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To obtain more accurate depth information with stereo cameras, various learning-based stereo-matching algorithms have been developed recently. These algorithms, however, are significantly affected by textureless regions in indoor applications. To address this problem, we propose a new deep-neural-network-based data-driven stereo-matching scheme that utilizes the surface normal. The proposed scheme includes a neural network and a two-stage training strategy. The neural network involves a feature-extraction module, a normal-estimation branch, and a disparity-estimation branch. The training processes of the feature-extraction module and the normal-estimation branch are supervised while the training of the disparity-estimation branch is performed unsupervised. Experimental results indicate that the proposed scheme is capable of estimating the surface normal accurately in textureless regions, leading to improvement in the disparity-estimation accuracy and stereo-matching quality in indoor applications involving such textureless regions. [ABSTRACT FROM AUTHOR]

This paper proposes two different approaches for estimating grade and bank angles for arbitrary vehicle-trailer configurations independent from road friction conditions: model-based and Machine Learning (ML) approaches. The model-based method employs unknown input observers on a vehicle-trailer roll/pitch dynamic model with fault thresholds. In the proposed ML approach, a Recurrent Neural Network (RNN) with long-short term memory gates is designed to estimate the road angles. The inputs of the RNN have been selected based on the vehicle-trailer roll and pitch dynamic models, and are normalised by the vehicle wheel-base, mass, and centre of gravity height so that the network is modularly applicable to different trailer types. The simulation and experimental test results justify the performance of the proposed road-bank and grade-angle estimation scheme in various cases and demonstrate that both bank and grade angles can be estimated with high accuracy. [ABSTRACT FROM AUTHOR]

Vehicle-trailer systems have various unstable modes including trailer snaking, jack-knifing, and roll-over, which should be considered in their stability control. For stability control design purposes, various techniques have been proposed to estimate vehicle-trailer system states and parameters. Some of these techniques rely on vehicle kinematic/dynamic models while others are data-driven and do not require a model. This review paper provides a comprehensive overview of different model-based and non-model-based techniques/algorithms developed for estimating vehicle-trailer system states and parameters. The main features, limitations, and assumptions for each estimation method are discussed. The trailer parameter estimation feasibility is also investigated for different possible vehicle-trailer on-board sensor settings. This paper can be used as a review and reference resource for engineers working in vehicle with semi-trailer state estimation and safety systems. [ABSTRACT FROM AUTHOR]

In this paper, ultra-sonic sensors along with kinematics and dynamics equations of a towing vehicle are used to develop three approaches for hitch angle estimation. The first approach is based on direct calculation of hitch angle using certain a priori geometric information and distance measurements of four ultra-sonic sensors. An angle estimate is generated for each of the six possible sensor-pair combinations, and these six estimates are passed through a voting algorithm to produce a single estimate. As the second and third approaches, kinematic and dynamic models of the tractor-trailer system are used to develop least-squares and Kalman filtering based recursive hitch angle estimations. A more reliable hitch angle estimation scheme is then proposed as the integration of the algorithms developed following each of the three approaches via a switching data fusion logic. It is shown that the proposed integrated hitch angle estimation scheme can be used for any ball type box trailer with a flat or symmetric V-nose frontal face without any priori information on the trailer parameters. Based on the validity of the assumptions, the proposed scheme can estimate the hitch angle in both low-speed using the kinematic model, and high-speed using the dynamic model of the tractor-trailer system. Experimental results corroborate the algorithms in estimating the hitch angle estimates in various cases conducted in this study. [ABSTRACT FROM AUTHOR]

Using a 3-degree-of-freedom lateral dynamic vehicle-trailer system model with the LuGre tyre model, this paper proposes a constrained unscented transformation based scheme for estimating the lateral velocity of a vehicle trailer. It is shown that the proposed lateral velocity estimation scheme is robust to the road condition. An affine quadratic stability approach is used to analyze the stability of the proposed estimation scheme. Simulations studies in CarSim/Simulink along with experimental tests are used to validate the proposed scheme. The test results confirm the accuracy of the developed estimation scheme and convergence of the vehicle-trailer lateral velocity estimate to the actual value. [ABSTRACT FROM AUTHOR]

