The battery management system (BMS) is the cornerstone of the safe operation of electric vehicles (EVs), and its stable operation relies heavily on the accuracy of the battery current sensor. In this paper, a novel method for current sensor fault diagnosis in lithium-ion batteries is presented. First, a fractional-order (FO) battery model is proposed, and a hybrid particle swarm optimization algorithm with a cross-learning strategy is developed for model parameter identification. Second, a FO observer is designed for state of charge (SOC) estimation, and the stability of the observation error system is verified. Finally, by accurately estimating the change in SOC caused by a fault current and employing residual analysis, current sensor faults are identified. Experimental analysis, carried out under different operating conditions, reveals that the proposed method detects current sensor faults more accurately and quickly than the general open-circuit voltage method. Moreover, it can adapt to varying operating conditions and temperatures.

The aim of this article is to propose an observer-based event-triggered Robin boundary control strategy for the exponential stabilization of the coupled semilinear reaction-diffusion neural networks with spatially varying coefficients. Toward this aim, we design an observer to estimate the value of system states by using some of these system values as the available measurement. An observer-based event-triggered boundary stabilizer is then presented to exponentially stabilize the considered systems with the Zeno behavior being excluded. Throughout this article, the main used method is backstepping, which yields an explicit expression of the control formulae. Moreover, we see that the proposed event-triggered boundary control scheme can ensure the desired level of control performance with fewer control law updates. A numerical example is finally given to illustrate the effectiveness of our proposed method.

In this article, the projective synchronization of uncertain fractional-order (FO) reaction-diffusion systems is studied for the first time via the fractional adaptive sliding mode control (SMC) method. A FO integral type switching function is designed, and corresponding adaptive SMC laws are derived which ensure the FO sliding mode surface (SMS) is reachable after a finite time interval. The improved version of these control laws which have smaller oscillation and better control performance are also derived. A new lemma for proving the finite-time reachability of the FO SMS is developed. At last, numerical examples are provided to verify the effectiveness of our theories.

This article studies the leader-follower weighted consensus control for a class of one-sided Lipschitz (OSL) nonlinear fractional-order (FO) multiagent systems (FOMASs) with norm-bounded time-varying parametric uncertainty and time delay. First, it addresses a more complex nonlinear form, known as the OSL nonlinearity condition, which has recently been frequently used in the control field. Second, the weighted consensus protocol is achieved using the current and time delay state information of the FOMASs, which guarantees accurate control and fast convergence speed. Third, new low-conservative sufficient conditions for asymptotic convergence and stability are obtained, using the linear matrix inequality (LMI) approach and the FO Razumikhin theorem. Further, order- and delay-dependent consensus protocol for the leader-follower OSL FOMASs is achieved, with design parameters being easily computed by solving LMI constraints. Finally, three simulation examples are studied to demonstrate the effectiveness and feasibility of the new control scheme. The new LMI-based condition is less conservative, is applicable to both Lipschitz nonlinear FOMASs and linear FOMASs, and the control protocol is more universal compared to those proposed in the existing literature.

Many machine-learning-based defect detection methods, especially deep learning-based approaches, have high requirements on computing power and network. They lead to time delay, high cost, and energy consumption when applied to deal with the massive data in an autonomous manufacturing enterprise. So efficient detection in the end-edge-cloud architecture is a good solution to overcome the above challenges. A branchy deep learning detection model with early exit ability of inference is proposed, in which the main branch is deployed on the cloud server and the side branches are on edge equipment. The proposed method quickly and effectively detects the category and location of the defect in printed circuit boards since partial computing task is offloaded to the edge nodes. A prototype system is implemented based on a computer as the cloud server and a Raspberry Pi as an edge node in order to verify the feasibility of the proposed method. The experiment result manifests high detection accuracy and fast computing speed.

