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The objective of this paper is system identification and design of a robust H ∞ controller for a stabilization system of a two-axis gimbaled sensor. Typically the tracking gimbaled sensor is mounted on a missile to track a dynamic target. The tracking is bound to destabilize from the wind disturbances and vibrations of a missile. Stabilization is meant for providing rejection of disturbances induced in the sensor by the missile's motion. Using frequency domain system identification approach the plant transfer function has identified on the basis of experimental freqency response data. The identified plant model consists of gimbal and actuator dynamics. Here a feed back H ∞ suboptimal controller is designed to stbilize the sensor such that tracking is uninterupted. The performance simulation results with H ∞ controller has shown in this paper.

A recently proposed Bayesian tracking procedure for sensor networks approximates the update equation, which involves non-linear measurements, with a simple equation using the maximum likelihood (ML) estimate of the unknown state. This approach permits a numerically efficient implementation of the tracking procedure, and is suitable for a distributed implementation. In this paper we study the extent to which this approach approximates the theoretical Bayesian solution. We provide conditions to guarantee that the approximation becomes asymptotically exact, as the number of nodes becomes large. This result is relevant in applications where each sensor obtains a measurement with limited information about the state, but a large number of sensors is available. We apply our result to a case study, and present numerical simulations, showing that the approximation error becomes negligible for a relatively small number of nodes.

This paper considers the problem of controlling relative attitude between two spacecrafts, so as to follow a smoothly time varying desired relative attitude trajectory. The spacecrafts are modelled as rigid bodies and attitudes are represented in non-linear state space of SO(3). The attitude control law proposed does not need relative attitude measurement and is determined from line of sight (LOS) unit vectors measured by spacecrafts to each other and from each spacecraft to a common object in respective body fixed frames. Unlike existing control laws using LOS measurements, positions of spacecrafts are not considered fixed. The state feedback laws proposed in this work guarantee almost global asymptotic tracking of the desired time varying attitude trajectory, when positions of spacecrafts do not become collinear or coincide. The simulation results of the proposed control law are also shown.

This paper studies a distributed consensus tracking control problem for a class of stochastic linear multi-agent systems subject to two types of attacks. The problem boils down to how to achieve robust consensus tracking of multi-agent systems with switching connected and disconnected directed topologies under attacks. The attacks on the edges instead of nodes lead to the loss of consensus tracking security. Based on a multi-step design procedure for designing a distributed secure algorithm, sufficient conditions on robust mean-square exponential consensus tracking are derived via the idea of average dwell time switching between some stable and unstable subsystems obtained from graph theory analysis. An applicaton to a practical power system is considered. It is proved that each distributed generator (DG) modeled as an agent in a microgrid can successfully synchronize their terminal voltage amplitude to a prespecified reference value under these two types of attacks.

This work discusses several approaches to the multi input, single output problem in hydraulic control. The multi input problem arises from the fact, that the actuator force in a hydraulic cylinder is the result of a combination of two pressure states, which is not uniquely defined. With each individual pressure state there may be associated a subsystem with a control input. To achieve a desired actuator force there are inifinitely many combinations of pressure values possible, and it is therefore unclear how to chose the tracking reference values for the pressure subsystems. Secondary objectives are defined, with time optimality of the step response of the subsystems being one of them, that can be used to solve the multi input problem by treating it as an optimization problem. The practical implications of the optimal solutions are discussed and a new solution is proposed, motivated by the results of the optimization problems. The proposed solution is used to develop a backstepping control for a hydraulic multi input single output system, and its performance is shown in simulation.

The ability to robustly track a human is an essential prerequisite to an increasing number of applications that needs to interact with a human user. This paper presents a robust vision based algorithm to track a human in a dynamic environment using interest point-based method. The tracking algorithm is expected to cope with changes in pose, scale, illumination as well as camera motion. The interest point based (e.g. SURF) tracking methods suffer from the limitation of unavailability of sufficient number of matching key points for the target in all frames of a running video. One solution to this problem is to have an object model which contains SURF features for all possible poses and scaling factors. So an object model with all possible descriptors could be created off-line and could be used for detecting the target in each and every frame. However, such a scheme can not be used for tracking an object online. In order to overcome this problem, we propose a new approach which update the object model online and have sufficient matching key points for the target in case of change in the pose as well as scaling. Experimental results are provided to show the efficacy of the algorithm.