This paper investigates the formation control of connected autonomous vehicle (CAV) platoons moving in multi lanes using distance‐based formation control techniques based on rigid graphs and V2V communication. A hierarchical architecture is proposed to decompose the cooperative control into velocity planning and vehicle dynamic control. A new velocity planning method is first developed via a distributed distance‐based formation controller so that each vehicle can keep platoon and change lane. Then, for the vehicle dynamics with nonlinearities and bounded disturbances, an adaptive controller is designed for regulating driving/braking torque to achieve the longitudinal velocity output of the velocity planner. The steering controller is designed to adjust the yaw angle of each vehicle to track and change lanes. Furthermore, stability analysis is conducted based on the Lyapunov theory. Finally, the applications of the proposed control designs to various of automated highway system (AHS) scenarios including lane‐change, curve lane and platoon overtaking, are simulated and numerically analysed to validate the effectiveness of theoretical results. [ABSTRACT FROM AUTHOR]

Trailer mass is one of the important trailer parameters that affects the stability of the tractor-trailer systems. In this paper, two different approaches are proposed to estimate trailer mass for arbitrary tractor-trailer configurations; dynamic system model-based and Machine Learning (ML) approaches. The stability of the dynamic system model-based estimation algorithm is analyzed, establishing the convergence of the estimation error to zero. In the proposed ML-based approach, a Deep Neural Network (DNN) is designed to estimate trailer mass. The inputs of the ML-based method have been selected based on the tractor-trailer dynamic model, and are considered to be normalized by the tractor mass, tire sizes, and geometry so that re-training of the network is not needed for different towing vehicles. The simulation and experimental results justify the accuracy of the trailer mass estimation in various cases and demonstrate that the trailer mass can be estimated with less than 10% error. [ABSTRACT FROM AUTHOR]

Cable-driven parallel robots (CDPRs) provide fast motions over considerably large workspaces, however, they suffer from undesired vibrations. Kinematically constrained CDPRs (KC-CDPRs) are CDPRs that are designed intentionally to have certain kinematic constraints, which help minimizing undesired modes of motion, enhancing stiffness, and hence establishing robustness to external disturbances. This article focuses on the vibration regulation of KC-CDPRs using minimal number of actuators. Considering a generic vibration model with six mode shapes, the effects of configuration of a set of four attachable actuators on energy dissipation of a KC-CDPR system are investigated. Accordingly, a measure is developed to quantify each configuration's capacity of vibration suppression in all six degrees of freedom (DoFs) all over the KC-CDPR workspace. In order to demonstrate the use of the proposed measure, five different actuator configurations are analyzed in detail. Moreover, an optimal proportional–derivative (PD) controller is designed to minimize the maximum settling time of the vibration signals. The proposed actuator configuration effectiveness measure, the provided comparative analysis of configurations, and the proposed optimal PD controller are evaluated and verified to be effective via an experimental KC-CDPR setup, for a planar warehousing case study of vibration regulation in all six DoFs using only four actuators. Comparison of the experimental results of the proposed controller with ${H}_{\infty }$ -based vibration controllers of CDPR demonstrates the superior characteristics of the proposed control technique. [ABSTRACT FROM AUTHOR]

