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Thin-walled structures, i.e. the wing-upper-skin panels, are prone to buckling accompanied by a significantly large out-of-plane deflection. The computational efficiency of the conventional finite element based full-order method is not satisfactory for nonlinear buckling problems of the structure. In this work, the skin panels on the upper surface of the wing butt box are selected using a sub-modeling technique based on the nonlinear structural analysis. A reduced-order method is proposed to trace the geometrically nonlinear response of the single and double-curved skin panels in a stepwise manner. A predictor-corrector strategy is developed using the Koiter-perturbation-theory based reduced-order model and Newton iterations. The high-efficiency of the proposed method is validated by comparing the number of path-following steps and iterations with the full-order method. A fairly large step size is achieved to significantly reduce the computational cost in a nonlinear buckling analysis. The influence of the number of closely-spaced modes on the numerical accuracy and efficiency of the proposed reduced-order method is also studied. Different boundary condition, location, and thickness of the wing-upper-skin panel, are considered to further demonstrate the favorable performance of the proposed method. • Nonlinear buckling analysis is applied to skin panels with single and double-curved shapes on the wing upper surface. • A reduced-order method is adopted to trace geometrically nonlinear responses in predictor-corrector based stepwise manner. • Influence of the number of closely-spaced modes on numerical accuracy and efficiency of the proposed method is investigated. • Numerical accuracy and high-efficiency of the proposed method are validated for skin panels con- sidering various parameters. [ABSTRACT FROM AUTHOR]

This paper focuses on the output-feedback control for path-following of underactuated autonomous underwater vehicles subject to multiple uncertainties and unmeasured velocities. First, a novel extended state observer is proposed to estimate the mismatched lumped disturbance and recover the unmeasured velocities. Based on this premise, to overcome the limitation of relying solely on the accurate kinematic model, a disturbance observer-based stabilizing controller is developed. The difference in bandwidths between the observer and the vehicle dynamics allows for a mathematical setup amenable to standard singular perturbation theory. In the fast mode, a kinematic observer is designed to reject system uncertainty caused by unknown attack angular velocity and prohibitive path-tangential angular velocity, using a novel physical perspective. In the slow mode, an interconnected-system control law is proposed by integrating the backstepping technique with the time scale decomposition method. Furthermore, the stability of the overall closed-loop system is established. Finally, simulation results are presented to demonstrate the effectiveness of the proposed method for path-following of underactuated autonomous underwater vehicles in the vertical plane. • A novel ESO is developed to estimate the mismatched lumped disturbances and recover the unmeasured velocities by combining high-gain and singular perturbation techniques, leading to reduced complexity in stability analysis. • A DO is designed to estimate model uncertainties by utilizing the difference in bandwidths between the observer and state dynamics. The resulting singular perturbation analysis provides a new perspective on designing a DO in a straightforward manner. • An interconnected-system control law is proposed based on a time scale decomposition method to address the problem of the "explosion of terms" inherent in conventional backstepping. • The stability analysis enables the derivation of mathematical bounds on the control gains, offering guidance for selecting control gains to prevent controller ill-conditioning and/or closed-loop instability. [ABSTRACT FROM AUTHOR]

A snake robot should be robust to actuator failures because it has more actuators than required for locomotion. However, controlling a snake robot is problematic if some of the joints become passive because of problems in power supply lines. In a recent study, we proposed a controller to achieve path-following in cases where one of the joints is passive. However, the controller applies only to cases with a single passive joint. With two or more passive joints, it cannot be applied even if sufficient active joints remain. This paper considers the path-following control of a snake robot with passive joints by extending our previous study (the single passive joint case). The validity of the method is verified by numerical simulation. [ABSTRACT FROM AUTHOR]

The problem addressed in the present paper is the design of a controller based on an evolutionary neural network for autonomous flight in quadrotor systems. The controller's objective is to govern the quadcopter in such a way that it reaches a specific position, bearing on attitude limitations during flight and upon reaching a target. Given the complex nature of quadcopters, an appropriate neural network architecture and a training algorithm were designed to guide a quadcopter toward a target. The designed controller was implemented as a single multi-layer perceptron. On the basis of the quadcopter's current state, the developed neurocontroller produces the correct rotor speed values, optimized in terms of both attitude-limitation compliance and speed. The neural network training was completed using a custom evolutionary algorithm whose design put particular emphasis on the cost function's definition. The developed neurocontroller was tested in simulation to drive a quadcopter to autonomously follow a complex path. The obtained simulated results show that the neurocontroller manages to effortlessly follow several types of paths with adequate precision while maintaining low travel times. [ABSTRACT FROM AUTHOR]

This paper uses a line-following approach to study the longitudinal and lateral problems in vehicle platooning. Under this setup, we assume that inter-vehicle distance sensing is unreliable and propose a cooperative control strategy to render the platoon less vulnerable to these sensing difficulties. The proposed control scheme uses the velocity of the predecessor vehicle, communicated through a Vehicle-to-Vehicle technology, to avoid significant oscillations in the local speed provoked by tracking using unreliable local distance measurements. We implement the proposed control algorithm in the RUPU platform, a low-cost experimental platform with wireless communication interfaces that enable the implementation of cooperative control schemes for mobile agent platooning. The experiments show the effectiveness of the proposed cooperative control scheme in maintaining a suitable performance even when subject to temporal distortions in local measurements, which, in the considered experimental setup, arise from losing the line-of-sight of the local sensors in paths with closed curves. [ABSTRACT FROM AUTHOR]

