in highways and urban-like tracks shows the potential and validness of the integrated system.

The main objective of this paper is to design and implement a real-time autonomous navigation system for intelligent vehicles by integrating a real-time kinematics differential global position system (RTK-DGPS) and a laser range finder. Firstly, a fuzzy logic algorithm for target position tracking is proposed to achieve the human driving concept. In addition, an actuator is equipped in the experimental vehicle TAIWAN iTS-1 so that the steering wheel can be controlled to let the vehicle follows a desired trajectory in digital maps. A laser range finder is used to detect obstacles in front of the vehicle for warning announcement and collision avoidance. A human machine interface is also built up to provide information from sensors. The performance and accuracy of the proposed system is verified by real-time experimental results.

Issues of stability and bifurcation phenomena in vehicle lateral dynamics are presented. Based on the assumption of constant driving speed, a second-order nonlinear lateral dynamics model is obtained. Local stability and existence conditions for saddle-node bifurcation appearing in vehicle dynamics with respect to the variations in front wheel steering angle are then derived via system linearization and local bifurcation analysis. Bifurcation phenomena occurring in vehicle lateral dynamics might result in spin and/or system instability. A perturbation method is employed to solve for an approximation of system equilibrium near the zero value of the front wheel steering angle, which reveals the relationship between sideslip angle and the applied front wheel angle. Numerical simulations from an example model demonstrate the theoretical results

Vision-based target tracking and fixation to keep objects that move in three dimensions in view is important for many tasks in several fields including intelligent transportation systems and robotics. Much of the visual control literature has focused on the kinematics of visual control and ignored a number of significant dynamic control issues that limit performance. In line with this, this paper presents a neural network (NN)-based binocular tracking scheme for high-performance target tracking and fixation with minimum sensory information. The procedure allows the designer to take into account the physical (Lagrangian dynamics) properties of the vision system in the control law. The design objective is to synthesize a binocular tracking controller that explicitly takes the systems dynamics into account, yet needs no knowledge of dynamic nonlinearities and joint velocity sensory information. The combined neurocontroller-observer scheme can guarantee the uniform ultimate bounds of the tracking, observer, and NN weight estimation errors under fairly general conditions on the controller-observer gains. The controller is tested and verified via simulation tests in the presence of severe target motion changes.

This paper proposes an intelligent tracking control for the chaotic systems via backstepping approach. The intelligent tracking control system is comprised of a neural controller and a robust controller. The neural controller containing a self-organizing fuzzy neural network (SOFNN) identifier is the principal controller, and the robust controller is designed to dispel the effect of minimum approximation error introduced by the SOFNN identifier. The SOFNN identifier is used to online estimate the chaotic dynamic function with structure and parameter learning phases of fuzzy neural network. Finally, simulation results verify that the proposed intelligent tracking control can achieve favorable tracking performance.

For many years, the control approaches for the dc-dc power converters are limited to PI controller structures. However, it gives the overshoot in output voltage as the rise time of response is reduced. To tackle this problem, an adaptive recurrent fuzzy neural network (ARENN) control system is developed in this paper. The on-line adaptive laws of the ARENN control scheme are derived based on the Lyapunov stability theorem, so that the stability of the system can be guaranteed. Experimental results show that the proposed ARFNN control scheme can achieve good regulation performances.

In this paper, a new method, applied the annealing robust Walsh function networks, is proposed to discretize the continuous-time controller in computer-controlled systems. That is, the annealing robust Walsh function networks are used to add nonlinearly and to approximate smooth controller with digital neural networks. Hence, the proposed controller is a new smooth controller that can replace original controller and independent of the sampling time under the Sample Theorem. Besides, the input-output stability is proposed for this discretizating continuous-time controller with the annealing robust Walsh function networks. Consequently, the proposed annealing robust Walsh function networks controller cannot only discretize the continuous-time controllers, but can also tolerate a wider range of sampling time uncertainty.

In this paper, the stability in static fuzzy control systems for linear systems with different fixed reference inputs and adjustable parameters are analyzed. Under certain conditions, the unique equilibriums of error in fuzzy control systems can be solved with fixed reference. Furthermore, when the unique equilibrium is stable, the steady state error can be obtained from this stable equilibrium. Under the adjustable parameters and reference inputs, the equilibriums may have stable and unstable transformations each other respect to absolutely stability. Additionally, the insight mechanism for oscillation of equilibrium is also given with a numerical example.

Based on some useful frequency domain methods, this paper proposes a systematic procedure to address the limit cycle prediction of a neural vehicle control system with adjustable parameters. A simple neurocontroller can be linearized by using describing function method firstly. According to the classical method of parameter plane, the stability of linearized system with adjustable parameters is then considered. In addition, gain margin and phase margin for limit cycle generation are also analyzed by adding a gain-phase margin tester into open loop system. Computer simulations show the efficiency of this approach.

This paper presents the integration design and implementation of a longitudinal automation system with the interaction of human-in-the-loop (HITL). The proposed system has a hierarchical structure composed and consists of an adaptive sensory processor, a supervisory control and a regulation control. The adaptive sensory processor routes the information from on-board sensors to avoid missing detection of the vehicle ahead. Based on the recognized measurement from the adaptive sensory processor, the supervisory control determines the desired velocity for the vehicle so as to maintain safety and smooth operation in different modes. The regulation control utilizes soft-computing technique and drives the throttle action to execute the desired velocity commanded from the supervisory control. The feasible sensory distance is within 40 m, and the according driving velocity can achieve 100 km/h upward. The challenge in low velocity operation can also be handled by the regulation control against gear changes and torque converter. Among experimental tests under various kinds of traffic flows, the system validness is exhibited and also the preferable comfort is achieved through the examination of international standard ISO 2631.

In this paper, an self-organizing neural-network-based adaptive control (SONNAC) system is developed. The SONNAC system is comprised of a neural controller and a compensation controller. The neural controller utilizes a self-organizing neural network (SONN) to mimic an ideal controller, and the compensation controller is designed to compensate for the approximation error between the neural controller and the ideal controller. When the approximation performance of the SONN is not good enough, the SONN can create new neurons in the hidden layer to decrease the approximation error. Moreover, the adaptive laws of controller parameters are derived in the sense of Lyapunov, so that the stability of the system can be guaranteed. Finally, to investigate the effectiveness of the proposed SONNAC system, the design methodology is applied to control a linear ultrasonic motor.