A novel cyber physical method is proposed and experimentally verified for reliable distributed estimation of vehicle longitudinal velocity, robustly to road friction condition variations. In this method, the vehicle speed estimated at each of the four corners of the vehicle, using a linear parameter-varying observer in the physical layer, and speed data measured by a conventional low-cost GPS are incorporated in a distributed structure (in the cyber layer) to enhance the reliability of the estimate. The method minimizes a cost function quantizing the effect of disturbances on each corner's estimation and adversaries due to occasional GPS signal drops. A fault-tolerant estimation policy is integrated to deal with large deviations in corner estimations, which have unexpectedly high levels of confidence. The main advantages of the proposed method are increased reliability on various road surface conditions and robustness to faults, as confirmed by road tests. Several experimental tests, including lane change and low-excitation maneuvers, with various powertrain configurations on dry and slippery roads demonstrate the efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a type-2 fuzzy adaptive sliding mode control scheme for tracking control of hypersonic aircraft with uncertainties. This method uses full-state feedback to linearize the nonlinear model of hypersonic aircraft. Combining the interval type-2 fuzzy approach and adaptive sliding mode control keeps the system stable in the existence of uncertain parameters. For rapid stabilization of the system, the adaptive laws are designed using a direct constructive Lyapunov analysis together with a well-established type-2 fuzzy logic control. Simulation test results indicate that the proposed control scheme provides enhancement of robustness to parametric uncertainty and improvement in tracking performance of the hypersonic aircraft. [ABSTRACT FROM AUTHOR]

This paper designs a fault-tolerant adaptive controller for air-breathing hypersonic vehicles (AHVs) subject to modelling parameter uncertainties, external disturbances, and actuator nonlinearities of saturation and backlash. The proposed adaptive control scheme is able to compensate the effects of actuator saturation by utilising the states of five auxiliary dynamic systems, which are driven by the differences between the nominal and saturated input signals. Additionally, the effects of control surfaces on the aerodynamic force and moment, which are commonly neglected by the existing adaptive control designs, can be well handled. Transient tracking performance is explicitly derived in terms of norms of the tracking errors. The final control scheme is obtained in a direct form, which makes its implementation more practical compared with other adaptive controllers for AHVs. The effectiveness of the proposed control scheme is demonstrated by numerical simulations. [ABSTRACT FROM AUTHOR]

Reliable estimation of vehicle speed is an active topic of research in the automotive industry and academia due to its technical challenges as well as applications to vehicle traction and stability control. In this direction, the emergence of new generations of communication technologies has brought new perspectives to traditional studies on vehicle speed estimation and control. To this end, this paper introduces a cooperative vehicle speed fault diagnosis and correction algorithm. The distributed part of the algorithm is based on a distributed function calculation algorithm for vehicle networks. The introduced algorithm enables each vehicle to gather some information from other vehicles in the network in a distributed manner and is robust to communication failures. A procedure to use such information for a single vehicle to diagnose and correct a possible fault in its own speed estimation/measurement is discussed. The functionality and performance of the proposed algorithms are verified via illustrative examples and simulation results. [ABSTRACT FROM AUTHOR]

This paper presents a performance‐guaranteed adaptive back‐stepping design for a class of nonlinear systems with uncertainties and disturbances. To circumvent the increasing complexity caused by the repeated analytic differentiations in back‐stepping, sliding mode differentiation technique is employed to estimate the derivative of the virtual control. Compared with the well‐known command filtered back‐stepping, no compensating signal is required. Besides, time‐varying parameters, system uncertainties and external disturbances are compensated using nonlinear damping technique, while the output tracking error is regulated in the prescribed range with the adjustable convergence speed and steady‐state error. As a verification example, this method is applied to the longitudinal control of an air‐breathing hypersonic vehicle configured with the variable geometry inlet. [ABSTRACT FROM AUTHOR]