The stability of fractional-order (FO) nonlinear system with state-dependent delays (SDDs) is investigated. Unlike the usual time-dependent delays, the state-dependent (SD) delays make the size of the delays relative to the states, which makes the system uncertain when historical state information would be used. A Lemma on Riemann-Liouville derivative is first given to ensure the monotonicity of the considered function. Then, based on the Lyapunov method, several sufficient criteria are presented to guarantee the Mittag-Leffler stability of the discussed systems. In the end, three examples are applied to illustrate the correctness and applicability of our theoretical conclusions, including practical applications in submarine positioning models.

As a generation of the real-valued neural network (RVNN), complex-valued neural network (CVNN) is based on the complex-valued (CV) parameters and variables. The fractional-order (FO) CVNN with linear impulses and fixed time delays is discussed. By using the sign function, the Banach fixed point theorem, and two classes of activation functions, some criteria of uniform stability for the solution and existence and uniqueness for equilibrium solution are derived. Finally, three experimental simulations are presented to illustrate the correctness and effectiveness of the obtained results.

This article focuses on the conservativeness issue of the existing Lyapunov method for linear time-invariant (LTI) nabla fractional-order systems and proposes a converse Lyapunov theorem to overcome the conservative problem. It is shown that the LTI nabla fractional-order system is asymptotically stable if and only if there exist a positive-definite Lyapunov function whose first-order difference is negative definite. After developing a systematic scheme to construct such Lyapunov candidates, the Lyapunov indirect method is derived for the nonlinear system. Finally, the effectiveness and practicability of the proposed methods are substantiated with four examples.

This article considers the regularity, nonimpulsiveness, stability, as well as admissibility of singular fractional-order systems (SFOSs) with the fractional-order$\alpha \in (1,2)$. First, the existence and uniqueness of time-domain solutions of the systems are analyzed by using the Kronecker equivalent standard form, and then the necessary and sufficient condition for the regularity is proposed. Second, the explicit time-domain solutions of the systems are presented, which are the basis of the necessary and sufficient conditions for the nonimpulsiveness and stability. Third, two novel necessary and sufficient conditions for the admissibility of the systems are proposed in terms of the nonstrict linear matrix inequalities (LMIs) and strict LMIs, respectively. Finally, two numerical examples about the SFOSs are provided to illustrate the validity of the obtained conclusions.

It is not easy when evaluating 3D mapping performance because existing metrics require ground truth data that can only be collected with special instruments. In this paper, we propose a metric, dense map posterior (DMP), for this evaluation. It can work without any ground truth data. Instead, it calculates a comparable value, reflecting a map posterior probability, from dense point cloud observations. In our experiments, the proposed DMP is benchmarked against ground truth-based metrics. Results show that DMP can provide a similar evaluation capability. The proposed metric makes evaluating different methods more flexible and opens many new possibilities, such as self-supervised methods and more available datasets.

This paper investigates the regularity, nonimpulsiveness, stability and admissibility of the singular fractional-order systems with the fractional-order α ∈ (0, 1). Firstly, the structure, existence and uniqueness of the time domain solutions of singular fractional-order systems are analyzed based on the Kronecker equivalent standard form. The necessary and sufficient condition for the regularity of singular fractional-order systems is proposed on the basis of the above analysis. Secondly, the necessary and sufficient conditions of non-impulsiveness as well as stability are obtained based on the proposed time domain solutions of singular fractional-order systems, respectively. Thirdly, two novel sufficient and necessary conditions for the admissibility of singular fractional-order systems are derived including the non-strict linear matrix inequality form and the linear matrix inequality form with equality constraints. Finally, two numerical examples are given to show the effectiveness of the proposed results.

Non-Gaussian statistical signal processing is be-coming increasingly significant in the current complex world. However, the variance of a non-Gaussian distribution may not exist, therefore many conventional methods will be considerably weakened, meaningless or even misleading. For example, the least-squares criterion may not be robust in the non-Gaussian environment, owing to the impulsive behaviors and outliers. In the paper, the statistical measures with fractional lower order moments (FLOM) are used instead of second-order moments to assess the control performance for non-Gaussian processes. The results show that FLOM is robust against outliers for the non-Gaussian signals. In the end, Hurst exponent fitting with FLOM and multifractal detrended fluctuation analysis (MFDFA) with FLOM are applied to the real data.