A self-tuning fuzzy PI controller (STFPIC) is designed elsewhere, using parallelly operated two rule-bases; one control rule-base, and the other gain rule-base, each having 49 rules. The output scaling factor (SF) of STFPIC is modified online by a gain updating factor following an operator's strategy. STFPIC is found to provide significantly improved performance for a wide range of processes. This study is an attempt for neuro-fuzzy implementations of STFPIC with considerably lesser number of rules, which are complete and capable of realizing almost similar performance as that of STFPIC. We consider two different structures of the proposed neuro-fuzzy PI controller (NFPIC); called NFPIC-1 and NFPIC-2, having only 50 and 49 rules respectively against 98 original rules of STFPIC. NFPIC-1 is similar in structure to that of STFPIC with two parallel rule-bases, each having 25 rules, whereas, the structure of NFPIC-2 is same as that of a conventional fuzzy controller with a single rule-base. Effectiveness of the developed neuro-fuzzy controllers (NFPIC-1 and NFPIC-2) is demonstrated using second-order linear as well as nonlinear processes.

This paper proposes a robust intelligent backstepping control (RIBC) scheme for the car-following control of a platoon of automated vehicles using a recurrent cerebellar model articulation controller (RCMAC) via the Hinfincontrol technique, so that the robust tracking performance can be achieved. In the RIBC system, an adaptive RCMAC is used to mimic an ideal backstepping control law and a robust controller is designed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors. Moreover, the Taylor linearization technique is employed to derive the linearized model of the RCMAC. The adaptation laws of the RIBC system are derived on the basis of the Lyapunov stability analysis and Hinfincontrol theory so that the stability of the system can be guaranteed. Finally, the simulation results denominate that the proposed RIBC system can achieve favorable tracking performance for a safe car-following control.

This paper introduces a new paradigm for avoiding vehicle collisions in intelligent transportation systems. The key feature of the concept is that we introduce interactive control of different vehicular sub-systems ultimately leading to interdependent maneuvering of vehicles in order to avoid collision encounters. A general scenario of motion of vehicular systems passing a common point at the same time is studied here. Using a fuzzy logic-based controller, collision avoidance maneuvers of the individual vehicular sub-systems are realized. An inter-vehicle communication (IVC) system that facilitates interactions between the vehicles is studied as a top tier above the individual vehicular controllers. The concept that is adopted here is that in order to control a general system with participatory sub-systems of vehicles, all the influencing members have to interactively work together in order to effectively negotiate to achieve an optimal result in avoiding collision encounters. While the interactive operations are being carried out, each vehicular sub-system may act as a 'master' one time and a 'slave' at another time. This switching between the states of 'master' and 'slave' should be done efficiently to better prevent a collision encounter. Even though above paradigm is proved only for two interactive sub-systems, it is emphasized that the concept can be extended to any number of vehicular sub-systems to realize effective collision avoidance maneuvers.

In this paper, the intelligent Stop and Go system (S&G) is designed and implemented for low speed traveling vehicles in urbane area. The human-driving like fuzzy logic control is applied for achieving desired speed and safe inter-vehicle spacing. By proposed intelligent Stop and Go system, the following functions can be obtained including collision avoidance, traffic accidence reduction, driving pressure decrease, and increment of traffic capability. Furthermore, the real car equipped with sensors and a longitudinal controller is provided to demonstrate three scenarios that meet the situations of low speed vehicles in urban area. Three seniors include pedestrian crossing, stop in front of obstacle, and Stop and Go. The application of proposed intelligent Stop and Go system is possible for low speed driving assistance in urban area by our experimental results.

In the work, we try to construct the corresponding S-system and modified power-low model from a dataset. These two mathematical models are highly nonlinear. Though they can clearly describe reactions among genes in the biological system, the identification is a tough work, especially for huge genes. We adopt the evolution strategy to achieve 16-genes modeling with 544 or 288 parameters. The time-course data of the yeast cell cycle is concerned. The proposed two different gene regulatory networks and their corresponding pathways can provide biological researchers for further experiments in yeast cell cycle control.

The consistency of a rule base is an essential issue for rule-based intelligent information processing. Due to the uncertainty inevitably included in the rule base, it is necessary to verify the consistency of the rule base while investigating, designing, and applying a rule-based intelligent system. In the framework of the lattice-valued first-order logic system LF(X), which attempts to handle fuzziness and incomparability, this article focuses on how to verify and increase the consistency degree of the rule base in the intelligent information processing system. First, the representations of eight kinds of rule bases in LF(X) as the generalized clause set forms based on these rule bases' nonredundant generalized Skolem standard forms are presented. Then an α-automated reasoning algorithm in LF(X), also used as an automated simplification algorithm, is proposed. Furthermore, the α-consistency and the α-simplification theories of the rule base in LF(X) are formulated, and especially the coherence between these two theories is proved. Therefore, the verification of the α-consistency of the rule base, often an infinity problem that is difficult to solve, can be transformed into a finite and achievable α-simplification problem. Finally, an α-simplification stepwise search algorithm for verifying the consistency of the rule base as well as a kind of filtering algorithm for increasing the consistency level of the rule base are proposed. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 399–424, 2006.

A neural network (NN) adaptive model-based combined lateral and longitudinal vehicle control algorithm for highway applications is presented in this paper. The controller is synthesized using a proportional plus derivative control coupled with an online adaptive neural module that acts as a dynamic compensator to counteract inherent model discrepancies, strong nonlinearities, and coupling effects. The closed-loop stability issues of this combined control scheme are analyzed using a Lyapunov-based method. The neurocontrol approach can guarantee the uniform ultimate bounds of the tracking errors and bounds of NN weights. A complex nonlinear three-degree-of-freedom dynamic model of a passenger wagon is developed to simulate the vehicle motion and for controller design. The controller is tested and verified via computer simulations in the presence of parametric uncertainties and severe driving conditions

This paper proposes a wavelet adaptive backstepping control (WABC) system for a class of second-order nonlinear systems. The WABC comprises a neural backstepping controller and a robust controller. The neural backstepping controller containing a wavelet neural network (WNN) identifier is the principal controller, and the robust controller is designed to achieve L2 tracking performance with desired attenuation level. Since the WNN uses wavelet functions, its learning capability is superior to the conventional neural network for system identification. Moreover, the adaptation laws of the control system are derived in the sense of Lyapunov function and Barbalat's lemma, thus the system can be guaranteed to be asymptotically stable. The proposed WABC is applied to two nonlinear systems, a chaotic system and a wing-rock motion system to illustrate its effectiveness. Simulation results verify that the proposed WABC can achieve favorable tracking performance by incorporating of WNN identification, adaptive backstepping control, and L2 robust control techniques.