An opinion dynamics approach is proposed to enhance the reliability of the vehicle velocity estimators, which are required for autonomous driving as well as advanced vehicle active safety systems, such as traction and stability control. The corners’ estimates of a velocity observer, which is formed by combining the kinematic and model-based estimation schemes, are used as opinions with different levels of confidence in the developed algorithm. This is to find more reliable estimates robust to disturbances and time delay via solving a convex optimization problem. To bypass the effect of failure in velocity estimation, a fault rejection policy is used concurrently with the opinion dynamics. Road tests confirm the validity and robustness of the algorithm on slippery and dry roads independent of the powertrain configuration in different driving scenarios, especially for combined-slip and low-excitation maneuvers, which are demanding for the current vehicle state estimators. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, we exploit an adaptive control scheme to adjust highly sensitive oscillations of fluid conveying carbon nanotube (CNT) resonators. Firstly, we focus on the nonlinear vibrations of the fluid conveying CNTs, considering an added mass using nonlocal Euler‐Bernoulli beam theory. CNT rests on nonlinear Winkler and Pasternak foundations. We use the Galerkin method to extract the nonlinear ordinary differential equation models of the CNT oscillations. We elicit a linear parametric model for estimating the added mass and other parameters of the system. Numerical simulations delineate that the developed model has sensitivity to added mass at the yoctogram level. It is known that CNT vibrations are very sensitive to small perturbations. Accordingly, a small perturbation results in significantly abrupt changes in the vibrational parameters of the targeted system. For that reason, it is crucial to have a potent apparatus for identifying the system parameters in case of sudden changes in the vibrational parameters. For such parameter identification, a least squares (LS) parameter identification algorithm and an extended Luenberger observer are integrated to a pole placement controller for online estimation of the system parameters as well as vibration control of the objective system. It is well‐known that CNTs are potentially ideal atomic force microscopy (AFM) probes, and accordingly, the proposed method is potentially beneficial for identifying highly sensitive motions in AFM. In addition, numerical simulations are presented, showing that the proposed adaptive controller has the potential to be used for vibration control of the CNT resonators even in the case of chaotic motions. [ABSTRACT FROM AUTHOR]

In this paper, we present the cooperative driving system developed by the Chalmers car team for the grand cooperative driving challenge 2016. This paper gives an overview of the system architecture and describes in detail the communication, signal processing, and decision-making sub-systems. Experimental results demonstrate the system’s performance and operation according to the rules and requirements of the competition. [ABSTRACT FROM PUBLISHER]

Trajectory tracking and vibration control of redundant cable-driven parallel robots (CDPR) attracted the attention of different researchers; however, the effects of the CDPRs end-effectors disturbing force/moment on the undesired vibrations and minimization of such effects has received few attention. A new general dynamic modeling and robust control structure for redundant CDPR is proposed in this paper, which evaluates such factors and tries to minimize their effects on the performance of CDPRs. Considering the effects of redundant cables on the CDPRs’ stiffness improvement, we neglect the second and higher order terms of motion errors that helps to separate the moving platform's undesired vibration equation from its desired (nominal) equation of motion. The obtained vibration model forms a linear parametric variable (LPV) dynamic system that lets us investigate the effects of external disturbances on the undesired vibration in different directions, and use a wide class of well-established robust and optimal vibration control techniques. Consequently, in order to minimize the effect of disturbances on the moving platform trajectory tracking performance, a robust and optimal vibration compensator is designed using the $LPV-H_\infty$ control design techniques. Based on the platform's desired trajectory, the proposed control scheme calculates the cables’ nominal tension required for trajectory tracking and further generates the auxiliary signals to augment these tensions to counteract the undesirable vibrations. Based on the obtained dynamic model, the developed approach is simplified for kinematically-constrained redundant CDPRs as a new actuation method with restricted rotational degrees of freedom. Via numerical analysis and experimental tests of a planar kinematically-constrained CDPR, the proposed modeling and robust control design approach are examined and compared with other common control design methodologies. Moreover, the effectiveness of kinematically-constrained actuation method in vibration elimination and control design simplification of CDPRs is demonstrated. [ABSTRACT FROM PUBLISHER]

This paper presents longitudinal and lateral velocity estimators by considering the effect of the suspension compliance (SC) at each corner (tire) for ground vehicles. The estimators are developed to be resilient to sensor measurement inaccuracies, model and tire parameter uncertainties, switchings in observer gains, and measurement failures. More particularly, the stability of the observer is investigated, and its robustness to road condition uncertainties and sensor noises is analyzed. The sensitivity of the observers’ stability and performance to the model parameter changes is discussed. Moreover, the stability of the velocity observers for two cases of arbitrary and stochastic switching gains is investigated. The stochastic stability of the observer in the presence of faulty measurements is also studied, and it is shown that if the probability of a faulty measurement occurring is less than a certain threshold, the observer error dynamics will remain stochastically stable. The performance of the observer and the effect of the SC are validated via several road experiments. [ABSTRACT FROM PUBLISHER]