Because that the light-energy consumption (LC) is growing quite dramatically over the past decades, the light-energy efficiency is regarded as a key component of the modern energy system in major cities. In this paper, a novel and sophisticated algorism of lighting control is presented to improve light-energy efficiency and cut down energy consumption, when considering the lighting system with multilighting equipment. In this paper, a multivariate extremum seeking controller is used to manipulate, respectively, the luminance of multilighting equipment, in order to track the minimal LC efficiently. Meanwhile, a proportional- integral-differential (PID) approach is adopted to achieve the required level of illumination. The proposed extremum seeking control (ESC) is designed to ensure the amplitude of the dither signal converge exponentially to zero, by making the amplitudes change with the extremum estimation error. The presented frame helps to improve search speed for the minimal LC value and remove the steady-state oscillations. The comparative experiments reveal that, compared with the convergence velocity of the Newtonand gradient-based ESCs, the developed method for the track of the minimal LC can have higher accuracy and faster speed.

The OS4 quadrotor is a classic quadrotor simulation platform. So far, many different kinds of controllers have been designed based on its plant model. Most of the research only provided a numerical simulation to verify their designed controllers. Only a few researches have put the proposed controllers back to OS4 quadrotor to verify, but they didn't share the project folder to let others continue their work. More open-source and well-documented codes are needed to accelerate the application of fractional order controllers in industry. This paper updated the OS4 folder for the latest MATLAB version. A case of study demonstrated the workflow to design a fractional order proportional derivative controller for the simulated drone. Comparisons showed that fractional order controllers perform better in a nonlinear system like OS4 than integer order PID controllers. An impulse disturbance scenario is also used as a testbed. Project folder can be accessed from: https://ww2.mathworks.cn/matlabcentral/fileexchange/67882-os4-foc. Related videos can be found from this link: https://youtu.be/heuz4tFqf64.

A modified Elman neural network (MENN) based neural adaptive inverse control scheme is proposed for trajectory tracking of rate-dependent hysteresis. To attenuate the influence of the rate-dependent hysteresis, a modified inverse backlash operator (MIBO) is developed to act as the hidden layer neuron of the MENN to describe the dynamic behavior of the inverse rate-dependent hysteresis. The diagonal recurrent weights of context layer and the recurrent weights from hidden layer to context layer are designed to enhance the dynamic learning capability of the MENN. To determine an appropriate structure and the parameters of the MENN for adaptive inverse control, a MENN is trained first based on the data of inverse rate-dependent hysteresis. In view of the nonsmooth characteristics of the MIBOs, a restricted step proximal bundle (RSPB) method is employed to search the appropriate subgradients at the nonsmooth vertexes of the MIBOs. The relevant Levenberg-Marquardt (L-M) algorithm is developed to acquire an appropriate MENN that is used as the initial controller to implement adaptive inverse control for rate-dependent hysteresis via gradient descent learning algorithm. Numerical control results on a Duhem model of piezoelectric actuators have validated the effectiveness of the proposed method.

Remote sensing data using thermal-infrared (TIR) cameras can be very helpful for many applications including agriculture and thermal refugia for fish habitat. Small unmanned aerial vehicles (UAS) can be efficient platforms for providing high-resolution thermal imagery at a low-cost; however most thermal cameras used for remote sensing are cooled systems and can be very large, expensive, and consume large amounts of power. Because of these constraints they are not easily integrated with small UAS. For surveillance and military applications, microbolometer thermal cameras are widely used on UAS because they are smaller, less expensive and consume less power than cooled thermal cameras. However, mi-crobolometer thermal cameras are not as sensitive and accurate as cooled systems. Also, many are not calibrated and only measure relative temperature. This presents a challenge when microbolometer thermal cameras are used for scientific and ecological applications: absolute surface temperature is necessary for these applications. This paper presents procedures that can be followed to convert the raw images from a microbolometer camera to accurately represent surface temperature (without compensating for emissivity). The steps needed for this include preparing the images for orthoretification, geometric calibration, orthorectification, and correction for external disturbances.