This paper proposes an adaptive fuzzy controller (AFC) with a proportional-integral (PI) learning algorithm for an induction servomotor. The proposed AFC is comprised of a fuzzy controller and a robust controller. The fuzzy controller is to mimic an ideal controller and the robust controller is to dispel the effect of the approximation error between the fuzzy controller and the ideal controller. All the control parameters of the AFC are on-line tuned by a PI learning algorithm in the Lyapunov sense, thus the stability of the system can be guaranteed. Finally, a comparison between a fuzzy controller, an AFC with integral learning algorithm, and the proposed AFC with PI learning algorithm is presented. Simulation results verify that for the induction servomotor systems, the tracking performance of the AFC with PI learning algorithm is better than those of the fuzzy controller and the AFC with integral learning algorithm. Also, the convergence of the tracking error is speeded up

This paper proposes an adaptive fuzzy sliding-mode controller with proportional-integral learning algorithm (PI-AFSMC) for the unknown nonlinear systems. All the controller parameters are on-line tuned by the derived learning algorithms in the Lyapunov stability theorem, thus the stability of the system can be guaranteed. Finally, the proposed PI-AFSMC is applied to control a linear piezoelectric ceramic motor. Experimental results demonstrate the effectiveness of the proposed PI-AFSMC can achieve favorable tracking performances with unknown the controlled system dynamics

Type-1 fuzzy logic controllers (TIFLCs) have been successfully developed and used in various applications. The experience and knowledge of human experts are needed to decide both the membership functions and the fuzzy rules. However, in the real-time applications, uncertainty associated with the available information always happens. This paper proposes a type-2 fuzzy logic control (T2FLC), which involves the fuzzifier, rule base, fuzzy inference engine, and output processor with type reduction and defuzzifier. Because the antecedent and/or consequent membership functions of the T2FLC are type-2 fuzzy sets, the T2FLC can handle rule uncertainties when the operation is extremely uncertain and/or the engineers cannot exactly determine the membership grades. Furthermore, the proposed T2FLC is applied to a buck DC-DC converter control. Experimental results show that the proposed T2FLC is robust against input voltage and load resistance variations for the converter control

This paper proposes a wavelet-neural-network-based backstepping control (WNNBC) for the chaotic systems. The WNNBC is comprised of a neural backstepping controller and an adaptive robust controller. The neural backstepping controller containing a wavelet neural network identifier is the principal controller, and the adaptive robust controller is designed to achieve L/sub 2/ tracking performance with desired attenuation level. Finally, simulation results verify that the proposed WNNBC can achieve favorable tracking performance.

Support vector regression (SVR) based on statistical learning is a useful tool for nonlinear regression problems. The SVR method deals with data in a high dimension space by using linear quadratic programming techniques. As a consequence, the regression result has optimal properties. However, if parameters were not properly selected, overfitting and/or underfilling phenomena might occur in SVR. Two parameters /spl sigma/, the width of Gaussian kernels and /spl epsi/, the tolerance zone in the cost function are considered in this research. We adopted the concept of the sampling theory into Gaussian filter to deal with parameter /spl sigma/. The idea is to analyze the frequency spectrum of training data and to select a cut-off frequency by including 90% of power in spectrum. The corresponding /spl sigma/ can then be obtained through the sampling theory. In our simulations, it can be found that good performances are observed when the selected frequency is near the cut-off frequency. For another parameter /spl epsi/, it is a tradeoff between the number of support vectors and the RMSE. By introducing the confidence interval concept, a suitable selection of /spl epsi/ can be obtained. The idea is to use the L/sub 1/-norm (i.e., when /spl epsi/ = 0 ) to estimate the noise distribution of training data. When /spl epsi/ is obtained by selecting the 90% confidence interval, simulations demonstrated superior performance in our illustrative example. By our systematical design, proper values of /spl sigma/ and /spl epsi/ can be obtained and the resultant system performances are nice in all aspects.

This paper studies an intelligent system, including a support vector regression (SVR) and a similar analysis, for the classification of the ovarian cancer with microarray data. That is, steps in the classification include a feature selection step and a distance measure step. Firstly, the SVR is used to do the feature selection. That is, the SVR is applied to obtain the important genes of ovarian cancer for all samples of microarray data. At the same time, we can compute the frequency of the gene selection based on the results of SVR for all samples to determine the target genes of ovarian cancer. Secondly, the distance under the similar analysis between target data and test data can be determined. From the distance results, the classification of ovarian cancer can easy to determine

A neural network (NN) adaptive tracking controller for rigid revolute robots is presented that requires position measurements only. The controller is synthesized using a computed torque like control part of which a modified version of the nonlinear part of Lagrangian dynamics is learnt online by a neural estimator that needs no offline training phase. Therefore, the implementation of the control algorithm needs a reasonable knowledge of the inertia matrix alone. The combined neurocontroller-linear observer scheme can guarantee the uniform ultimate bounds (UUB) of the tracking errors and the observer errors under fairly general conditions on the controller-observer gains.

In this paper, a fuzzy model based time-optimal controller is proposed. The control input is bounded, and only achieved in minimum time. A design approach of the minimal time fuzzy controller using Pontryagin's Minimum Principle (PMP) is presented. The necessary conditions of PMP for time-optimal problem achieve the goal in minimum time under bounded controls can be formulated as bang-bang control. Determination of time-optimal solution by solving the corresponding two-point boundary-value problems (TPBVP) is, in general, a difficult problem. The proposed approach does not require to solve TPBVP. The Euler's method can be adopted to obtain approximate solution for T-S fuzzy model. Based on this method, the separating set of each subsystem in T-S fuzzy model is analytical and can cope with firing strength to generate switching conditions.

In this paper, a novel online B-spline membership function (BMF) fuzzy-neural sliding mode controller trained by an adaptive bound reduced-form genetic algorithm (ABRGA) is developed to guarantee robust stability and tracking performance for robot manipulators with uncertainties and external disturbances. The general sliding manifold is used to construct the sliding surface and reduce the chattering of the control signal, which can be nonlinear or time varying. For the purpose of identification, the proposed BMF fuzzy-neural network trained by the ABRGA approximates the regressor of the manipulator. An adaptive bound algorithm is used to aid and speed up the searching speed of the RGA. Simulation results show that the proposed on-line ABRGA-based BMF fuzzy-neural sliding mode controller is effective and yields superior tracking performance for robot manipulators.