To ensure that a liquid tank semi-trailer has good yaw stability and path-following performance on low-adhesion roads under turning conditions, a multi-object PID differential braking-control method is proposed, which takes the tractor yaw rate, semi-trailer yaw rate, and articulation angle as the control parameters. According to the principle of equivalence, the trammel pendulum (TP) model is used to simulate the liquid sloshing effect in the liquid tank, and the Fluent software is used to identify the key parameters of the trammel pendulum model and verify the correctness of its simulation effect. Then, a co-simulation model is built based on TruckSim and MATLAB/Simulink. Based on the simplified six degrees of freedom model and the co-simulation model of a liquid tank semi-trailer, a multi-object PID differential braking-control method is designed, and the vehicle state responses with and without control are compared when it is turning on a low-adhesion road. The simulation results show that the proposed multi-object PID differential braking-control method can effectively improve the yaw stability and path-following performance of the liquid tank semi-trailer when turning on a low-adhesion road. [ABSTRACT FROM AUTHOR]

Path-following techniques of ships have received a lot of attention in recent years, to promote future autonomous ships and develop advanced autopilots. This paper deals with the path-following problem of underactuated ships without having prior knowledge regarding the hydrodynamic coefficients and ship parameters. A novel data-driven control strategy that combines Koopman operator theory and extended dynamic mode decomposition (EDMD) method and integrates with a model predictive control (MPC) framework is proposed. It makes use of data collected from experiments to learn the Koopman eigenfunctions of unknown ship dynamics via supervised learning, which are utilized as the lifting functions in the EDMD method to build a linear, lifted state-space model. The identified linear model acts as the predictor in the designed MPC controller, and a line-of-sight (LOS) algorithm is introduced as the guidance law for path-following. Simulation results show that the prediction model could provide sufficient prediction accuracy, and that it can be combined with MPC to achieve good path-following performance in a computationally efficient way. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are aircraft that can be manually operated or autonomously guided through an autopilot. In the last case, the system is responsible for stabilising the aircraft and executing guidance tasks such as path-following. In literature, several path-following algorithms were proposed for straight lines and loiter paths. Previous comparisons generally consider straight line algorithms in a 2D space using the kinematic model of an aircraft. In order to complement existing research, this paper compares four 3D path-following algorithms for loiter paths under different wind intensities. Tests are made through Software-in-the-Loop (SiL) simulations using the dynamic model of a fixed-wing UAV. Furthermore, a genetic algorithm is employed to tune the parameters and the analysis is carried out under varying wind conditions. The algorithms compared are four well-known geometric methods: Carrot-Chasing (CC), Non-Linear Guidance Law (NLGL), Pure Pursuit and Line-of-Sight (PLOS) and Vector Field (VF). Results show that Vector Field has the smallest errors, while PLOS is the most resistant to wind disturbance. [ABSTRACT FROM AUTHOR]

This study presents a novel path planning and path-following system designed explicitly for real-world driving conditions in autonomous vehicles. The pipeline considers uncertainties in the dynamic parameters of the vehicle, which are common in real-world scenarios. To address these uncertainties, a robust multi-stage model predictive control approach is developed to generate optimal control outputs. A customized convolutional neural network, Nano-FastSCNN, quantized to half-precision, is employed for accurately segmenting left and right lane boundaries. To ensure that our visual planner is robust against adverse weather conditions, we have rigorously evaluated its generalization capabilities under challenging weather types. The centerline of these lanes is then selected as the desired path and transformed into the camera reference frame by leveraging the camera projection matrix. The controller utilizes this reference trajectory to guide the vehicle along the desired trajectory. To demonstrate real-time processing capabilities, the entire pipeline, including the vision system module and controller, is implemented in a hardware-in-the-loop setup utilizing three NVIDIA Jetson devices to demonstrate the scalability of our architecture. Comprehensive evaluations demonstrate that the pipeline achieves an inference speed of 15.6 FPS, even on the less powerful NVIDIA Jetson Nano. Moreover, the system successfully alleviates the detrimental effects of uncertainties and reliably follows the desired path. • Integrated CNN-based framework for versatile visual path planning and following. • Developing a customized quantized CNN for accurate and fast line segmentation. • HIL-validated pipeline on Jetson Nano to demonstrate real-time capability. • Utilizing robust multi-stage MPC to address vehicle dynamics uncertainties. [ABSTRACT FROM AUTHOR]

In this research, we offer a bifurcation analysis to describe impacting, stick, and grazing between a particle and the piston to better understand the nonlinear dynamics of a Fermi oscillator. The principles of hybrid dynamical systems will be utilized to explain the moving process in such a Fermi oscillator. The motion complexity and stick mechanism of such an oscillator are demonstrated using periodic and chaotic motions. We introduce the hybrid dynamical system and numerical continuation detection tool COCO. Physical and mathematical models are used to construct the obtained bounded mathematical model. The frequency, amplitude in oscillating base displacement, and the gap between the stationary boundary and the piston's equilibrium position are the major parameters. We employ path-following analysis to illustrate the bifurcation that leads to solution destabilization. From the viewpoint of eigenvalue analysis, the essence of period-doubling and Fold bifurcation is revealed. Numerical continuation methods are used to perform a complete one-parameter bifurcation analysis of the Fermi oscillator. The presence of codimension-1 bifurcations of limit cycles, like period-doubling, fold, and grazing bifurcations, is shown in this work. Then, the one-parameter continuation analysis is tracked in two-parameter bifurcation diagrams that include a second important factor of interest. Bifurcations cause all solutions to lose stability, according to our findings. The acquired results provide a better understanding of the Fermi oscillator mechanism and demonstrate that we may control the system dynamics by modifying the parameters. [ABSTRACT FROM AUTHOR]