International audience; In this paper, the problem of the switched H ∞ tracking output feedback control problem is studied. The control design problem is addressed in the context of discrete-time switched linear systems. Then, the design of continuous-time case becomes trivial. Linear Matrix Inequality (LMI) and Linear Matrix Equality (LME) representations are used to express all sufficient conditions to solve the control problem. Some transformations leading to sufficient conditions for the control problem are also used. All conditions are established for any switching using a switched Lyapunov function and a common Lyapunov function. The effectiveness of the proposed control approach is shown through a steering vehicle control implementation. Interesting simulation results are obtained using real data acquired by an instrumented car.

Post-contrastographic Dynamic Contrast Enhancement (DCE) is a consolidated MRI technique to perform non-invasive analysis via Imaging. The dynamics perfusion of specific drugs, named Contrast Agents (CA), allows to highlight anomalies in tissues, since the vascular substrate is differently developed (hypervascularized) for diseases compared with healthy parenchyma. The goodness of the analysis procedure influences the inferred diagnosis: the more objective the analysis, the more accurate the prognosis and the therapies follow-up. Deformations and displacements affect organs and soft tissues and their correction is challenging for DCE analysis; the complexity lies also on artefacts of image registration process because of the backscattered image signal intensity varies over time due to the diffusion/perfusion phenomenon induced by the CA injection. In this work we present a combined approach based on the Active Contours framework and on the Optimal Mass Transportation for the automatic depicting and tracking of deformable objects. Active Contours provide the automatical contour's depiction of a deformable object by minimizing an energy cost functional; Optimal Mass Transportation allows to tracking a deformable object by minimizing the (object's) transport cost under the (object) mass conservation principle. We applied these methodologies to post-contrastographic DCE MR images' series and we are going to show the improvement in the goodness of DCE analysis compared with results gathered with other tracking strategies.

For most practical nonlinear state estimation problems, the conventional nonlinear filters do not usually work well for some cases, such as inaccurate system model, sudden change of state-interested and unknown variance of measurement noise. In this paper, an adaptive cubature strong tracking information filter using variational Bayesian (VB) method is proposed to cope with these complex cases. Firstly, the strong tracking filtering (STF) technology is used to integrally improve performance of cubature information filter (CIF) aiming at the first two cases and an iterative scheme is presented to effectively evaluate a strong tracking fading factor. Secondly, the VB method is introduced to iteratively evaluate the unknown variance of measurement noise. Finally, the novel adaptive cubature information filter is obtained by perfectly combining the STF technology with the VB method, where the variance estimation provided by the VB method guarantees normal running of the strong tracking functionality.

The development of high performance controllers capable of dealing with both tracking control and disturbance rejection has attracted considerable attention. This paper investigates the two-degree-of-freedom control structures with a disturbance observer and command feedforward control, respectively, and presents a double-loop control structure. The proposed control structure allows intuitive and independent tuning of disturbance rejection as well as tracking performance. Experimental results obtained from an illustrated servo control system are given to demonstrate the effectiveness and feasibility of the double-loop control structure and design methodology.

An adaptive integrator backstepping control law for magnetically actuated earth-pointing satellite is proposed. The equation for required magnetic control torque along the three body axes is shown with the proposed control law. The stability of the system about the origin is proved and it is uniformly asymptotically stable. A time variant uncertainty in the dynamics of spacecraft is considered. The detailed implementation of the control law is developed and presented. In order to show the efficacy of the proposed control law, the simulation is given for the tracking problem of the satellite under time variant uncertain disturbance.

Biomedical application of swimming microrobots such as controlled drug delivery, microsurgery and diseases monitoring have made the researches conspicuous in MEMS technology. In this paper, inspired by the flagellar motion of microorganisms (i.e. bacteria) in nature, a 3-DOF swimming microrobot is developed and analyzed. The body of the microrobot is driven by use of three prokaryotic flagella. The rotation of each flagellum in the fluid media leads to propulsion in the microrobot. At first, attention is focused on the dynamic modeling of the microrobot's motion and then, a suitable controller is designed for its tracking performance. We use resistive-force theory to derive the propulsion force generated by the flagella. Feedback linearization method is used to control the motion of the swimming microrobot for tracking performance. It is seen that, by applying three flagella, the microrobot enables to do 3-D maneuvers. The results show that the tracking performance is guaranteed by use of the designed controller and the microrobot can be controlled to do desired maneuvers and follow the desired trajectory.