The robust stability analysis of a neural control based vehicle steering system is considered in this paper. The neural controller can be linearized by the use of describing function first. Since the perturbed parameters including velocity and friction are existed in the vehicle steering system, the stability of equivalent linearized system is then analyzed by using the parameter plane method. According to this procedure, the amplitude and frequency of limit cycles caused by the neural controller can be figured out clearly in the parameter plane. Moreover, the limit cycles may be suppressed by adjusting the control factors carefully. Computer simulation shows the efficiency of this approach.

This paper proposes a new automotive driving control for vehicle to track the specified multi-objectives. By considering the nonlinear and coupling vehicle dynamics and relative motions with respect to the road, the corresponding T-S fuzzy model of vehicle can be obtained with a suitable input decoupled technique. The controller based on the optimal fuzzy tracking design is then developed and it pushes the outputs of vehicle systems close to any desired targets without excessive control-energy consumption. The desired speed and lateral offset in double lane-change maneuver are given as tracking targets in this work, and simulation results show that the effectiveness of proposed tracking scheme.

This paper addresses the problem of designing robust static output-feedback controllers for nonlinear discrete-time interval systems with time delays both in states and in control input. In the approach, we do not directly employ the Lyapunov approach, as do in most of traditional fuzzy control design approaches. Instead, sufficient conditions for guaranteeing the robust stability for the considered systems are derived in terms of the matrix spectral norm of the closed-loop fuzzy system. The stability conditions are further formulated into linear matrix inequalities so that the desired controller can be easily obtained by using the Matlab linear matrix inequality (LMI) toolbox. Finally, an example is provided to illustrate the effectiveness of the proposed approach.

There are two types of fuzzy modeling: 1) imitating an expert experiment or fulfilling an engineering knowledge, and 2) modeling a complex or unknown system. In this paper, based on the first type of fuzzy modeling, a new fuzzy suction controller (NFSC) is proposed using its linguistic rules to design nonlinear boundary layer. Two kinds of nonlinear boundary layers are discussed. The first kind is designed by three rules derived according to a new interpretation of the switching conditions for a suction controller such that the new controller reduces chattering and spends less energy than a suction controller does. A design procedure summarizes the NFSC design. The second kind of nonlinear boundary layer is the linguistic rules designed to have sliding sectors to control a mobile robot for trajectory tracking. The discussion emphasizes the advantage of nonlinear boundary layers, compared with traditional suction controllers usually using linear boundary. In addition, the proposed NFSC provides a flexible way to adjust the controller functions using linguistic rules based on the first type of fuzzy modeling

In this paper, an observer-based direct adaptive fuzzy-neural control scheme is presented for nonaffine nonlinear systems in the presence of unknown structure of nonlinearities. A direct adaptive fuzzy-neural controller and a class of generalized nonlinear systems, which are called nonaffine nonlinear systems, are instead of the indirect one and affine nonlinear systems given by Leu et al. By using implicit function theorem and Taylor series expansion, the observer-based control law and the weight update law of the fuzzy-neural controller are derived for the nonaffine nonlinear systems. Based on strictly-positive-real (SPR) Lyapunov theory, the stability of the closed-loop system can be verified. Moreover, the overall adaptive scheme guarantees that all signals involved are bounded and the output of the closed-loop system will asymptotically track the desired output trajectory. To demonstrate the effectiveness of the proposed method, simulation results are illustrated in this paper.

The problem of optimal simultaneous regional pole placement for a collection of linear time-invariant systems via a single static output feedback controller is considered. The cost function to be minimized is a weighted sum of quadratic performance indices of the systems. The constrained region for each system can be the intersection of several open half-planes and/or open disks. This problem cannot be solved by the linear matrix inequality (LMI) approach since it is a nonconvex optimization problem. Based on the barrier method, we instead solve an auxiliary minimization problem to obtain an approximate solution to the original constrained optimization problem. Moreover, solution algorithms are provided for finding the optimal solution. Furthermore, a necessary and sufficient condition for the existence of admissible solutions to the simultaneous regional pole placement problem is derived. Finally, two examples are given for illustration.

The computed torque or inverse dynamics control techniques are based on a good understanding of the system dynamics and even its environment. For the real-time applications, the system dynamics is always difficult to obtain. To tackle this drawback, the goal of this paper is to develop a model-free control method which is referred to as the robust neuro-fuzzy sliding-mode control (RNFSMC) system. The proposed RNFSMC system is comprised of a fuzzy controller and a robust controller. The fuzzy controller is utilized to approximate an ideal controller by the developed tuning algorithms, and the robust controller is designed to achieve H/sup /spl infin// tracking performance index. To investigate the effectiveness of the proposed RNFSMC system, it is applied to control a Chua's chaotic circuit system. Finally, simulation results demonstrate that the effect of the fuzzy approximation error on the tracking error can be attenuated efficiently by the proposed method without any knowledge of the controlled systems.

The paper proposes a way of designing state feedback controllers for affine Takagi-Sugeno-Kang (TSK) fuzzy models. In the approach, by combining two different control design methodologies, the proposed controller is designed to compensate all rules so that the desired control performance can appear in the overall system. Our approach treats all fuzzy rules as variations of a nominal rule and such variations are individually dealt with in a Lyapunov sense. Previous approaches have proposed a similar idea but the variations are dealt with as a whole in a robust control sense. As a consequence, when fuzzy rules are distributed in a wide range, the stability conditions may not be satisfied. In addition, the control performance of the closed-loop system cannot be anticipated in those approaches. Various examples were conducted in our study to demonstrate the effectiveness of the proposed control design approach. All results illustrate good control performances as desired.

In this paper, we apply some effective methods, including the gain-phase margin tester, describing function and parameter plane, to predict the limit cycles of dynamic fuzzy control systems with adjustable parameters. Both continuous-time and sampled-data fuzzy control systems are considered. In general, fuzzy control systems are nonlinear. By use of the classical method of describing functions, the dynamic fuzzy controller may be linearized first. According to the stability equations and parameter plane methods, the stability of the equivalent linearized system with adjustable parameters is then analyzed. In addition, a simple approach is also proposed to determine the gain margin and phase margin which limit cycles can occur for robustness. Two examples of continuous-time fuzzy control systems with and without nonlinearity are presented to demonstrate the design procedure. Finally, this approach is also extended to a sampled-data fuzzy control system.