In this paper, a novel path-following and obstacle avoidance control method is given for nonholonomic wheeled mobile robots (NWMRs), based on deep reinforcement learning. The model for path-following is investigated first, and then applied to the proposed reinforcement learning control strategy. The proposed control method can achieve path-following control through interacting with the environment of the set path. The path-following control method is mainly based on the design of the state and reward function in the training of the reinforcement learning. For extra obstacle avoidance problems in following, the state and reward function is redesigned by utilizing both distance and directional perspective aspects, and a minimum representative value is proposed to deal with the occurrence of multiple obstacles in the path-following environment. Through the reinforcement learning algorithm deep deterministic policy gradient (DDPG), the NWMR can gradually achieve the path it is required to follow and avoid the obstacles in simulation experiments, and the effectiveness of the proposed algorithm is verified. [ABSTRACT FROM AUTHOR]

This paper presents an adaptive neural fault-tolerant control algorithm for the path-following activity of the underactuated surface vehicle (USV) using the novel output-based triggering approach. In the algorithm, the event-triggered mechanism is designed utilizing the attitude states from the kinematics aspect of USV. Both the control inputs and the related calculation thread are implemented only at the trigger instants. Furthermore, neural networks (NNs) are employed to remodel the model uncertainties, and the adaptive observer is developed to estimate and compensate for the effect of the unknown actuator faults. With the direct Lyapunov theorem, the semi-global uniformly ultimately bounded (SGUUB) stability can be guaranteed for the closed-loop system in aspects of both the trigger instant and the continuous interval. The comparison experiment has been illustrated to verify the effectiveness of the proposed algorithm, which can effectively improve the information transmission performance and the fault-tolerant capability. [ABSTRACT FROM AUTHOR]

This paper proposes a novel control method for path-following of joints of a snake robot. The proposed method applies the port-controlled Hamiltonian based approach, which has originally been proposed for a full-actuated friction-less system. This paper extends the controller for an under-actuated system with friction. It is proven that, by the proposed controller, the path-following of the joints is achieved. The validity of the controller is checked through simulations. Furthermore, simulation results suggest that the controller is robust to modeling errors. [ABSTRACT FROM AUTHOR]

The problem addressed in the present paper is the design of a controller based on an evolutionary neural network for autonomous flight in quadrotor systems. The controller’s objective is to govern the quadcopter in such a way that it reaches a specific position, bearing on attitude limitations during flight and upon reaching a target. Given the complex nature of quadcopters, an appropriate neural network architecture and a training algorithm were designed to guide a quadcopter toward a target. The designed controller was implemented as a single multi-layer perceptron. On the basis of the quadcopter’s current state, the developed neurocontroller produces the correct rotor speed values, optimized in terms of both attitude-limitation compliance and speed. The neural network training was completed using a custom evolutionary algorithm whose design put particular emphasis on the cost function’s definition. The developed neurocontroller was tested in simulation to drive a quadcopter to autonomously follow a complex path. The obtained simulated results show that the neurocontroller manages to effortlessly follow several types of paths with adequate precision while maintaining low travel times.

This paper uses a line-following approach to study the longitudinal and lateral problems in vehicle platooning. Under this setup, we assume that inter-vehicle distance sensing is unreliable and propose a cooperative control strategy to render the platoon less vulnerable to these sensing difficulties. The proposed control scheme uses the velocity of the predecessor vehicle, communicated through a Vehicle-to-Vehicle technology, to avoid significant oscillations in the local speed provoked by tracking using unreliable local distance measurements. We implement the proposed control algorithm in the RUPU platform, a low-cost experimental platform with wireless communication interfaces that enable the implementation of cooperative control schemes for mobile agent platooning. The experiments show the effectiveness of the proposed cooperative control scheme in maintaining a suitable performance even when subject to temporal distortions in local measurements, which, in the considered experimental setup, arise from losing the line-of-sight of the local sensors in paths with closed curves.

The paper deals with a minimum potential energy algorithm considering the maximum deflection angle of joint (MPE-MDA algorithm) for three key factors including the maximum deflection angle of the joint, the total length of the path, and the minimum distance from obstacles. The algorithm is applied for space obstacle avoidance of hyper-redundant manipulators. On the path-following planning of the hyper-redundant manipulator, the MPE-MDA algorithm equates one link to multiple virtual small links and then derives the relationship between the length of the small links and the deflection angle of the small links. The planned path based on this relationship meets that the deflection angle of the link does not exceed the limited angle. The MPE-MDA algorithm resolves the problem that the traditional algorithm cannot limit the maximum deflection angle of the joint. Besides, the paper checks different paths by changing the step length, the direction of path planning, and other factors, and then selects the optimal path according to the evaluation function. Furthermore, by comparing the simulation results of path-following planning employing the MPE-MDA algorithm and the artificial potential field (APF) method, respectively. It is found that when the manipulator moves along the optimal path planned by the MPE-MDA algorithm, it can not only keep the specified distance from the obstacle but also can satisfy the condition that the deflection angle of the joint never exceeds the limited range, which verifies the reliability and effectiveness of the algorithm.