Small unmanned aerial systems (UAS) can be very useful for acquiring high resolution thermal imagery for many ecological applications. With these systems, routine ground sampling is important in order to calibrate the imagery and to understand unique environmental disturbances. Simple, accurate methods to sample surface temperature is needed. This paper introduces two methods for sampling ground surface temperatures. One method uses a ground-based thermal camera to capture a high-resolution sample of a small area. The other method uses temperature controlled pools, which can be seen from the aerial thermal image. These samples are used to calibrate a thermal mosaic captured by the UAS AggieAir. Accuracy and simplicity are evaluated for each method.

Stream temperature is important for understanding the environment within a stream. Many collect these data at discrete locations over specific time periods using temperature sensors, however it is becoming more common to gather thermal imagery to have a spatially distributed understanding. The utility of these data can be limited due to cost, and spatial and temporal resolutions. This paper presents a platform for low-cost high-resolution thermal imagery using an unmanned aerial vehicle (AggieAir1). AggieAir can be used to acquire visual, near-infrared (NIR), and thermal imagery at high spatial and temporal resolutions at a much lower cost when compared to conventional sources of remote sensing. In this application, AggieAir is used to collect visual, NIR, and thermal imagery for a stream in northern Utah. Details about the payload and postprocessing methods are presented. The resulting imagery was used to clip the thermal mosaic to only include pixels associated with the stream. This thermal image provided 30cm×30cm resolution stream temperatures. Finally, a simple method of adjusting the images to observed temperatures is proposed to provide better information regarding absolute stream temperatures which are key in understanding the health of the aquatic ecosystem.

In this study, the authors focus on the tracking performance and the robustness of 12 sliding-windowed Hurst estimators for multifractional processes with linear trend local Hölder exponent, noisy multifractional processes and multifractional processes with infinite second-order statistics. Four types of multifractional processes are synthesised to test the tracking performance and robustness of these 12 sliding-windowed Hurst estimators. They are (i) noise-free multifractional process; (ii) multifractional process corrupted by 30-dB signal-to-noise ratio (SNR) white Gaussian noise; (iii) multifractional process corrupted by 30-dB SNR impulse noise; and (iv) multifractional stable process, which has no finite second-order statistics. Furthermore, the standard error of different sliding-windowed Hurst estimators are calculated in order to quantify the accuracy and robustness. This study provides a guideline and principle in the selection of Hurst estimators for noise-free multifractional process, noise-corrupted multifractional process and multifractional process with infinite second-order statistics. The results of this analysis show that the sliding-windowed Kettani and Gubner's method provides the best-tracking performance for multifractional processes with linear trend local Hölder exponent and good robustness to noise.

Long-range dependence (LRD) is discovered in time series arising from different fields, especially in network traffic and econometrics. Detecting the presence and the intensity of LRD plays a crucial role in time-series analysis and fractional system identification. The existence of LRD is usually indicated by the Hurst parameters. Up to now, many Hurst parameter estimators have been proposed in order to identify the LRD property involved in a time series. Since different estimators have different accuracy and robustness performances, in this study, 13 most popular Hurst parameter estimators are summarised and their estimation performances are investigated. LRD processes with known Hurst parameters are generated as the control data set for the robustness evaluation. In addition, three types of LRD processes are also obtained as the test signals by adding noises in terms of means, trends and seasonalities to the control data set. All 13 Hurst parameter estimators are applied to these LRD processes to estimate the existing Hurst parameters. The estimation results are documented and quantified by the standard errors. Conclusions of the accuracy and robustness performances of the estimators are drawn by comparing the estimation results.