Type-2 fuzzy logic system (FLS) cascaded with neural network, type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of a type-2 fuzzy linguistic process as the antecedent part, and the two-layer interval neural network as the consequent part. A general T2FNN is computational-intensive due to the complexity of type 2 to type 1 reduction. Therefore, the interval T2FNN is adopted in this paper to simplify the computational process. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process (back propagation). It can also be shown both learning rates cannot be both negative. Further, due to variation of the initial MF parameters, i.e., the spread level of uncertain means or deviations of interval Gaussian MFs, the performance of back propagation training process may be affected. To achieve better total performance, a genetic algorithm (GA) is designed to search optimal spread rate for uncertain means and optimal learning for the antecedent part. Several examples are fully illustrated. Excellent results are obtained for the truck backing-up control and the identification of nonlinear system, which yield more improved performance than those using type-1 FNN.

Fuzzy sets theory is introduced and applied to the problems of signal restoration. A fuzzy approach for system identification and state estimation is developed. The estimated values obtained from the fuzzy model are in the form of fuzzy numbers which present the possibility of the system structure. Both L-R and symmetric triangular type membership functions are employed to derive the fuzzy-data based estimator (FBE). Illustrative examples with white and color noise are provided to demonstrate the applicability and effectiveness of the developed FBE. Comparisons between the FBE and various modified Kalman filter are also included.

In this part, a New Fuzzy Suction Controller (NFSC), based on the first type of fuzzy modeling, is proposed using its linguistic rules to design a controller like a suction controller. According to new interpreting switching conditions of a suction controller, three rules are derived to design the linguistic rules of a NFSC such that it reduces chattering and expends less energy than a suction controller does. A design procedure summarizes the NFSC design. In addition, the linguistic rules are designed to have sliding sectors to control a mobile robot for tracking FPE gradient directions with obstacle avoidance. It's the demonstration of motion control in robot navigation using the proposed FPE discussed in Part I of this paper. The simulation includes the robot starting outside or inside a U-shaped obstacle. The proposed NFSC provides a flexible way for adjusting the controller functions using linguistic rules based on the first type of fuzzy modeling.

This two-part series discusses a novel design and application of fuzzy modeling. There are two types of fuzzy modeling: (1) irritating expert experience or implementing engineering knowledge and (2) modeling a complex or unknown system. In Part I of this paper, the second type of fuzzy modeling is employed to define Fuzzy Potential Energy (FPE). Different FPE values are then assigned into a workspace to construct a spatial model for robot navigation. How the FPE guides a robot is revealed by its gradient directions. A workspace that contains a U-shaped obstacle is used as an example to illustrate the FPE applications. Two situations, one with the start position outside the U-shaped obstacle and the other with the start position inside, are discussed. The discussions show that FPE provides a way to merge both global and local path-planning strategies. The proposed FPE concretely improves traditional path planning strategy. This two-part series pioneers a novel design and application of fuzzy modeling for path planing and motion control.

This paper is focused on the optimal control problem of the nonlinear unknown systems with multiobjectives. The concept of system output response function based upon system input sequence over time is proposed, and the problem is formulated accordingly. Considering the fact that a finite response curve set is easy to be obtained, we employ the Pareto rule-base and the approximate Pareto control algorithm for multiobjective control optimization based on the finite response curve set. In this way, an easy method to find a Pareto rule-base for the complicated multiobjective optimal control problem that converts the problem to the one that can be resolved only in a finite set consisting of input-output date and curves over time is presented. It can guarantee that every rule's input and output base point is optimally matched in Pareto sense within the known set of input and output of the system. Moreover, some sufficient conditions are obtained for the conventional fuzzy control algorithm to be a Pareto one. It is shown that if the rule-base is composed of Pareto rules, then for any inputs between two rule base-point, the corresponding output of the algorithm is also bounded by the two corresponding out base-points of the two rules. From the view of approximation, the Pareto algorithm can guarantee the system response is of Pareto performance relative to the objectives. As an illustration, the theory is applied to Monotone Inertial System. Simulation results agree with theories presented in this paper, and show that the fuzzy controller based on the Pareto rule-base presents very good behaviors in adaptivity, robustness and tracking with time-varying setpoint.

In this paper, a hybrid adaptive fuzzy control (HAFC) system is developed for a wing rock motion system. The design of HAFC system contains three parts: one is an indirect controller, the other is a direct controller and the last is a robust controller. A weighting factor /spl alpha/, which can be adjusted by a tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from the indirect and direct controllers. The robust controller is designed to achieve favorable control performance with a desired robustness. Simulation results demonstrate that the HAFC system can achieve favorable desired tracking performances for unknown the wing rock motion dynamics.

The main purpose of this paper is to predict the limit cycles of neurocontrol system with perturbed parameters by combining the approaches of stability equation, describing function and parameter plane. The neurocontroller is first linearized by using the describing function method. The stability of equivalent linearized system is then analyzed by using stability equations and the parameter plane method. According to this procedure, the amplitude and frequency of limit cycles can be figured out clearly in the parameter plane. Moreover, the limit cycle may be suppressed by adjusting the control parameters carefully. Finally, a vehicle model is illustrated to demonstrate its validity.

This study proposes a missing data recovery method based on grey-relational nearest-neighbor substitution techniques for treating with missing data from dual-loop detectors in estimating travel time and evaluates the effects of the missing data on travel-time estimation performance. Field data from the Taiwan national freeway no.1 were used as a case study for testing the proposed model. Study results shown that the travel time estimation with missing data rate up to 33%. It is indicated that the proposed missing data treatment model can ensure the accuracy of travel time estimation with incomplete data sets.

The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this study, a robust hybrid control system is developed for the antilock braking systems. In this design method, the robust hybrid control system is comprised of a hybrid controller and a robust controller to achieve a desired H/sup /spl infin// control performance. The hybrid controller containing a fuzzy uncertainty observer is the principal controller, and the robust controller is designed to achieve the desired H/sup /spl infin// tracking performance. Finally, simulations are performed to demonstrate the effectiveness of the proposed robust hybrid control system for antilock braking control under various road conditions.

The consistency of the rule-base is an essential basis for the rule-based intelligent information processing. In the framework of first-order lattice-valued logic system LF(X) which is in the attempt of handling fuzziness and incomparability, the present work focus on how to verify and increase the consistency degree of the rule-base in the intelligent information process system for handling fuzziness and incomparability. An /spl alpha/-automated simplification algorithm in LF(X) is proposed, the /spl alpha/-consistency and the /spl alpha/-simplification theory of the rule-base are formulated. As such, the verification of the /spl alpha/-consistency of the rule-base, often an infinity problem which is difficult to be achieved, can be transformed into a finite and achievable /spl alpha/-simplification stepwise search algorithm for verifying the consistency of the rule-base as well as a kind of filtering algorithm for increasing the consistency degree of the rule-base are proposed.