Maneuvering a truck-trailer system while docking is extremely challenging. This article aims to alleviate this problem by presenting an enhanced path-following control framework for autonomous semi-trailer docking. In the proposed system, adaptive controllers that utilize gain scheduling are introduced for forward and reverse path-following tasks in docking maneuvers to increase the robustness and path-following performance. The system includes an improved pure pursuit controller with adaptive look-ahead distance for forward path following; a cascade controller of reverse pure pursuit and a gain-scheduled LQ control for reverse path-following. In the evaluation of the path-following performance of forward and reverse controllers, the closed-loop system of path-following controllers with the truck-trailer kinematic model is simulated in MATLAB/Simulink for various test cases, and the results are compared with those of other studies. Furthermore, different docking scenarios are generated via the cascade path planning algorithm for autonomous semitrailer docking. These are tested with a high degree semi-trailer model within the IPG TruckMaker simulation environment, and with a full truck-trailer vehicle in the test field. The results of both simulations and physical testing clearly demonstrate improvements in terms of the control problem formulation, i.e., the stabilized path-following is obtained with acceptable path-following errors. [ABSTRACT FROM AUTHOR]

This article studies the design, modeling, and implementation of a path-following algorithm as a guidance, navigation, and control (GNC) architecture for an autonomous underwater vehicle (AUV). First, a mathematical model is developed based on nonlinear equations of motion and parameter estimation techniques, including the model validation based on field test data. Then, the guidance system incorporates a line-of-sight (LOS) algorithm with a combination of position PID controllers. The GNC architecture includes a modular and multi-layer approach with an LOS-based, path-following algorithm in the AUV platform. Furthermore, the navigation used in the path-following algorithm is developed based on a predefined coverage area. Finally, this study addresses simulation and field test control scenarios to verify the developed GNC architecture. [ABSTRACT FROM AUTHOR]

To ensure that a liquid tank semi-trailer has good yaw stability and path-following performance on low-adhesion roads under turning conditions, a multi-object PID differential braking-control method is proposed, which takes the tractor yaw rate, semi-trailer yaw rate, and articulation angle as the control parameters. According to the principle of equivalence, the trammel pendulum (TP) model is used to simulate the liquid sloshing effect in the liquid tank, and the Fluent software is used to identify the key parameters of the trammel pendulum model and verify the correctness of its simulation effect. Then, a co-simulation model is built based on TruckSim and MATLAB/Simulink. Based on the simplified six degrees of freedom model and the co-simulation model of a liquid tank semi-trailer, a multi-object PID differential braking-control method is designed, and the vehicle state responses with and without control are compared when it is turning on a low-adhesion road. The simulation results show that the proposed multi-object PID differential braking-control method can effectively improve the yaw stability and path-following performance of the liquid tank semi-trailer when turning on a low-adhesion road.

Purpose – Two-handed automobile steering at low vehicle speeds may lead to reduced steering ability at large steering wheel angles and shoulder injury at high steering wheel rates (SWRs). As a first step toward solving these problems, this study aims, firstly, to design a surface electromyography (sEMG) controlled steering assistance interface that enables hands-free steering wheel rotation and, secondly, to validate the effect of this rotation on path-following accuracy. Design/methodology/approach – A total of 24 drivers used biceps brachii sEMG signals to control the steering assistance interface at a maximized SWR in three driving simulator scenarios: U-turn, 90° turn and 45° turn. For comparison, the scenarios were repeated with a slower SWR and a game steering wheel in place of the steering assistance interface. The path-following accuracy of the steering assistance interface would be validated if it was at least comparable to that of the game steering wheel. Findings – Overall, the steering assistance interface with a maximized SWR was comparable to a game steering wheel. For the U-turn, 90° turn and 45° turn, the sEMG-based human–machine interface (HMI) had median lateral errors of 0.55, 0.3 and 0.2 m, respectively, whereas the game steering wheel, respectively, had median lateral errors of 0.7, 0.4 and 0.3 m. The higher accuracy of the sEMG-based HMI was statistically significant in the case of the U-turn. Originality/value – Although production automobiles do not use sEMG-based HMIs, and few studies have proposed sEMG controlled steering, the results of the current study warrant further development of a sEMG-based HMI for an actual automobile.

This paper studies the problem of spatial path-following control of underactuated autonomous underwater vehicles (AUVs) with multiple uncertainties and input saturation taken into account. Initially, the reduced-order extended state observes (ESOs) are introduced to estimate and compensate all lumped uncertainties due to the model parameters perturbations, unmodeled dynamics, environmental disturbances, and nonlinear hydrodynamic damping terms. Furthermore, the spatial path-following control strategy is established by combining with backstepping, integral sliding mode control, and estimator to cope with the position, attitude, and linear velocity tracking of the AUV. Specifically, the dynamic surface control (DSC) technique is utilized to achieve satisfactory differential performance and avoid the “explosion of complexity.” Additionally, the auxiliary dynamic compensator is presented to analyze the effect of input saturation, and the states of the auxiliary design system are used to develop the controller. It is easily proved that the proposed control scheme can guarantee that all signals of the closed-loop system are globally stable through the Lyapunov theorem, with the tracking errors converging to an arbitrarily small neighborhood of zero. Finally, the numerical simulation results are carried out to illustrate the effectiveness of the proposed controller.