A novel suboptimal control technique for output tracking was developed using model predictive static programming (MPSP). The control technique was applied to a nonlinear model of a single-stage closed grinding mill circuit in simulation. The control technique could determine a suboptimal input trajectory to maintain the output variables at their desired values within 7 iterations. The input trajectory determined after only 2 iterations was already close to the final solution. This technique shows promise to significantly reduce computational time to find a suboptimal input trajectory for output tracking in large industrial processes.

In this paper, the second-order sliding mode super-twisting controller is applied to distributed tracking problem. The distributed tracking is ensured by the proposed controller after a finite-time transient. The controller ensures the exact tracking of leader's position in spite of external disturbances acting on each follower. The corresponding conditions for the distributed tracking and for the simultaneous consensus are studied. Simulations show the workability of the proposed method.

The attitude coordinated tracking problem of multiple spacecraft with control input saturation is studied in this paper. We first construct a decentralized attitude coordinated tracking algorithm with input saturation, such that spacecraft can track a time-varying desired trajectory and align their attitudes. We then introduce a novel auxiliary dynamical systems to present an attitude coordinated tracking algorithm with input saturation and without angular velocity measurements. Throughout this paper, the information flow between spacecraft is assumed to be undirected. Finally, design examples are given to show the effectiveness of the proposed attitude coordinated tracking algorithms.

There has been a considerable amount of work done on tracking the boundaries of specified regions. The present paper focuses on the same problem. However, unlike previous works, this paper uses the cyclic pursuit scheme and its variants to locate the boundaries of specified regions, when the agents are initially deployed away from the region, and subsequently track the boundary continuously. The number of agents required under this paradigm is also considerably lesser than in existing strategies. In almost all cases, the algorithm described in this paper guarantees the convergence to the boundary. Simulation results have been presented to substantiate the efficacy of the proposed scheme.

When a load is added to the tip of a wire actuated robotic forceps, tracking accuracy of the forceps is degraded due to the elastic elongation of the wire. In this paper, for the wire actuated robotic forceps, a new algorithm that can estimate a reaction torque and an amount of elongation of the wire, is proposed. By compensating for amount of elongation of the wire, track ability of the forceps is improved. The effectiveness of the proposed algorithm was verified through simulation and experimental works.

The paper deals with tracking problem for single-input single-output linear systems with unstable zero dynamics under parametric and signal uncertainty of control plant and reference model within the framework of the sliding mode techniques for feedback design, state observation and parameters identification in real time.

Performance of trajectory tracking is important for Wheeled Mobile Robots (WMR), which have wide applications in industry, medical treatment, and domestic service, etc. In this paper, comprehensive modeling of a WMR is presented with model parameters estimated via system identification. The influence on experimental frequency response due to friction disturbances at low frequency and modeling uncertainties at high frequency are carefully analyzed. To achieve higher trajectory-tracking performance, both the non-holonomic kinematic constraints and the dynamics of actual WMRs are taken into account, and a -synthesis based robust control method is presented to deal with the friction disturbances and the high-frequency modeling errors. Comparative experimental results are also obtained to verify the effectiveness of the proposed control strategy in actual implementation.

Model predictive control (MPC) has been attractive in practical designs due to the inherent manner in which both control and state constraints can be incorporated. An open problem is the choice of an appropriate prediction horizon that guarantees both stability and performance. The goal of this paper is to show that an optimal prediction window arrived at by switching between multiple receding-horizon controllers can provide closed loop stability and improve tracking performance.

In networked control systems, sequence-based controllers are used to compensate for transmission delays and losses in unreliable data networks. For this purpose, the controller sends not only the current control input to the actuator but also a sequence of predicted control inputs. The additional inputs can be used when subsequent transmissions get delayed or lost. In this paper, the sequence-based method is applied to the problem of trajectory tracking over an unreliable network and an optimal sequenced-based tracking controller is derived. The main advantage of the presented approach is that future information on the reference trajectory can optimally be embedded in the predicted control sequences. Furthermore, the controller can be implemented offline. An interesting result is that the optimal controller can still be separated into a feedback part and a feedforward part (as in standard optimal tracking control) despite of both the unreliable network and the sequence-based method. The performance of the derived tracking controller is demonstrated by Monte Carlo simulations with an inverted pendulum.