We consider a graph-theoretic approach to the performance and robustness of a platoon of vehicles, in which each vehicle communicates with its $k$ -nearest neighbors. In particular, we quantify the platoon’s stability margin, robustness to disturbances (in terms of system \mathcal {H}_{\infty } norm), and maximum delay tolerance via graph-theoretic notions, such as nodal degrees and (grounded) Laplacian matrix eigenvalues. The results show that there is a trade-off between robustness to time delay and robustness to disturbances. Both first-order dynamics (reference velocity tracking) and second-order dynamics (controlling inter-vehicular distance) are analyzed in this direction. Theoretical contributions are confirmed via simulation results. [ABSTRACT FROM PUBLISHER]

In this paper, we consider the adaptive attitude control of spacecraft with time-varying inertial properties. We use a coordinate independent approach for the purpose of designing the control and estimation laws in terms of the rotation matrices representing the spacecraft body frame and reference tracking signals. This method helps to overcome the difficulties regarding the attitude representation on SO(3) such as ambiguities associated with quaternion representation and inherent singularities inside Euler parameters. We model the time variations in inertial parameters in two different ways, and design adaptive control schemes for each case. As the first uncertain dynamic model, we consider a setting of spacecraft with multiple moving appendages, and based on this model, design an adaptive control scheme with three different versions, where Frobenious norm is used in measuring the deviation of the estimated inertia tensors from their actual values. The proposed adaptive control scheme is later extended for the more direct model where the inertia tensor of the spacecraft has a nonlinear relation with the norm of the input moment. Further, we derive the allowable sets of initial conditions to ensure the convergence of the tracking error. Simulation results are provided to illustrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

This paper develops a general model for kinematically-constrained redundant cable-driven parallel robots (CDPRs), and studies stiffness improving effects and redundancy resolution of such robots aiming stiffness optimization to minimize their undesired perturbations under external disturbances in a desired direction. In the developed model, assuming an axially flexible model for the cables, motion equation is derived. Considering the role of constrained cables in restriction of CDPR's rotational degrees of freedom, the vibration equation of the moving platform is separated from the equation of motion. The resulted vibration equation is a linear dynamic system with a stiffness matrix formed by the cables’ tension and the constrained cables’ axial stiffness. Based on that, the substantial effects of constrained cables and the potential effects of cables’ tension on the stiffness improvement of CDPRs are shown. Accordingly, the cables’ tension redundancy problem is formulated. Redundancy resolution is studied considering the directional stiffness of the moving platform as the objective function to maximize. This objective function is derived as a linear function of cables’ redundant tensions and the corresponding redundancy problem solved by using a time-efficient method of linear programming. The developed model and the proposed redundancy resolution approach are experimentally tested on an actual warehousing robot to maximize its translational stiffness. Comparison of theoretical and experimental results demonstrates the validity of the proposed optimization approach and the effectiveness of kinematically-constrained actuation method. [ABSTRACT FROM PUBLISHER]

Driver-in-the-loop stability is a central issue in vehicle control systems. However, since a general human behavior model to explore it in a quantitative fashion has been lacking, little is known about how the vehicle can be controlled while considering the driver effects. Indeed, applying a control method without considering the driver effects, and instead separating human level and machine dynamic layers, guaranteeing stability of a vehicle, is impossible. Here, a new formulation of the problem that involves a driver model and a linear vehicle model is proposed. Given that practical controllers usually do not have access to the future road preview data, this information is also modeled in terms of bounded uncertainties. The design allows the tools of robust control to stabilize the system, offering an implementable approach to overcome ranges of delay and uncertainties of closed-loop modeling due to the human presence. The formulation can further deepen the understanding of the effects of a driver during vehicle steering. To evaluate the proposed controller, a nonlinear full vehicle model along with a driver model in CarSim are used. The simulations performed for a standard harsh double-lane-change scenario under different driver and vehicle conditions demonstrate that vehicle stability is enhanced using the proposed controller. [ABSTRACT FROM PUBLISHER]

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