In this paper, a novel path-following and obstacle avoidance control method is given for nonholonomic wheeled mobile robots (NWMRs), based on deep reinforcement learning. The model for path-following is investigated first, and then applied to the proposed reinforcement learning control strategy. The proposed control method can achieve path-following control through interacting with the environment of the set path. The path-following control method is mainly based on the design of the state and reward function in the training of the reinforcement learning. For extra obstacle avoidance problems in following, the state and reward function is redesigned by utilizing both distance and directional perspective aspects, and a minimum representative value is proposed to deal with the occurrence of multiple obstacles in the path-following environment. Through the reinforcement learning algorithm deep deterministic policy gradient (DDPG), the NWMR can gradually achieve the path it is required to follow and avoid the obstacles in simulation experiments, and the effectiveness of the proposed algorithm is verified.

The current study employs a novel nonlinear robust control approach for path-following control of underactuated autonomous underwater vehicles (AUVs) with multiple uncertainties in the vertical plane. Firstly, a nonlinear underactuated AUV model is established to characterize the dynamics of AUV and path-following error. To resolve dependence on a detailed model that appeared in previous studies, the unknown time-varying attack angular velocity in the dynamic model of the path-following error is considered as the kinematic uncertainty, while the linear superposition of the external environmental disturbances, the perturbations in the internal model parameters, and other unmodeled dynamics in the dynamic model is chosen as lumped dynamic uncertainties. Several reduced-order extended state observers (ESOs) are designed for estimating both of these uncertainties. Secondly, to reduce the impact of input saturation and avoid the “explosion of complexity” associated with traditional back-stepping method, a nonlinear track differentiator (NTD) is utilized to follow the virtual control signal and its derivative. Thirdly, the constructed reduced-order ESOs and NTD are adopted to establish an augmented back-stepping controller, where its ability to stabilize the overall system is demonstrated using the Lyapunov theorem. Finally, extensive simulations and analyses in various working conditions, including the nominal working condition without disturbances, the working condition with multiple uncertainties, and the conditions which better replicate the actual environment, are performed to demonstrate the effectiveness, superiority, and robustness of the designed controller.

The paper addresses path planning and path-following problems in an unknown complex environment for an underactuated autonomous underwater vehicle (AUV). The AUV is required to follow a given reference path represented as a sequence of smoothly joined lines and arcs, bypassing obstacles encountered on the path. A two-level control system is proposed with an upper level for event-driven path planning and a lower level for path-following. A discrete event system is designed to identify situations that require planning a new path. An improved waypoint guidance algorithm and a Dubins curves based algorithm are proposed to build paths that allow the AUV to avoid collision with obstacles and to return to the reference path respectively. Both algorithms generate paths that meet the minimum turning radius constraint. A robust parameter-varying controller is designed using sublinear vector Lyapunov functions to solve the path-following problem. The performance of the developed event-based control system is demonstrated in three different simulation scenarios: with a sharp-edged obstacle, with a U-shaped obstacle, and with densely scattered obstacles. The proposed scheme does not require significant computing resources and allows for easy implementation on board. [ABSTRACT FROM AUTHOR]

This article studies the design, modeling, and implementation of a path-following algorithm as a guidance, navigation, and control (GNC) architecture for an autonomous underwater vehicle (AUV). First, a mathematical model is developed based on nonlinear equations of motion and parameter estimation techniques, including the model validation based on field test data. Then, the guidance system incorporates a line-of-sight (LOS) algorithm with a combination of position PID controllers. The GNC architecture includes a modular and multi-layer approach with an LOS-based, path-following algorithm in the AUV platform. Furthermore, the navigation used in the path-following algorithm is developed based on a predefined coverage area. Finally, this study addresses simulation and field test control scenarios to verify the developed GNC architecture.

This paper presents a nonlinear, universal, path-following controller for Wheeled Mobile Robots (WMRs). This approach, unlike previous algorithms, solves the path-following problem for all common categories of holonomic and nonholonomic WMRs, such as omnidirectional, unicycle, car-like, and all steerable wheels. This generality is the consequence of a two-stage solution that tackles separately the platform path-following and wheels’ kinematic constraints. In the first stage, for a mobile platform divested of the wheels’ constraints, we develop a general paradigm of a path-following controller that plans asymptotic paths from the WMR to the desired path and, accordingly, we derive a realization of the presented paradigm. The second stage accounts for the kinematic constraints imposed by the wheels. In this stage, we demonstrate that the designed controller simplifies the otherwise impenetrable wheels’ kinematic and nonholonomic constraints into explicit proportional functions between the velocity of the platform and that of the wheels. This result enables us to derive a closed-form trajectory generation scheme for the asymptotic path that constantly keeps the wheels’ steering and driving velocities within their corresponding, pre-specified bounds. Extensive experimental results on several types of WMRs, along with simulation results for the other types, are provided to demonstrate the performance and the efficacy of the method.

