We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving. In this article, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.

This paper concerns delay-dependent guaranteed cost control (GCC) problem for a class of linear state-delayed systems with norm-bounded time-varying parametric uncertainties. By incorporating the free weighing matrix approach developed recently, new delay-dependent conditions for the existence of the guaranteed cost controller are presented in terms of matrix inequalities for both nominal state-delayed systems and uncertain state-delayed systems. An algorithm involving convex optimization is proposed to design a controller achieving a suboptimal guaranteed cost such that the system can be stabilized for all admissible uncertainties. Through numerical examples, it is shown that the proposed method can yield less guaranteed cost than the existing delay-dependent methods.

In this paper, a wind turbine with power splitting transmission, which is realized through a novel three-shaft planetary, is presented. The input shaft of the transmission is driven by the rotor of the wind turbine, the output shaft is connected to the grid via the main generator (asynchronous generator), and the third shaft is driven by a control motor with variable speed. The dynamic models of the sub systems of this wind turbine, e.g. the rotor aerodynamics, the drive train dynamics and the power generation unit dynamics, were given and linearized at an operating point. These sub models were integrated in a multidisciplinary dynamic model, which is suitable for control syntheses to optimize the utilization of wind energy and to reduce the excessive dynamic loads. The important dynamic behaviours were investigated and a wind turbine with a soft main shaft was recommend.

A new distance measurement method with the use of a single camera and a rotating mirror is presented. A camera in front of a rotating mirror acquires a sequence of reflected images, from which distance information is extracted. The distance measurement is based on the idea that the corresponding pixel of an object point at a longer distance moves at a higher speed in a sequence of images in this type of system setting. Distance measurement based on such pixel movement is investigated. Like many other image-based techniques, this presented technique requires matching corresponding points in two images. To alleviate such difficulty, two kinds of techniques of image tracking through the sequence of images and the utilization of multiple sets of image frames are described. Precision improvement is possible and is one attractive merit. The presented approach with a rotating mirror is especially suitable for such multiple measurements. The imprecision caused by the physical limit could be improved through making several measurements and taking an average. In this paper, mathematics necessary for implementing the technique is derived and presented. Also, the error sensitivities of related parameters are analyzed. Experimental results using the real camera-mirror setup are reported.

In this paper, we propose a wavelet based fuzzy neural network (WFNN) based direct adaptive control scheme for the solution of the tracking problem of mobile robots. To design a controller, we present a WFNN structure that merges the advantages of the neural network, fuzzy model and wavelet transform. The basic idea of our WFNN structure is to realize the process of fuzzy reasoning of the wavelet fuzzy system by the structure of a neural network and to make the parameters of fuzzy reasoning be expressed by the connection weights of a neural network. In our control system, the control signals are directly obtained to minimize the difference between the reference track and the pose of a mobile robot via the gradient descent (GD) method. In addition, an approach that uses adaptive learning rates for training of the WFNN controller is driven via a Lyapunov stability analysis to guarantee fast convergence, that is, learning rates are adaptively determined to rapidly minimize the state errors of a mobile robot. Finally, to evaluate the performance of the proposed direct adaptive control system using the WFNN controller, we compare the control results of the WFNN controller with those of the FNN, the WNN and the WFM controllers.

This paper provides a basis for investigating 'The Origin of Artificial Species,' as a robot can be considered as an artificial creature. To design an artificial creature, its general internal architecture is presented and its artificial chromosomes are proposed as its essential components. Rity as an artificial creature is developed in a virtual world of PC to test the world's first robotic 'chromosomes,' which are a set of computerized DNA (Deoxyribonucleic acid) codes for creating robots (artificial creatures) that can have their own personality, and can ultimately reproduce their kind, or even evolve as a distinct species. The effectiveness of the artificial chromosomes is demonstrated by implanting the genetic code into two Ritys living in a virtual world, in order to define their personality.