This paper presents a MPC (Model Predictive Control) algorithm for MEMS vibratory gyroscopes based on force-balancing control strategy. In the proposed MPC method, using a set of orthonormal basis functions named Laguerre functions, a new prediction and optimization technique is designed. To enhance the capability of proposed MPC method for tracking time-varying reference trajectories, first a repetitive control technique is developed. Second, following representing the governing dynamical equations of the vibratory gyroscope, discrete-time Laguerre network based MPC has been developed. The effective tracking performance of the proposed control methods has been shown through computer simulations.

This paper investigates the distributed tracking control problem for multiple Lagrangian systems under a general directed graph where only a portion of the agents have access to the desired time-varying trajectory. To overcome the problem that only positions are measured, a observer is designed to estimate the velocity for each follower. By employing the estimated states, the distributed observer-based controller is proposed using only position measurements. Furthermore, the condition for the distributed tracking problem on the directed graph is derived, such that the tracking errors and observer errors semi-globally converge to zero. Finally, simulation examples are provided to show the effectiveness of the proposed control algorithms.

This paper presents an internal model-based approach for trajectory tracking in the discrete time-varying setting. A newly designed parallel kinematics servo gantry is well suited for high precision contouring at high speed ranges. The discrete time-varying internal model-based control is developed for controlling the servo gantry system to track complicated trajectories generated by linear time-varying systems. Based on a novel parallel time-varying internal model structure, a low order robust discrete time-varying stabilizer is synthesized. The proposed tracking control architecture is deployed on the novel parallel kinematics servo gantry system to achieve high precision contour tracking performance for frequency-varying signals generated by high order time-varying autonomous system.

The distributed tracking problem is investigated for a class of multi-agent systems with high-order stochastic nonlinear dynamics where the subsystem of each agent is driven by inherent nonlinear drift and diffusion terms. For the case where the graph topology is directed and the leader is the neighbor of only a small portion of followers, a new distributed integrator backstepping design method is proposed, and distributed tracking control laws are designed to ensure that the closed-loop system has an almost surely unique solution on [0, ∞), all the states of the closed-loop system are bounded in probability, and the tracking errors can be tuned to arbitrarily small with a tunable exponential converge rate. The efficiency of the tracking controller is demonstrated by a simulation example.

In this paper, we propose a new control scheme, position and velocity profile tracking control for new generation servo track writing (STW). While conventional servo track writers need controllers that perform fast positioning control with fast track seek and regulation, spiral servo track writers require accurate position and velocity profile tracking control for high quality servo patterns on the media disk. Since STW timing eventually renders geometrically accurate servo patterns, in constant velocity region both position and and velocity error signals should be regulated within small bounds. It is known that regulation via integral sliding mode controller (SMC) provides a good tracking performance, but using a high switching gain is not appropriate for a system having resonance modes. In this paper, thus we apply sliding mode control with disturbance observer to STW. To verify utility of the proposed position/velocity profile track control, we conducted some experiments using a state-of-the-art STW system. Comparative study with the conventional control method was made. The performance of proposed method was experimentally validated.

This paper presents an implementation of predictive functional control for image- based satellite attitude control. The approach considers Low Earth Orbit small satellites equipped with an on-board camera for interactive Earth observation. The control goal is to keep a target on the Earth surface in the satellite's camera field of view during orbiting of the satellite above the target. The control error is given in the image frame, therefore in-image interaction with the system is possible. Two types of control problems are considered: target tracking and oriented-target tracking. The system contains a delay that is taken into account in development of the predictive control law. The control problem is solved in the framework of predictive functional control that is based on the kinematic model of the satellite attitude. The presented control algorithm is validated in simulation environment.