We present an electrothermal drift–diffusion model for organic semiconductor devices with Gauss–Fermi statistics and positive temperature feedback for the charge carrier mobilities. We apply temperature-dependent Ohmic contact boundary conditions for the electrostatic potential and discretize the system by a finite volume based generalized Scharfetter–Gummel scheme. Using path-following techniques, we demonstrate that the model exhibits S-shaped current–voltage curves with regions of negative differential resistance, which were only recently observed experimentally. [ABSTRACT FROM AUTHOR]

The actual performance of model-based path-following methods for unmanned aerial vehicles (UAVs) shows considerable dependence on the wind knowledge and on the fidelity of the dynamic model used for design. This study analyzes and demonstrates the performance of an adaptive vector field (VF) control law which can compensate for the lack of knowledge of the wind vector and for the presence of unmodeled course angle dynamics. Extensive simulation experiments, calibrated on a commercial fixed-wing UAV and proven to be realistic, show that the new VF method can better cope with uncertainties than its standard version. In fact, while the standard VF approach works perfectly for ideal first-order course angle dynamics (and perfect knowledge of the wind vector), its performance degrades in the presence of unknown wind or unmodeled course angle dynamics. On the other hand, the estimation mechanism of the proposed adaptive VF effectively compensates for wind uncertainty and unmodeled dynamics, sensibly reducing the path-following error as compared to the standard VF. [ABSTRACT FROM AUTHOR]

• The paper presents an efficient computational method for economic nonlinear model predictive control, which is based on nonlinear programming (NLP) sensitivity update. • The novelty of the paper lies on the NLP sensivity method that is designed for a weaker constraint qualification namely, Mangasarian-Fromovitz constraint qualification (MFCQ). • The proposed method is demonstrated on a large-scale chemical process case example, consisting more than 10.000 decision variables. We present a sensitivity-based nonlinear model predictive control (NMPC) algorithm and demonstrate it on a case study with an economic cost function. In contrast to existing sensitivity-based approaches that make strong assumptions on the underlying optimization problem (e.g. the linear independence constraint qualification implying unique multiplier), our method is designed to handle problems satisfying a weaker constraint qualification, namely the Mangasarian-Fromovitz constraint qualification (MFCQ). Our nonlinear programming (NLP) sensitivity update consists of three steps. The first step is a corrector step in which a system of linear equations is solved. Then a predictor step is computed by a quadratic program (QP). Finally, a linear program (LP) is solved to select the multipliers that give the correct sensitivity information. A path-following scheme containing these steps is embedded in the advanced-step NMPC (asNMPC) framework. We demonstrate our method on a large-scale case example consisting of a reactor and distillation process. We show that LICQ does not hold and the path-following method is able to accurately approximate the ideal solutions generated by an NLP solver. [ABSTRACT FROM AUTHOR]

Economic efficient Autonomous Road Vehicles (ARVs) are invariably subjected to uncertainties and perturbations. Therefore, control of vehicle systems requires stability to withstand the effect of variations in the nominal performance. Lateral path-tracking is a substantial task of ARVs, especially in critical maneuvering and cornering with variable speed. In this study, a new controller on the basis of interval type-3 (T3) fuzzy logic system (FLSs) is designed. The main novelties and advantages are as follows. (1) The uncertainty is a main challenge in the path-following problem of ARVs. However, in the fuzzy-based approaches, the bounds of uncertainty are assumed to be known. However, in the our suggested approach, the bounds of uncertainties are also fuzzy sets and type-3 FLSs with online adaptation rules are suggested to handle the uncertainties. (2) The approximation errors (AEs) and perturbations are investigated and tackled by the compensators. (3) The bounds of estimation errors are also uncertain and are estimated by the suggested adaptation laws. (4) The stability is ensured under unknown dynamics, perturbations and critical maneuvers. (5) Comparison with the benchmarking techniques and conventional fuzzy approaches verifies that the suggested path-following scheme results in better maneuver performance.

We highlight an essential difference between path-following and reference-tracking for nonminimum phase systems. It is well known that in the reference-tracking, for nonminimum phase systems, there exists a fundamental performance limitation in terms of a lower bound on the L-2-norm of the tracking error, even when the control effort is free. We show that this is not the case for the less stringent path-following problem, where the control objective is to force the output to follow a geometric path without a timing law assigned to it. Furthermore, the same is true even when an additional desired speed assignment is imposed.

A modified path-following control system using the vector field method for an underactuated autonomous surface ship model is proposed in the presence of static obstacles. With this integrated system, autonomous ships are capable of following the predefined path, while avoiding the obstacles automatically. It is different from the methods in most published papers, which usually study path-following and obstacle collision avoidance, separately. This paper considers the coupled path following and collision avoidance task as a whole. Meanwhile, the paper also shows the heading control design method in the presence of static obstacles. To obtain a strong stability property, a nonlinear autopilot is designed based on the manoeuvring tests of the free-running ship model. The equilibrium point of the controller is globally exponentially stable. For the guidance system, a novel vector field method was proposed, and the proof shows the coupled guidance and control system is uniform semi-global exponentially stable (USGES). To prevent the obstacles near the predefined path, the proposed guidance law is augmented by integrating the repelling field of obstacles so that it can control the ship travel toward the predefined path through the obstacles safely. The repelling field function is given considering the obstacle shape and collision risk using the velocity obstacle (VO) algorithm. The simulations and ship model test were performed to validate the integrated system of autonomous ships.