In this paper, we extend some effective methods, including the gain-phase margin tester method, the describing function method and the parameter plane method, to predict the limit cycles of fuzzy control systems. In general, fuzzy control systems are nonlinear. By use of the classical method of describing functions, a fuzzy controller may be linearized first. The stability of the equipment linearization system with adjustable parameters is then analyzed due to the stability equations and parameter plane methods. For the robust design, a novel way is proposed to determine the gain margin and phase margin, which limit cycles, can occur. Two examples are offered to illustrate the design procedure. First, the fuzzy control system with a third-order linear plant is considered. In addition, this approach is also extended to the fuzzy control system with nonlinearity. The results of computer simulation can verify its validity.

Type-2 fuzzy logic system (FLS) cascaded with neural network, called type-2 fuzzy neural network (T2FNN), is presented in this paper to handle uncertainty with dynamical optimal learning. A T2FNN consists of type-2 fuzzy linguistic process as the antecedent part and the two-layer interval neural networks as the consequent part. The dynamical optimal training algorithm for the two-layer consequent part of interval T2FNN is first developed. The stable and optimal left and right learning rates for the interval neural network, in the sense of maximum error reduction, can be derived for each iteration in the training process. It can is also shown that both learning rates cannot be both negative. Excellent results are obtained for the truck backing-up control, which yield more improved performance than those using type-1 FNN.

A new Takagi-Sugeno (TS)-type FNN learning architecture is proposed for the on-line identification of the TS-type fuzzy model of the uncertain system. The dynamical optimal learning rule is adopted to update the linearized TS-type fuzzy model to guarantee the convergence of the on-line training process. To improve the convergence speed of the on-line training process, the least-squared identification is applied to identify the initial parameters of the TS-type fuzzy model. Once the linearized TS-type fuzzy model of the uncertain linear system is obtained in real-time environment, the on-line adaptive controller can be easily designed to accomplish the design specifications. A simplified tracking controller is also proposed to perform the tracking of a reference signal for unknown system. Critical constraint criteria are applied to find the computational time for generating the controller signal. Based on this sampling time, suitable equipments are used in actual hardware implementation. Inverted pendulum system is illustrated to track sinusoidal signal.

In this paper, a qualitative mode and a quantitative control model for unknown system are proposed. Based on the system output process trend according to control input sequence, a qualitative model- monotone inertial system (MIS, for short) is proposed, which can be used to describe most MIMO plants with no exactly mathematical model or unknown systems in practical engineering. In a MIS, the relation between input and output is monotonic and the output process is of time inertial. In a quantitative way, we suggest a new concept- the effective rule-base. After getting some qualitative properties of the MIS, a conclusion about the control stability is obtained by a way, which differs from Lyapunov method. Furthermore, a method to find an effective rule-base is presented for an unknown MIS based on finite knowledge. Simulation results on a challenging nonlinear magnetic bearing system support the conclusions of this paper.

A new design approach for an adaptive fuzzy sliding mode controller (AFSMC) for linear systems with mismatched time-varying uncertainties is presented. The coefficient matrix of the sliding function can be designed to satisfy a sliding coefficient matching condition provided time-varying uncertainties are bounded. With the sliding coefficient matching condition satisfied, an AFSMC is proposed to stabilize the uncertain system. The parameters of output fuzzy sets in the fuzzy mechanism are on-line adapted to improve the performance of the fuzzy sliding mode control system. The bounds of uncertainties are not required to be known in advance for the AFSMC. Stability of the fuzzy control system is guaranteed and the system is shown to be invariant on the sliding surface. Moreover, chattering around the sliding surface in sliding mode control can be reduced by the proposed design approach. Simulation results are included to illustrate the effectiveness of the proposed AFSMC.

An approach to adjust both control points of B-spline membership functions (BMFs) and weightings of fuzzy-neural networks using a simplified genetic algorithm (SGA) is proposed. The SGA is proposed by using a sequential-search-based crossover point (SSCP) method in which a better crossover point is determined and only the gene at the specified crossover point is crossed as a single point crossover operation. Chromosomes consisting of both the control points of BMFs and the weightings of fuzzy-neural networks are coded as an adjustable vector with real number components and searched by the SGA. Because of the use of the SGA, faster convergence of the evolution process to search for an optimal fuzzy-neural network can be achieved. Nonlinear functions approximated by using the fuzzy-neural networks via the SGA are demonstrated to illustrate the applicability of the proposed method.

A novel adaptive fuzzy-neural sliding-mode controller with H(infinity) tracking performance for uncertain nonlinear systems is proposed to attenuate the effects caused by unmodeled dynamics, disturbances and approximate errors. Because of the advantages of fuzzy-neural systems, which can uniformly approximate nonlinear continuous functions to arbitrary accuracy, adaptive fuzzy-neural control theory is then employed to derive the update laws for approximating the uncertain nonlinear functions of the dynamical system. Furthermore, the H(infinity) tracking design technique and the sliding-mode control method are incorporated into the adaptive fuzzy-neural control scheme so that the derived controller is robust with respect to unmodeled dynamics, disturbances and approximate errors. Compared with conventional methods, the proposed approach not only assures closed-loop stability, but also guarantees an H(infinity) tracking performance for the overall system based on a much relaxed assumption without prior knowledge on the upper bound of the lumped uncertainties. Simulation results have demonstrated that the effect of the lumped uncertainties on tracking error is efficiently attenuated, and chattering of the control input is significantly reduced by using the proposed approach.

A new hybrid direct/indirect adaptive fuzzy neural network (FNN) controller with a state observer and supervisory controller for a class of uncertain nonlinear dynamic systems is developed in this paper. The hybrid adaptive FNN controller, the free parameters of which can be tuned on-line by an observer-based output feedback control law and adaptive law, is a combination of direct and indirect adaptive FNN controllers. A weighting factor, which can be adjusted by the tradeoff between plant knowledge and control knowledge, is adopted to sum together the control efforts from indirect adaptive FNN controller and direct adaptive FNN controller. Furthermore, a supervisory controller is appended into the FNN controller to force the state to be within the constraint set. Therefore, if the FNN controller cannot maintain the stability, the supervisory controller starts working to guarantee stability. On the other hand, if the FNN controller works well, the supervisory controller will be deactivated. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded. Two nonlinear systems, namely, inverted pendulum system and Chua's (1989) chaotic circuit, are fully illustrated to track sinusoidal signals. The resulting hybrid direct/indirect FNN control systems show better performances, i.e., tracking error and control effort can be made smaller and it is more flexible during the design process.