Based on the proportional-integral (PI) closed-loop control widely used in industrial engineering practice, a robust iterative learning control (ILC) method is proposed for industrial batch processes subject to time-varying uncertainties and load disturbance. An important merit is that the proposed ILC design is independent of the PI tuning which maintains the closed-loop system stability, owing to that the ILC updating law is implemented through adjusting the set-point of the closed-loop system and adding a feedforward control signal to the plant input along the batch-to-batch direction. Using the robust H infinity control objective, a robust discrete-time PI tuning algorithm is given in terms of the plant state-space model description with norm-bounded time-varying uncertainties. For the batch-to-batch direction, a robust ILC updating law is developed based on the two-dimensional (2D) control system theory, which is capable of perfect output tracking against repetitive type load disturbance. An illustrative example from the literature is adopted to demonstrate the effectiveness and merits of the proposed ILC method.

This paper presents a model-based receding horizon optimal control algorithm for the engine speed tracking control. A mean-value model including the air intake dynamics and the rotational dynamics is exploited in the tracking controller design, which is calibrated based on the physical rules combined with curve fitting techniques. Based on this mean-value model, the dynamical model of the speed tracking error is derived for any given speed command. The design problem is reduced to the receding horizon control problem under the constraint of the tracking error dynamics. The online computational algorithm based on C/GMRES approach is adopted to solve this nonlinear receding optimal problem. Finally, simulation and experiments results demonstrate the satisfactory tracking performance by using the proposed controller.

In this paper design of reduced order switching function for a discrete-time uncertain nonminimum phase system in special coordinate basis form is proposed. The sliding mode control with this method guarantees the asymptotic stability of all states of system in presence matched disturbance. This problem is further extended to the tracking problem of discrete time uncertain nonminimum phase systems. The results obtained with reduced order sliding surface design are compared with the results of full order sliding surface.

We consider the output tracking of a set-point using cooperative distributed model predictive control. We propose a framework to avoid the loss of feasibility, guarantee stability, constraint satisfaction as well as convergence to admissible set-points. To enable a distributed implementation of the model predictive control law we utilize a cyclic varying horizon length. A simulation example illustrates the approach and its applicability for interconnected systems.

Unlike the traditional tracking control of single marine vehicle, this paper considers the formation tracking control of multiple marine surface vehicles over a directed network in the presence of unknown dynamics, all seeking to maintain a formation relative to a time-varying trajectory. A cooperative dynamic surface control design approach is proposed to devise the formation tracking controllers, where nonlinearly-parameterized neural networks are used to compensate for the model uncertainties. It is proved that with the developed controllers, formation among vehicles can be reached if the trajectory has directed paths to all follower vehicles. Based on Lyapunov stability analysis, all signals in the closed-loop system are guaranteed to be uniformly ultimately bounded, and formation tracking errors converge to a small neighborhood of the origin. Simulation results are given to show the efficacy of the proposed method.

In this paper, a kind of rectangular pulse is adopted to compensate for the magnitude-varying PD-type iterative learning control updating rule so as to suppress the tracking error incurred by the initial state shifts. In the tracking performance analysis, the iteration-wise tracking error function is normalized by its input magnitude and then measured in the integration form of the Lebesuge- p norm. The normalized tracking error is evaluated by taking advantage of the generalized Young inequality of convolution integral. The evaluation shows that the pulse compensation may effectively suppress the normalized tracking error caused by the initial state shifts whilst the interaction among the subsystems and the magnitude-variance of the successive inputs may influence the precision of the tracking performance. Numerical simulations are given to illustrate the validity and the effectiveness.

A Trajectory tracking problem for direct-drive inertially stabilized platforms suffered from nonlinear friction was considered in this paper. The dynamic characteristics of the system was study. A parameter identification method for LuGre model based on multilevel coordinate search algorithm was presented. Furthermore, nonlinear friction observer was designed to estimate the unmeasurable internal state of LuGre model. The comparative trajectory tracking experiments were conducted on a simulator of direct-drive systems between the single proportional–derivative (PD) control and the PD with LuGre model-based compensation. The experimental results revealed that the control scheme based on LuGre-model-based friction compensation reduced 3δ tracking error to 0.2mrad which is improved almost 66.7% compared with the PD control. At last, the ‘fake non-dissipativity phenomenon’ of LuGre in simulation and the digital implementation of LuGre friction compensation were discussed.