A synthesis of recent progress is presented on a topic that lies at the heart of both structural engineering and non-linear science. The emphasis is on thin elastic structures that lose stability subcritically—without a nearby stable post-buckled state—a canonical example being a uniformly axially-loaded cylindrical shell. Such structures are hard to design and certify because imperfections or shocks trigger buckling at loads well below the threshold of linear stability. A resurgence of interest in structural instability phenomena suggests practical stability assessments require stochastic approaches and imperfection maps. This article surveys a different philosophy; the buckling process and ultimate post-buckled state are well-described by the perfect problem. The significance of the Maxwell load is emphasized, where energy of the unbuckled and fully-developed buckle patterns are equal, as is the energetic preference of localized states, stable, and unstable versions of which connect in a snaking load-deflection path. The state of the art is presented on analytical, numerical and experimental methods. Pseudo-arclength continuation (path-following) of a finite-element approximation computes families of complex localized states. Numerical implementation of a mountain-pass energy method then predicts the energy barrier through which the buckling process occurs. Recent developments also indicate how such procedures can be replicated experimentally; unstable states being accessed by careful control of constraints, and stability margins assessed by shock sensitivity experiments. Finally, the fact that subcritical instabilities can be robust, not being undone by reversal of the loading path, opens up potential for technological exploitation. Several examples at different length scales are discussed; a cable-stayed prestressed column, two examples of adaptive structures inspired by morphing aeroelastic surfaces, and a model for a functional auxetic material.

A new type of path-following method has been developed to steer marine surface vehicles along desired paths. Path-following is achieved by a new hyperbolic guidance law for straight-line paths and a backstepping control law for curved paths. An optimal controller has been improved for heading control, based on linear quadratic regulator (LQR) theory with nonlinear feedback control techniques. The control algorithm performance is validated by simulation and comparison against the requirements of International Standard IEC62065. Deviations are within the allowable range of the standard. In addition, the experimental results show that the proposed method has higher control accuracy.

This paper proposes a novel approach to the visual path following problem based on Nonlinear Model Predictive Control. Simplified visual features are extracted from the path to be followed. Then, aiming to calculate the control actions directly from the image plane, a regulatory model is obtained in the optimal control problem scope. For this purpose a Serret-Frenet system is placed in the center of camera's field of view and the optimal control actions generate velocity references to an inner loop embedded in the robot. Stability issues are handled through a classical method and a new approach based on constraints relaxation is proposed in order to guarantee feasibility. Experimental results with a nonholonomic platform illustrate the performance of the proposed control scheme. [ABSTRACT FROM AUTHOR]

This paper focuses on the controller design for path-tracking problem of autonomous ground vehicles (AGVs) by employing a multi-constraint nonlinear predictive control (NMPC) schema. It is aimed to improve the transient performance of the vehicle and to consider a rollover prevention criterion in the proposed method. The path-tracking problem is transformed into the yaw stabilization issue, and a feedback control law with input saturation is developed to decrease the steady-state errors. Furthermore, the yaw-rate signal is generated for the desired path-tracking performance. The major contributions of the present paper are, first, developing a neural network autoregressive with exogenous input system to assist in obtaining an accurate and explicit model in order to contribute to the control of the system over the prediction horizon; second, describing a Frenet–Serret differential geometry based path-following agenda and developing AGV dynamic model by incorporating the vehicle vertical mode of motion to prevent vehicle rollover during critical maneuvers, and finally, achieving an enhanced yaw stabilization and transient tracking performance considering saturated input signal by employing the proposed system identification algorithm. The effectiveness of the proposed control system is verified by comparing with the traditional NMPC method by employing a high fidelity CarSim/MATLAB framework. [ABSTRACT FROM AUTHOR]

In this paper, a three-dimensional (3-D) path-following control design for an underactuated stratospheric airship is proposed based on the parameterized path description. The desired attitude, velocity, and the path parameter updating law are obtained by employing the guidance-based path-following principle. The resultant system possesses a cascaded structure, which consists of a guidance loop, an attitude stabilization loop, and a velocity tracking loop. The stability analysis shows that the path-following error and all states of the closed-loop system are ultimately uniformly bounded. Simulation results demonstrate the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

Abstract In this work, the post-buckling analysis of thin-walled beams by using the Generalized Beam Theory (GBT) is presented. To this purpose, a geometrically nonlinear GBT finite element is developed by exploiting the features of mixed-stress GBT finite element formulation in the framework of the Implicit Corotational Method (ICM). The application of the ICM to GBT is briefly discussed and the details necessary to successfully transform the linear GBT finite element in its nonlinear counterpart are illustrated. The numerical results show how the proposed finite element can deal with global and distortional geometrically nonlinear phenomena, in good accordance with respect to 3D shell models. [ABSTRACT FROM AUTHOR]