In this paper, we develop an observer-based indirect adaptive fuzzy-neural controller with supervisory mode for a certain class of high order unknown nonlinear dynamical system. The free parameters of the adaptive fuzzy-neural controller with supervisory mode can be tuned on-line by an observer-based output feedback control law and adaptive law, based on the Lyapunov synthesis approach. The fuzzy controller is appended with a supervisory controller. If the fuzzy control system tends to unstable, the supervisory controller starts working to guarantee stability. From the energy point of view, this is a very economical design methodology. The overall adaptive scheme guarantees the global stability of the resulting closed-loop system in the sense that all signals involved are uniformly bounded.

Based on the H/sub /spl infin// control technique and the strictly positive real Lyapunov (SPR-Lyapunov) design approach, an adaptive fuzzy-neural observer tuned online for a class of uncertain (unknown) nonlinear systems is developed. Unlike the results of Marino et al. (1992, 1995), the assumption that the uncertain system nonlinearities only are restricted to the system output is not required. Moreover, the adaptive fuzzy-neural observer provides the modeling error (and the external bounded disturbance) attenuation with H/sub /spl infin// performance, obtained by a Riccati-like equation. Finally, simulation results demonstrate that the proposed observer yields satisfactory performance.

The authors propose a new neural structure with parallel and serial output via functional CPBUM neural network. Specifically, we combine the advantages in series to series and parallel to parallel to develop a parallel and serial output learning structure. Hence, the proposed model can reduce the computational complexity via the similarity analysis and sensitivity analysis. It is shown that the similarity analysis can be employed to determine the knowledge base of the controller. Furthermore, it is also shown that the sensitivity analysis can provide valuable information between input-output training pairs.

A Chebyshev polynomial-based unified model (CPBUM) neural network is introduced and applied to control a magnetic bearing systems. First, we show that the CPBUM neural network not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural network. It turns out that the CPBUM neural network is more suitable in the design of controller than the conventional feedforward/recurrent neural network. Second, we propose the inverse system method, based on the CPBUM neural networks, to control a magnetic bearing system. The proposed controller has two structures; namely, off-line and on-line learning structures. We derive a new learning algorithm for each proposed structure. The experimental results show that the proposed neural network architecture provides a greater flexibility and better performance in controlling magnetic bearing systems.

In this paper, an adaptive fuzzy controller for strict-feedback canonical nonlinear systems is proposed. The completely unknown nonlinearities and disturbances of the systems are considered. Since fuzzy logic systems can uniformly approximate nonlinear continuous functions to arbitrary accuracy, the adaptive fuzzy control theory is employed to derive the control law for the strict-feedback system with unknown nonlinear functions and disturbances. Moreover, H/sub /spl infin// tracking performance is applied to substantially attenuate the effect of the modeling errors and disturbances. Finally, examples are simulated to confirm the applicability of the proposed methods.

In this paper, we propose an approximate equivalence neural network model with a fast learning speed as well as a good function approximation capability, and a new objective function, which satisfies the H/sup /spl infin// induced norm to solve the worst-case identification and control of nonlinear problems. The approximate equivalence neural network not only has the same capability of universal approximator, but also has a faster learning speed than the conventional feedforward/recurrent neural networks. Based on this approximate transformable technique, the relationship between the single-layered neural network and multilayered perceptrons neural network is derived. It is shown that a approximate equivalence neural network can be represented as a functional link network that is based on Chebyshev polynomials. We also derive a new learning algorithm such that the infinity norm of the transfer function from the input to the output is under a prescribed level. It turns out that the approximate equivalence neural network can be extended to do the worst-case problem, in the identification and control of nonlinear problems.

A robust adaptive fuzzy-neural controller for a class of unknown nonlinear dynamic systems with external disturbances is proposed. The fuzzy-neural approximator is established to approximate an unknown nonlinear dynamic system in a linearized way. The fuzzy B-spline membership function (BMF) which possesses a fixed number of control points is developed for online tuning. The concept of tuning the adjustable vectors, which include membership functions and weighting factors, is described to derive the update laws of the robust adaptive fuzzy-neural controller. Furthermore, the effect of all the unmodeled dynamics, BMF modeling errors and external disturbances on the tracking error is attenuated by the error compensator which is also constructed by fuzzy-neural inference. We prove that the closed-loop system which is controlled by the robust adaptive fuzzy-neural controller is stable and the tracking error will converge to zero under mild assumptions. Several examples are simulated in order to confirm the effectiveness and applicability of the proposed methods.

We propose a neural gain scheduling network controller (NGSNC) to improve the gain scheduling controller for nonholonomic systems. We derive the neural networks that can approximate the gain scheduling controller arbitrarily well when the sampling frequency satisfies the sampling theorem. We also show that the NGSNC is independent of the sampling time. The proposed NGSNC has the following important properties: 1) same performance as the continuous-parameter gain scheduling controller; 2) less computing time than the continuous-parameter gain scheduling controller; 3) good robustness against the sampling intervals; and 4) straightforward stability analysis. We then show that some of nonholonomic systems can be converted to equivalent linear parameter-varying systems. As a result, the NGSNC can stabilize nonholonomic systems.

In this paper, an observer-based adaptive fuzzy-neural controller for a class of unknown nonlinear dynamical systems is developed. The observer-based output feedback control law and update law to tune on-line the weighting factors of the adaptive fuzzy-neural controller are derived. The total states of the nonlinear system are not assumed to be available for measurement. Also, the unknown nonlinearities of the nonlinear dynamical systems are not restricted to the system output only. The overall adaptive scheme guarantees that all signals involved are bounded. Simulation results demonstrate the applicability of the proposed method in order to achieve desired performance.

Shape memory alloy (SMA) is a device with lightweight and small volume. It is very ideal to use SMA as actuators for micro-robots. However, it is difficult to design a controller to handle the highly nonlinear properties of SMA. In this paper, a fuzzy walking pattern (FWP) is designed to control a small biped robot, using SMA as actuators. The FWP designed in this paper can not only generate the desired path for the biped robot, but also handle the exceptional case when the biped robot is subject to disturbance. Experimental results demonstrate the function of the FWP.

In this paper, we propose the approximate transformable technique, which includes the direct transformation and indirect transformation, to obtain a Chebyshev-Polynomials-Based (CPB) unified model neural networks for feedforward/recurrent neural networks via Chebyshev polynomials approximation. Based on this approximate transformable technique, we have derived the relationship between the single-layer neural networks and multilayer perceptron neural networks. It is shown that the CPB unified model neural networks can be represented as a functional link networks that are based on Chebyshev polynomials, and those networks use the recursive least square method with forgetting factor as learning algorithm. It turns out that the CPB unified model neural networks not only has the same capability of universal approximator, but also has faster learning speed than conventional feedforward/recurrent neural networks. Furthermore, we have also derived the condition such that the unified model generating by Chebyshev polynomials is optimal in the sense of error least square approximation in the single variable ease. Computer simulations show that the proposed method does have the capability of universal approximator in some functional approximation with considerable reduction in learning time.