An autonomous, unmanned, aerial, remote sensing platform called AggieAir™ has been developed at Utah State University (USU) to produce multispectral aerial imagery. Its independence of a runway, low cost, and rapid turn-around time for imagery make it an efficient platform for applications in riparian areas and in wetlands management. Using third-party software, the imagery from AggieAir can be stitched together into mosaics, georeferenced, and used to classify vegetation and map riparian systems, substrates and fish habitat for hydraulic modeling, river morphology and restoration monitoring. Likewise, the multispectral mosaics can be used to monitor changes in meso-scale aquatic habitat features and invasive/native plant species, as well as delineate different types of wetlands for wetlands management. This paper introduces AggieAir and highlights some of the projects in riparian and wetlands applications in which it has been involved. AggieAir has also been involved with agricultural and biofuel applications.

Small, low-altitude unmanned aerial vehicles (UAV)s can be very useful in many ecological applications as a personal remote sensing platform. However, in many cases it is difficult to produce a single georeferenced mosaic from the many small images taken from the UAV. This is due to the lack of features in the images and the inherent errors from the inexpensive navigation sensors. This paper focuses on improving the orthorectification accuracy by finding these errors and calibrating the navigation sensors. This is done by inverse-orthorectifying a set of images collected during flight using ground targets and General Procrustes Analysis. By comparing the calculated data from the inverse-orthorectification and the measured data from the navigation sensors, different sources of errors can be found and characterized, such as GPS computational delay, logging delay, and biases. With this method, the orthorectification errors are reduced from less than 60m to less than 1.5m.

This paper studies the distributed coordination of networked fractional-order systems over a directed interaction graph. A general fractional-order coordination model is introduced by summarizing three different cases: 1) fractional-order agent dynamics with integer-order coordination algorithms; 2) fractional-order agent dynamics with fractional-order coordination algorithms; and 3) integer-order agent dynamics with fractional-order coordination algorithms. We show sufficient conditions on the interaction graph and the fractional order such that coordination can be achieved using the general model. The coordination equilibrium is also explicitly given. In addition, we characterize the relationship between the number of agents and the fractional order to ensure coordination. Furthermore, we compare the convergence speed of coordination for fractional-order systems with that for integer-order systems. It is shown that the convergence speed of the fractional-order coordination algorithms can be improved by varying the fractional orders with time. Finally, simulation results are presented as a proof of concept.

This paper deals with the concept of (integer-order) memristive systems, which are generalized to non-integer order case using fractional calculus. We consider the memory effect of the fractional inductor (fractductor), fractional capacitor and fractional memristor. We also show that the memory effect of such devices can be also used for an analogue implementation of the fractional-order operator, namely fractional-order integral and fractional-order derivatives. This kind of operator is useful for realization of the fractional-order controllers. We present theoretical description of such implementation and we proposed the practical realization and did some simulations and experimental measurements as well.

Data acquired by aircraft, satellites and other sources of remote sensing has become very important for many applications. Even though current platforms for remote sensing have proved to be robust, they can also be expensive, have low spatial and temporal resolution, with a long turnover time. At Utah State University (USU), there is an ongoing project to develop a new small, low-cost, high resolution, multispectral remote sensing platform which is completely autonomous, easy to use and has a fast turnover time. Many new developments have been added to AggieAir which have improved the flight performance and flexibility, increased the flight time and payload capacity. Furthermore, these developments have made it possible to carry an imaging system with more quality and resolution. With these new developments, AggieAir has begun work with many projects from areas in agriculture, riparian habitat mapping, highway and road surveying and fish tracking. Development on AggieAir continues with future plans with a thermal inferred camera, an in house iner-tial measurement unit (IMU) and better navigation to handle higher winds.