Abstract This paper describes experiments and analysis of the complete post-buckling behavior of shallow geodesic lattice domes. Although individual members are straight, their geometric arrangement approximates a curved surface and typical behavior is highly nonlinear, including the possibility of sudden jumps in which there may be multiple discontinuous pops from one equilibrium configuration to another. A number of shallow domes were produced using a 3D printer and tested using a load cell and proximity laser sensor. In contrast to most previous studies, rather than pinned or semi-rigid joints, the lattices studied here incorporate moment-transmitting joints and clamped boundary conditions at the perimeter. Thus, flexure is the dominant mode of deformation under lateral loading and coupled instability behaviors are possible in practice. The complete load-displacement relationship and multiple equilibrium configurations are exhibited both in experiment and in simulation. The experimental data shows a close correlation with nonlinear finite element analysis using path-following techniques. A typical member (coupled) buckling behavior and its influence on the critical load are presented. Conclusions are drawn with respect to symmetry and geometric sensitivity. [ABSTRACT FROM AUTHOR]

The paper addresses path planning and path-following problems in an unknown complex environment for an underactuated autonomous underwater vehicle (AUV). The AUV is required to follow a given reference path represented as a sequence of smoothly joined lines and arcs, bypassing obstacles encountered on the path. A two-level control system is proposed with an upper level for event-driven path planning and a lower level for path-following. A discrete event system is designed to identify situations that require planning a new path. An improved waypoint guidance algorithm and a Dubins curves based algorithm are proposed to build paths that allow the AUV to avoid collision with obstacles and to return to the reference path respectively. Both algorithms generate paths that meet the minimum turning radius constraint. A robust parameter-varying controller is designed using sublinear vector Lyapunov functions to solve the path-following problem. The performance of the developed event-based control system is demonstrated in three different simulation scenarios: with a sharp-edged obstacle, with a U-shaped obstacle, and with densely scattered obstacles. The proposed scheme does not require significant computing resources and allows for easy implementation on board.

This paper deals with actuation planning and control for active tensegrity structures. Within actuation planning, a specific actuation in the form of adjustments of cables' rest lengths is assigned to the resulting structure to follow a prescribed complex trajectory. The methodology is based on the desired trajectory discretisation and following consecutive actuation optimisation, which employs the dynamic relaxation method. Since the actuation planning problem is treated as static, a controller is designed to suppress possible structural vibrations. With regard to the need to test dynamical properties and simulate the structure's motion, a methodology for automatic creation of tensegrity computational models in Simscape software is developed in the MATLAB scripting environment. Numerical examples of particular tensegrity structures show the whole algorithm's efficiency. • New actuation planning method for the analysis and design of active tensegrity. • Prevents from overuse of any active members and overstressing of the structure. • Convenient approach that discovers reachability of structure's workspace. • General method which is applicable to a wide range of tensegrity structures. • Optional H2 controller enables further enhancement of control performance. [ABSTRACT FROM AUTHOR]

This paper proposes a finite-time output feedback methodology for the path-following task of marine surface vessels. First, a horizontal path-following model is established with unknown sideslip angle, unmeasured system state and system uncertainties. A hierarchical control structure is adopted to deal with the cascade property. For kinematics system design, a finite-time sideslip angle observer is first proposed, and thus the sideslip angle estimation is compensated in a nonlinear line-of-sight (LOS) guidance strategy to acquire finite-time convergence. For the heading control design, an extended state observer is introduced for the unmeasured state and equivalent disturbance estimation, based on which an output feedback backstepping approach is proposed for the desired tracking of command course angle. The global stability of the cascade system is analyzed. Simulation results validate the effectiveness of the proposed methodology.

The present work studies the geometrically nonlinear response of class θ = 1 tensegrity prisms modeled as a collection of elastic springs reacting in tension (strings or cables) or compression (bars), under uniform uniaxial loading. The incremental equilibrium equations of the structure are numerically solved through a path-following procedure, with the aim of modeling the mechanical behavior of the structure in the large displacement regime. Several numerical results are presented with reference to a variety of physical models, which use two different materials for the cables and the bars, and show different aspect ratios associated with either “standard” or “expanded” configurations. An experimental validation of the predicted constitutive response is conducted with reference to a “thick” and a “slender” model, observing rather good theory vs. experiment matching. The given numerical and experimental results highlight that the elastic response of the examined structures may switch from stiffening to softening, depending on the geometry of the system, the magnitude of the external load, and the applied prestress. The outcomes of the current study confirm previous literature results on the elastic response of minimal tensegrity prisms, and pave the way to the use of tensegrity systems as nonlinear spring units forming tunable mechanical metamaterials.

Studies of self-controlled practice have shown benefits when learners controlled feedback schedule, use of assistive devices and task difficulty, with benefits attributed to information processing and motivational advantages of self-control. Although haptic assistance serves as feedback, aids task performance and modifies task difficulty, researchers have yet to explore whether self-control over haptic assistance could be beneficial for learning. We explored whether self-control of haptic assistance would be beneficial for learning a tracing task. Self-controlled participants selected practice blocks on which they would receive haptic assistance, while participants in a yoked group received haptic assistance on blocks determined by a matched self-controlled participant. We inferred learning from performance on retention tests without haptic assistance. From qualitative analysis of open-ended questions related to rationales for/experiences of the haptic assistance that was chosen/provided, themes emerged regarding participants’ views of the utility of haptic assistance for performance and learning. Results showed that learning was directly impacted by the frequency of haptic assistance for self-controlled participants only and view of haptic assistance. Furthermore, self-controlled participants’ views were significantly associated with their requested haptic assistance frequency. We discuss these findings as further support for the beneficial role of self-controlled practice for motor learning.