For many desirable applications of vision guided industrial robots, real-time visual servoing is necessary but also challenging. Difficulty comes from the limited sampling rate and response time of typical machine vision systems equipped on industrial robots. These factors are addressed as the dynamics of visual sensing. In addition, robot dynamics should also be fully considered when designing the control law. Considering these aspects, this paper presents a control scheme of visual servoing. A dual-rate adaptive tracking filter is presented to compensate the visual sensing dynamics. Based on the compensated vision feedback, the techniques of multi-surface sliding control and dynamic surface control are used to formulate a two-layer control law for target tracking. System kinematics and dynamics are decoupled and dealt with by the two layers of the control law respectively. The proposed method is validated through experiments on a SCARA robot.

Duo to the periodicity of farm cropping, harvesting, seeding and spraying, a discrete time model free adaptive iterative learning control (MFAILC) algorithm for path tracking of farm vehicle is proposed. Based on the dynamical form linearization model for farm vehicle kinematic model l, the MFAILC algorithm can be designed just depending on the I/O data of the farm vehicle. The proposed method also can compensate the effect of initial shifting to preserve a perfect tracking. Simulations results illustrate the validity of the proposed methods.

Tracking problem of uncertain nonminimum phase system using sliding mode control is solved in two steps. The first step involves structural decomposition of a system via two invertible transformations to bring the system into special coordinate basis. In the second step, sliding mode control is applied to stabilize the mismatch dynamic of the system without experiencing the overshoot in response, while keeping the speed of response faster in presence of matched disturbance.

This paper presents results of experiments on the integration of human operators and automation in the context of assembly systems. Several experiments have been conducted to evaluate the use of augmented reality applications in order to provide dynamic work instructions and online feedback information to a shop-floor operator. Based on these results, an experimental platform has been developed so that the shop-floor operator and the automated system behave as a closed-loop system.

This work presents a study that uses a fusion algorithm to improve the accuracy of tracking estimates employing target motion analysis and geographic data. The target tracking process is performed by an unmanned aerial vehicle that has an onboard bearings-only sensor to perform a surveillance mission. Geographic information comes from GIS and databases of real marine vessels. The used target tracking algorithm is a Course Constrained Interactive Multiple Model (IMM), able to detect on-course and off-course modes and track using a course constrained filter and a conventional filter respectively. The performed simulations results present the case of a vessel approaching a port and finally entering a harbour. Final results consist on the comparison of the tracking results using a conventional Extended Kalman Filter and the Course Constrained IMM in two different cases; the first one is a semi-ideal case, where the vessel is on course most of the time, and the second case uses actual vessel data which is off course most of the time.

In this paper a tracking controller is designed for a helicopter plus slung load system. The controller designed is nonlinear and is based on the Backstepping method. First we develop a comprehensive nonlinear mathematical model of the helicopter and design the nonlinear Backstepping controller for trajectory tracking for the helicopter alone. Then we model the slung load mathematically as a slung load and integrate it to the helicopter system. In the next step we design the delayed feedback controller for load swing damping which works independent of the nonlinear tracking controller. The anti-swing controller for load is optimized for best performance by the differential evolution (DE). Finally we present the simulation and results to illustrate the effectiveness of the method.

This paper presents the study of a quadrotor trajectory tracking control for outdoor applications where the measurements provided by the sensors are noisy and imprecise. Time scale separation of the translational and rotational quadrotor dynamics is used to achieve trajectory tracking. A sliding mode controller is proposed for the translational dynamic and provides the desired orientation for the UAV, which is controlled by a linear PD control. Simulations are carried out to test the proposed controller taking into account the measurement error from real not expensive sensors.

A method for PTZ camera re-configuration oriented toward tracking applications and surveillance systems is presented. The visual constraints are transformed into geometric constraints by a coverage model, and the final PTZ configurations are averaged by a consensus algorithm. The approach is to design a distributed algorithm that enables cooperation between the cameras. Experimental results show successful camera handoff.

Fast and accurate tracking of periodic reference trajectories is highly desirable in many nanopositioning applications, including scanning probe microscopy. Performance in common positioning stage designs is limited by the presence of lightly damped resonances, and actuator nonlinearities such as hysteresis and creep. To improve the tracking performance in such systems, several damping and tracking control schemes have been presented in the literature. In this paper, five different control schemes are presented and applied to a nanopositioning system for experimental comparison. They include schemes applying damping control in the form of positive position feedback, integral resonant control, integral force feedback, and passive shunt-damping. Also, a control scheme requiring only a combination of a low-pass filter and an integrator is presented. The control schemes are fixed-structure, low-order control laws, for which few results exist in the literature with regards to optimal tuning. A practical tuning procedure for obtaining good tracking performance for all of the presented control schemes is therefore presented. The schemes provide similar performance, and the main differences are due to the specific implementation of each scheme. Copyright © 2013 IFAC. Published by Elsevier Ltd. All rights reserved. This is the authors' accepted and refereed manuscript to the article.