Even though small, low-cost unmanned aerial vehicles (UAVs) make good remote sensing platforms by reducing the cost and making imagery easier to obtain, there are also some tradeoffs. The low altitude, small image footprint and high number of images make it difficult and tedious to georeference the images based on features. Auto-orthorectification techniques based on the position and attitude of the UAV would work well except the inherent errors in the UAV sensors reduce the accuracy of the orthorectification significantly. This paper presents a method to improve the orthorectification accuracy by calibrating the UAV sensors. This is done by inverse orthorectifing the images to find the actual position and attitude of the UAV using ground references setup in a square. Actual data from a test flight is used to validate this method.

This paper studies the robust stability of discrete-time iterative learning control (ILC) systems in a networked control system setting(NCS). First we consider the problem where data sent from a remote plant to the ILC algorithms is subject to random data dropouts. For this scenario we establish mean-square stability, taking into account the convergence of the covariance of the error along the iteration domain. Next, using this result, we derive a robust stability condition for a more general NCS-based ILC problem with data delays and dropouts in the control signals transmitted to the remote plant as well as data delays in output signals returned from the plant.

Cogging effect is a serious disadvantage of the permanent magnetic synchronous motor (PMSM), and cogging force is a position-dependent periodic disturbance. In our previous work [1], a dual high-order periodic adaptive learning compensation (DHO-PALC) method for state-dependent periodic disturbance was presented, where the long term stability issue was not addressed. Due to the impact of the high frequency components, when applying the DHO-PALC for a long time, the tracking errors may grow, and the system may become even unstable eventually. This phenomenon also appears in many other practical motion control systems using ILC or RC strategies. In this paper, in order to achieve long term stability, we propose a dual high-order dynamic adaptive learning compensation (DHO-D-PALC) method for cogging effect. In this method, stored information of more than one previous periods are included for both the composite tracking error and the estimate of cogging force. Particularly, since we use a dynamic learning control law to update the current estimate of cogging, the long term stability can be guaranteed. Extensive simulation results are included to demonstrate, 1) high-order in composite tracking error offers faster convergence, 2) high-order in cogging estimate better accommodates the case of varying reference signal, 3) dual high-order scheme has the potential of much better performance over the conventional first-order scheme, and 4) the introduction of dynamic learning updating scheme helps achieving the long term stability of the adaptive learning controller.

Aerial imaging has become very important to areas like remote sensing and surveying. However, it has remained expensive and difficult to obtain with high temporal and spatial resolutions. This paper presents a method to retrieve georeferenced aerial images by using a small UAV (unmanned aerial vehicle). Obtaining aerial images this way is inexpensive, easy-to-use and allows for high temporal and spatial resolutions. New and difficult problems are introduced by the small image footprint and the inherent errors from an inexpensive compact inertial measurement unit (IMU). The small image footprint prevents us from using the features from the images to help negate the errors from the IMU, which is done in conventional methods. Our method includes: using the data from the IMU to georeference the images, projecting the images on the earth and using a man-in-the-loop approach to minimize the error from the IMU. Sample results from our working system are presented for illustration.

In this paper, a simulation model of PMSM position servo system is presented briefly first, we then propose a high order periodic adaptive learning compensation (HO-PALC) method for cogging effect on PMSM position and velocity servo tasks. The cogging force is considered as a position-dependent disturbance that is periodic. The key idea of the implemented cogging disturbance compensation method is to use past information of more than one position period along the state axis to update the current adaptation learning law. Simulation results are presented to illustrate the effectiveness of the high order periodic adaptive cogging compensation scheme. Furthermore, the advantage of the HO-PALC is demonstrated through comparing with the first order periodic adaptive learning compensation.

In this paper, we experimentally implement and validate distributed consensus algorithms on a mobile actu- ator and sensor network platform under directed, possibly switching interaction topologies to explore issues and challenges in distributed multi-vehicle cooperative control. Distributed consensus algorithms are applied to three target applications namely rendezvous, axial alignment, and formation maneu- vering. In the rendezvous application, multiple mobile robots simultaneously arrive at a common a priori unknown target location determined through team negotiation. In the axial alignment application, multiple mobile robots collectively align their final positions along a line. In the formation maneuvering application, multiple mobile robots form a rigid geometric shape and maneuver as a group with a given group velocity. The experimental results show the effectiveness and robustness of the consensus algorithms even in the presence of platform physical limitations, packet loss, information delay, etc.