The adaptive fuzzy-neural controllers tuned online for a class of unknown nonlinear dynamical systems are proposed. To approximate the unknown nonlinear dynamical systems, the fuzzy-neural approximator is established. Furthermore, the control law and update law to tune on-line both the B-spline membership functions and the weighting factors of the adaptive fuzzy-neural controller are derived. Therefore, the control performance of the controller is improved. Several examples are simulated in order to confirm the effectiveness and applicability of the proposed methods in this paper.

The paper describes a novel application of the B-spline membership functions (BMF's) and the fuzzy neural network to the function approximation with outliers in training data. According to the robust objective function, we use gradient descent method to derive the new learning rules of the weighting values and BMF's of the fuzzy neural network for robust function approximation. In this paper, the robust learning algorithm is derived. During the learning process, the robust objective function comes into effect and the approximated function will gradually be unaffected by the erroneous training data. As a result, the robust function approximation can rapidly converge to the desired tolerable error scope. In other words, the learning iterations will decrease greatly. We realize the function approximation not only in one dimension (curves), but also in two dimension (surfaces). Several examples are simulated in order to confirm the efficiency and feasibility of the proposed approach in this paper.

A general methodology for constructing fuzzy membership functions via B-spline curves is proposed. By using the method of least-squares, the authors translate the empirical data into the form of the control points of B-spline curves to construct fuzzy membership functions. This unified form of fuzzy membership functions is called a B-spline membership function (BMF). By using the local control property of a B-spline curve, the BMFs can be tuned locally during the learning process. For the control of a model car through fuzzy-neural networks, it is shown that the local tuning of BMFs can indeed reduce the number of iterations tremendously. This fuzzy-neural control of a model car is presented to illustrate the performance and applicability of the proposed method.>

The authors propose a fuzzy logic controller (FLC) that uses a VSS to form the linguistic rules. First, they prove that there exists a sliding line in the FLC. Secondly, a Lyapunov function is selected to show that the fuzzy controller system (FCS) is globally asymptotical stable. The proposed control method is applied to stabilize a cart-pole system. The effect of adjusting the scaling factor, varying the rules of the VSS, and modifying the membership function on the FCS are also addressed.

To achieve online Cartesian space trajectory planning, the authors present methods for approximating Cartesian paths by recursive joint trajectories. Systematic procedures are proposed for approximating an arbitrary smooth Cartesian path by concatenated line segments and for selecting intermediate knot points along the line segments. Two recursive joint trajectory planning algorithms are derived using quartic spline interpolation. The proposed approach is illustrated by a numerical example.>

The synthesis and verification of biped walking trajectories during the single-support phase is discussed. The biped is modeled as a seven-link twelve degree-of-freedom anthropomorphic robot. By utilizing the zero moment point (ZMP) and the equivalent force-moment, the physical admissibility of the biped walking is characterized. For physically realizable walking during the single-support phase, the trajectory of the ZMP on the ground must be inside the stable region of the supporting sole. A biped walking trajectory specified by eight pattern parameters is derived through inverse kinematics. ZMP trajectories for different walking patterns and the influence of the pattern parameters on the ZMP trajectories are presented.>

A method is presented for path planning of a point robot moving among polyhedral obstacles with piecewise constant translating velocity. Representing the obstacles as polyhedra in the space-time domain allows a collision-free, minimum-time path to be obtained using graph search and linear programming. The planner views the space-time configuration of free space as disjoint polytopes where each point in the space-time domain is mapped into a unique polytope. The planner searches connected polytopes between the start and goal polytopes that satisfy the speed and time constraints. A near-optimal trajectory is determined by constrained optimization. In the proposed approach, the obstacle is allowed to move faster than the point robot, and the methodology can be easily extended to motion planning in higher-dimensional spaces.>

A unified approach suitable for path planning with moving polyhedral obstacles is presented. The planner views the space-time configuration of free space as disjoint polytopes that represent a time-dependent environment consisting of moving and stationary objects. Each point in the space-time domain is mapped into a unique polytope set. The planner then constructs a family of feasible collision-free trajectories by searching connected polytopes between the start polytope and the goal polytope that satisfy the speed and time constraints. Finally, a near-optimal trajectory is determined by constrained optimization. This approach does not require that obstacles be nonoverlapping or noncolliding. In addition, the obstacle is allowed to move faster than the planned robot. However, the speed of the obstacle must be piecewise-constant. The proposed approach can be easily extended to motion planning in higher dimensional spaces.>

Trajectory planning methods and control algorithms for a three-link biped robot are proposed. Based on the observation of the motions of human beings, trajectory planning methods are proposed without neglecting the nonlinear terms of the biped dynamics. A combination of several well-developed control methods is used to control the biped. Comparisons are made of the path tracking performance and torque consumption of these control algorithms.>

Hexapod tripod gaits for straight-line motion and crab walking are derived. Mathematical relations that express the stability margin, the stride length, and the duty factor are formulated for straight-line motion and for crab walking, respectively. The derived results provide tripod gaits of the hexapond for walking with a prescribed stability margin either over perfect terrain or constant slope terrain.>

The discrete shift-transformation matrix of general orthogonal polynomials is introduced. The discrete shift-transformation matrix is employed to transform the difference equations, which describe the discrete dynamic robot model, into algebraic equations. Several lemmas are introduced which, together with the discrete shift-transformation matrix, solve for the joint positions and velocities of discrete dynamic robot models via discrete orthogonal polynomials approximations. The initial numerical experiment with a cylindrical coordinate robot shows the feasibility and applicability of discrete orthogonal polynomials approximations.

Some of the fundamental problems of the gait control of a quadruped walking vehicle are addressed. It is shown that the product of the duty factor and the stride length is equal to the length of the boundary of the reachable area of the leg. Furthermore, the mathematical expression representing the relationship between the stability margin, the stride length and duty factor are also formulated. These equations are expressed in terms of quantities that specify the configuration of the quadruped walking vehicle. Based on the derived results, a graphical approach to determine the required stride length and the duty factor that corresponding to a regular gait with a prescribed static stability margin is presented. This graphical approach is then adopted to determine the regular gait of a quadruped walking vehicle, and the results agree with the analytic approach.