In this paper, we study the nonlinear data fusion problem for a kind of Bearings-only tracking network system with correlations among measurement noises. As a result, a decentralized cubature Kalman fusion algorithm is proposed by using an information filtering form (CIF) of cubature Kalman filter (CKF) and a noise de-correlation way. Based on the CIF and an augmented measurement, a centralized cubature Kalman fusion algorithm is firstly established and a problem on computational performance is pointed out. In order to improve computational performance of the centralized fusion method, the noise de-correlation technology is used to obtain a functionally equivalent Bearings-only tracking fusion system without the noise correlations, namely the variance of an augmented measurement noise is diagonalized. Accordingly, the proposed decentralized fusion method based on the CIF can be used to achieve a more effective tracking fusion estimate.

This paper presents automatic fuel consumption optimization with simultaneous keeping the recommended vehicle's speed. These tasks are closely related since a simple minimization of fuel consumption leads to stopping a vehicle. The proposed “double” optimization is performed online using combination of two controllers. The first of them is based on fully probabilistic design (FPD) under Bayesian methodology. It optimizes the “driver-vehicle” closed loop with the aim to save fuel and keep the recommended speed, using externally given setpoints. Optimized values serve as setpoints for PID controller, which provides necessary setpoint tracking. Research is performed in collaboration with Skoda auto ( www.skoda-auto.com ).

This paper presents a path-following control law that enables a multirotor, equipped with an autopilot tracking angular rates and thrust reference commands, to converge to and follow a three-dimensional path. The approach is based on the Special Orthogonal group SO(3) and allows for independent adjustment of the vehicle's speed profile in order to satisfy desired temporal specifications. Simulation results illustrate the efficacy of the proposed path-following control law.

In this paper, we develop a robust ℋ∞ stabilizer design method for a parallel structure of internal model-based tracking control for a Voice Coil Motor (VCM) driven servo gantry. The physical model of the VCM gantry is discussed and the system augmentation with the internal-model control structure and the exosystem immersion is generated, where the plant model uncertainties are taken into consideration as well. A mixed sensitivity optimization method is introduced for the synthesis of robust stabilizers. Simulations and real time experiments demonstrate good performance of the proposed method for high precision tracking of the servo gantry system.

The intelligent vehicles equipped with sensors may have an ability to recognize the environment and make a safe and efficient decision. This paper proposes the intervehicular sensor fusion system for tracking the nearby objects. The proposed system gathers information from nearby vehicles and returns more exact and precise information about the environment. The system is implemented by hierarchical joint probabilistic data association (JPDA) filter. In the first layer, the hierarchical JPDA filter tracks vehicles first. In the second layer, the obstacles are tracked using the measurements from the vehicles. This system improves the tracking performance by tracking targets which are not seen by an ego vehicle but seen by another vehicle. To verify the effect of the system, the simulation results are given.

In this paper, adaptive neural-network control is designed for an n-DOF robotic manipulator system. In the tracking control design, both uncertainties and input saturation are considered. Stability of the closed-loop system is analyzed via the Lyapunov's direct method. The uncertain system is approximated by the radial basis function neural-networks (RBFNN) and the input saturation is solved by adding an auxiliary signal. Simulation studies are conducted to examine the effectiveness of the proposed model-based and RBFNN control.

The aim of this work is to evaluate a vehicle tracking scheme as a means of monitoring traffic on roads. The scheme can be used as a component in a traffic monitoring system which can provide traffic management systems and road maintainers with traffic information. Vehicle tracking is achieved by determining vehicle position, velocity and magnetic moment using a nonlinear weighted least squares method (NWLS) on readings from two 3-axes magnetic sensors.The tracking was performed both in simulation and in real life. The traffic monitoring system is composed of two adjacently glue attached wireless sensor nodes, which are placed at a distance of 1 m along the road. A potential misalignment of the sensors due to placement errors is analysed in simulation and addressed. Godkänd; 2014; 20130918 (alikad)