In this paper, the iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors. The papers includes a general introduction to ILC and a technical description of the methodology. The selected results are reviewed, and the ILC literature is categorized into subcategories within the broader division of application-focused and theory-focused results.

This paper presents a discrete learning controller for vision-guided robot trajectory imitation with no prior knowledge of the camera-robot model. A teacher demonstrates a desired movement in front of a camera, and then, the robot is tasked to replay it by repetitive tracking. The imitation procedure is considered as a discrete tracking control problem in the image plane, with an unknown and time-varying image Jacobian matrix. Instead of updating the control signal directly, as is usually done in iterative learning control (ILC), a series of neural networks are used to approximate the unknown Jacobian matrix around every sample point in the demonstrated trajectory, and the time-varying weights of local neural networks are identified through repetitive tracking, i.e., indirect ILC. This makes repetitive segmented training possible, and a segmented training strategy is presented to retain the training trajectories solely within the effective region for neural network approximation. However, a singularity problem may occur if an unmodified neural-network-based Jacobian estimation is used to calculate the robot end-effector velocity. A new weight modification algorithm is proposed which ensures invertibility of the estimation, thus circumventing the problem. Stability is further discussed, and the relationship between the approximation capability of the neural network and the tracking accuracy is obtained. Simulations and experiments are carried out to illustrate the validity of the proposed controller for trajectory imitation of robot manipulators with unknown time-varying Jacobian matrices.

Soft computing (SC) and hard computing (HC) methodologies are fused together successfully in numerous industrial applications. The principal aim is to develop computationally intelligent hybrid systems that are straightforward to analyze, with highly predictable behavior and stability, and with computational burden that is no more than moderate. All these goals are particularly important in embedded real-time applications. This paper is intended to clarify the present vagueness related to the fusion of SC and HC methodologies. We classify the different fusion schemes to 12 core categories and six supplementary categories, and discuss the characteristic features of SC and HC constituents in practical fusion implementations. The emerging fusion approach offers a natural evolution path from pure hard computing toward dominating soft computing.

This paper presents an adaptive controller for the compensation of state-dependent disturbance with unknown amplitude in low-cost wheeled mobile robot servo control. The considered state-dependent disturbance is caused by the friction and the eccentricity between the wheel axis and the motor driver. Our proposed control algorithm guarantees the asymptotical stability for both the velocity and the position tracking. Experiment results show the effectiveness of the adaptive disturbance compensator for the wheeled mobile robot in low velocity diffusion tracking.

Mobile actuator and sensor network (MAS-net) is a project that adds node mobility and close-loop control concept into the field of wireless sensor network. An experiment platform is built for the MAS-net project. In the experiment platform, cheap, small, and energy-efficient Mica2 motes have been used as both wireless sensors and real-time embedded mobile robot controllers. These mote-based robots are called MAS-motes. An integrated system has been developed to locate MAS-motes by an overhead camera, collect MAS-motes' sensor reading and assign destinations to MAS-motes. This system can communicate with robots via Mica2 motes' built-in radio chips. Pattern formation can bring great benefit to mobile wireless sensor network in sensing range, fault tolerance and sensor-actuator cooperation. This paper tries to use cheap, energy-efficient and mote-based MAS-motes to achieve formation with a given pattern.

In this paper, we presented a review on the current control issues and strategies on a group of unmanned autonomous vehicles/robots formation. Formation control has broad applications and becomes an active research topic in the recent years. In this paper, we attempt to review the key issues in formation control with a focus on the main control strategies for formation control under different kinds of scenarios. Then, we point out some important open questions and the possible future research directions on formation control. This paper contributes with a new and interesting consideration on formation control and its application in distributed parameter systems. We pointed out that formation control should be classified as formation regulation control and formation tracking control, similar to regulator and tracker in conventional control.