In this paper, we proposed an algorithm for arrhythmia classification, which is associated with the reduction of feature dimensions by linear discriminant analysis (LDA) and a support vector machine (SVM) based classifier. Seventeen original input features were extracted from preprocessed signals by wavelet transform, and attempts were then made to reduce these to 4 features, the linear combination of original features, by LDA. The performance of the SVM classifier with reduced features by LDA showed higher than with that by principal component analysis (PCA) and even with original features. For a cross-validation procedure, this SVM classifier was compared with Multilayer Perceptrons (MLP) and Fuzzy Inference System (FIS) classifiers. When all classifiers used the same reduced features, the overall performance of the SVM classifier was comprehensively superior to all others. Especially, the accuracy of discrimination of normal sinus rhythm (NSR), arterial premature contraction (APC), supraventricular tachycardia (SVT), premature ventricular contraction (PVC), ventricular tachycardia (VT) and ventricular fibrillation (VF) were $99.307\%,\;99.274\%,\;99.854\%,\;98.344\%,\;99.441\%\;and\;99.883\%$, respectively. And, even with smaller learning data, the SVM classifier offered better performance than the MLP classifier.

In this paper, we propose a new stability criterion and a controller design method for switched linear systems. The proposed stability criterion is applicable to each subsystem independently without the need to consider the overall system. The controller can be easily designed through geometric relations between eigenvalues of each subsystem matrix. The proposed methods provide a systematic and simple pole assignment approach for switched linear systems. Illustrative examples are given.

In this paper, we discuss real-time control of networked control systems (NCSs) and practical issues in the choice of the communication networks for this purpose. An appropriate integration of control systems, real-time environments, and network communication systems allows the optimization of the quality-of-control (QoC) in NCSs. We compare several prevailing network types that may be used in control applications to offer a guideline of choosing a proper network. A real-time operating environment is also presented as an important ingredient of NCS design. To evaluate its feasibility and effectiveness, a real-time NCS containing a ball magnetic levitation (Maglev) setup is implemented via an Ethernet. Based on the experimental results, it is concluded in this paper that real-time control via Ethernet is a practical and feasible solution to NCS design.

In this paper, an active vibration control of a tensioned, elastic, axially moving string is investigated. The dynamics of the translating string are described with a non-linear partial differential equation coupled with an ordinary differential equation. A right boundary control to suppress the transverse vibrations of the translating continuum is proposed. The control law is derived via the Lyapunov second method. The exponential stability of the closed-loop system is verified. The effectiveness of the proposed control law is simulated.

A robust adaptive speed sensorless induction motor direct torque control (DTC) using a neural network (NN) is presented in this paper. The inherent lumped uncertainties of the induction motor DTC system such as parametric uncertainty, external load disturbance and unmodeled dynamics are approximated by the NN. An additional robust control term is introduced to compensate for the reconstruction error. A control law and adaptive laws for the weights in the NN, as well as the bounding constant of the lumped uncertainties are established so that the whole closed-loop system is stable in the sense of Lyapunov. The effect of the speed estimation error is analyzed, and the stability proof of the control system is also proved. Experimental results as well as computer simulations are presented to show the validity and efficiency of the proposed system.

This paper presents a digital controller for a single phase UPS inverter under two main considerations: (i) the overall system shall keep very low AC-voltage tracking error as well as no phase delay over different load conditions, and (ii) the digital controller shall be employed at a fixed sampling time. We propose that the former can be achieved by the proposed controller using the error-state approach and the latter can be dealt with by the socalled characteristics ration assignment.

In this paper, we propose a constructive realization procedure for 2D systems which may lead to a Fornasini-Marchesini local state-space model with much lower order than the existing realization procedure given by Bisiacco et al. Nontrivial examples are illustrated and the conditions for minimal realization are also discussed.

Closed-loop model-free optimization of positive feedback time delay controllers for dominant time delay systems is presented. Iterative feedback tuning (IFT) is applied to the tuning of positive feedback time delay controller. Three experiments are carried out to perform the model-free gradient descent optimization. The initial controller parameters and duration in specifying the cost function are suggested. The effects of step size, filter function and time weighting function on the performance of the optimized controlled are given. Simulation and experimental studies are included to demonstrate the effectiveness of the tuning scheme.

A recently published paper demonstrates the utility of allowing delays in state estimators for linear systems with unknown inputs. In this note, we point out an error in the derivation of the estimator.