This paper presents a numerical solution for a mobile sensor motion trajectory scheduling problem under nonholomonic constraints of a project named MAS-net, which stands for mobile actuator-sensor network. The motivation of the MAS-net project, at the first stage, is to estimate diffusion system parameters by networked mobile sensors. Each sensor is mounted on a differential-drive mobile robot to observe the diffusing fog. In other words, this project requires to observe a parabolic distributed parameter systems (DPS) by nonholonomic networked mobile sensors. This paper reformulates this problem in the framework of optimal control and proposes a procedure to obtain a numerical solution by using RIOTS by A. Schwartz, E. Polak, and Y. Q. Chen (1997) and Matlab PDE toolbox. The objective function of this method is designed to minimize the effect of the sensing noise. Extensive simulation results are presented for illustration.

A new tuning method for proportional-integral-derivative (PID) controller design is proposed for a class of unknown, stable, and minimum phase plants. We are able to design a PID controller to ensure that the phase Bode plot is flat, i.e., the phase derivative w.r.t. the frequency is zero, at a given frequency called the "tangent frequency" so that the closed-loop system is robust to gain variations and the step responses exhibit an iso-damping property. At the "tangent frequency," the Nyquist curve tangentially touches the sensitivity circle. Several relay feedback tests are used to identify the plant gain and phase at the tangent frequency in an iterative way. The identified plant gain and phase at the desired tangent frequency are used to estimate the derivatives of amplitude and phase of the plant with respect to frequency at the same frequency point by Bode's integral relationship. Then, these derivatives are used to design a PID controller for slope adjustment of the Nyquist plot to achieve the robustness of the system to gain variations. No plant model is assumed during the PID controller design. Only several relay tests are needed. Simulation examples illustrate the effectiveness and the simplicity of the proposed method for robust PID controller design with an iso-damping property.

This paper presents linear approximation ideas to range identification problem for a perspective dynamic system (PDS). Using a recently introduced linear approximation technique, the perspective dynamic system, which is a special class of nonlinear systems, can be approximated by a sequence of linear time-varying (LTV) subsystems. Observer design problem of the original nonlinear PDS reduces to the observer design of this sequence of LTV subsystems. For each LTV subsystem, existing standard observer methods can be applied.

Perspective problems arise in machine vision when using a camera to observer the scene. Essential problems include identification of unknown states and/or unknown parameters from perspective observations. Range identification is to estimate the states/positions of a moving object with known motion parameters which has been discussed in the literature using nonlinear observers with full homogeneous observations derived from the image plane. In this paper, the same range identification problem is discussed with single homogeneous observation using nonlinear observers. Our simulation results verify the convergence of the observers when their observability conditions are satisfied.

This paper presents a frequency-domain analysis and design approach for a learning feedforward controller (LFFC) using a dilated B-spline network. The LFFC acts as an add-on element to the existing feedback controller (FBC). The LFFC signal is updated iteratively based on the FBC signal of the previous iteration as the task repeats. Similar to proportional-integral-derivative controller tuning, there are only two parameters to adjust: The B-spline support width and the learning gain. The effect of dilation in the B-spline network is discussed. Detailed design formulae are given based on a stability analysis. As an illustration, simulation results on the path tracking control of a wheeled mobile robot are presented.

ODIS is an omni-directional mobile robot designed to autonomously or semi-autonomously inspect automobiles in a parking lot. Periodically, its position and orientation references need to be reset. This paper considers visual servoing to parking lot lines as one possible approach. Analysis and simulations demonstrate that a surprisingly simple proportional controller in the image coordinates can accomplish position and orientation alignment with parking lot lines. Unlike previous work, no image Jacobian matrix is necessary. Knowledge of the camera focal length is not required, but the camera and vehicle axes are assumed to be aligned, and the vehicle is assumed to rotate about the camera frame's y-axis.