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In this work, we consider a cooperative system in which UAVs perform long-distance missions with assistance of unmanned ground vehicles (UGVs) for battery charging. We propose an autonomous landing scheme for the UAV to land on a mobile UGV with high precision by leveraging multiple-scale Quick Response (QR)-codes for different altitudes. These QR-codes support the acquisition of the relative distance and direction between the UGV and UAV. As a result, the UGV is not required to report its accurate state to the UAV. In contrast to the conventional landing algorithms based on the global positioning system (GPS), the proposed vision-based autonomous landing algorithm is available for both outdoor and GPS-denied scenarios. To cope with the challenge on the moving platform landing, a quadratic programming (QP) problem is formulated to design the velocity controller by combining a control barrier function (CBF) and a control Lyapunov function (CLF). Finally, both extensive computer simulation and a prototype of the proposed system are shown to confirm the feasibility of our proposed system and landing scheme.

We present a new integrated guidance and control method for autonomous collision avoidance and navigation in an unmapped GPS-denied environment that contains unknown obstacles. The algorithm is implemented on an experimental custom quadrotor that uses onboard vision sensing (i.e., an Intel RealSense R200) to detect the positions of obstacles. We demonstrate autonomous collision avoidance and destination seeking in experiments, where the quadrotor navigates unknown GPS-denied environments. All feedback measurements are obtained from onboard sensors. The new guidance and control algorithm uses a nonlinear inner-loop attitude controller; a nonlinear middle-loop velocity controller; and an ellipsoidal-potential-field outer-loop guidance algorithm for collision avoidance and destination seeking. The main analytic result regarding the inner-loop control shows that every quadrotor attitude with pitch between $$\pm 90^{\circ }$$ is a locally exponentially stable equilibrium of the closed-loop attitude dynamics, and we quantify the region of attraction for each attitude equilibrium.

Pure pursuit algorithm is one of the most effective ways of path tracking in autonomous vehicles. Nevertheless, the tracking accuracy of the existing pure pursuit algorithm is limited by the look-ahead distance. In this paper, to improve the tracking accuracy of the pure pursuit algorithm, a novel pure pursuit algorithm based on the optimized look-ahead distance named OLDPPA is proposed. Four improvements are presented in OLDPPA. Firstly, to find a better look-ahead distance of pure pursuit algorithm, salp swarm algorithm (SSA) is used in pure pursuit algorithm. Secondly, Brownian motion, a random motion mechanism of particles, is introduced in SSA to enhance its exploitation and exploration capabilities. Thirdly, to accelerate the convergence speed of SSA, a weighted mechanism which uses two different weights in the search process to adjust the salps closer to the food source quickly is assigned. Based on innovations 2 and 3, adaptive Brownian motion salp swarm algorithm (ABMSSA) is proposed and applied to pure pursuit algorithm. Finally, a velocity controller which outputs the speed of the next moment according to the distance and time interval between the look-ahead point and the current vehicle position is designed in OLDPPA, to ensure that the vehicle reaches its destination at a specified time. To verify the effectiveness and efficiency of OLDPPA, OLDPPA is applied in four different paths and the corresponding results are compared with other pure pursuit algorithms that use different look-ahead distances. Experimental results show that the tracking accuracy of OLDPPA is better than other algorithms.

Providing the operator with a good view of the remote site is of paramount importance in aerial telemanipulation. In light of that, this paper proposes the application of a hierarchical control framework in order to tackle the problem of adjusting the field of view of an on-board camera as a secondary task. The proposed approach ensures that the flying base, and consequently the camera, can be steered in order to provide a distant operator with a desired field of view without disturbing the end-effector pose. The approach is focused on aerial manipulators with torque-controlled arms, like the DLR Suspended Aerial Manipulator (SAM), while allowing the base to be directly torque-controlled or, alternatively, through an inner-loop velocity controller. Quantitative, qualitative, and real-scenario experimental validation is carried out using the SAM and confirms the need for such an approach and its efficacy in achieving decoupled field-of-view control.

Low-velocity servo systems consisting of harmonic reducers can be controlled by using a double velocity controller structure, which needs velocity feedback from both the motor side and load side. To improve the performance of Low-velocity servo systems, this paper propose a new velocity observer model by applying a simple Butterworth filter, which uses an observer in the system for the velocity feedback, building a new velocity observer model to improve the estimation accuracy. Experimental results show that our method is more stable than traditional method, leading to an improved performance.

Precision landing is critical for rapid launching and recovering of unmanned aerial vehicles in ship-based scenarios and space-limited environments. The solution presented here uses a deep convolutional network based vision pipeline to detect the landing mark and estimate system state in real-time. The landing sequence is controlled by vehicle trajectory points using a linear predictive algorithm. The trajectory points are fed to a low level position controller and velocity controller. Both controllers demonstrate satisfactory performance and achieve < 0.3-meter accuracy with the platform moving up to 80% of maximum UAV velocity.

On this paper, a robust velocity controller applied to a three-phase squirrel-cage induction motor under variable load conditions is designed. The induction motor drives an induction generator representing the load which freely delivers the generated power to the utility grid. The closed-loop scheme is designed at $\alpha \beta $ coordinate frame and is based on the linearization block control technique in combination with the super-twisting algorithm. The controlled output variables are the angular mechanical velocity and the square modulus of rotor flux linkages. The motor reference velocity is set up by a pulse train above the synchronous velocity and, consequently, the impelled induction machine operates in generator mode; meanwhile, the reference of rotor flux square modulus varies according to load condition of the induction motor. In order to estimate the non-measurable variables, both a rotor flux linkages observer and a load torque observer are designed. For the first one, a non-linear state observer using first order sliding modes is applied at $\alpha \beta $ coordinate frame and, for the second one, a Luenberger reduced asymptotic observer is used. The validation of the robustness for the proposed velocity controller is performed in a real-time experiment using a work-bench.

Autonomous landing is the most difficult maneuver in a quadrotor mission because of challenges like (i) external disturbances causing deviation from the desired trajectory resulting in a crash and (ii) inaccuracy of GPS based landing due to localization errors. In this paper, we use onboard vision to detect and estimate the landing pad coordinates accurately. We develop a novel closing velocity controller and integrate it with pure pursuit guidance law to achieve faster and accurate landing in 3D as compared to traditional vertical landing approaches. We test the efficacy of the proposed controller for stationary and moving landing pad through simulations and validate the controller through outdoor experiments for a stationary landing pad.

Background Modern prosthetic hands are typically controlled using skin surface electromyographic signals (EMG) from remaining muscles in the residual limb. However, surface electrode performance is limited by changes in skin impedance over time, day-to-day variations in electrode placement, and relative motion between the electrodes and underlying muscles during movement: these limitations require frequent retraining of controllers. In the presented study, we used chronically implanted intramuscular electrodes to minimize these effects and thus create a more robust prosthetic controller. Methods A study participant with a transradial amputation was chronically implanted with 8 intramuscular EMG electrodes. A K Nearest Neighbor (KNN) regression velocity controller was trained to predict intended joint movement direction using EMG data collected during a single training session. The resulting KNN was evaluated over 12 weeks and in multiple arm posture configurations, with the participant controlling a 3 Degree-of-Freedom (DOF) virtual reality (VR) hand to match target VR hand postures. The performance of this EMG-based controller was compared to a position-based controller that used movement measured from the participant’s opposite (intact) hand. Surface EMG was also collected for signal quality comparisons. Results Signals from the implanted intramuscular electrodes exhibited less crosstalk between the various channels and had a higher Signal-to-Noise Ratio than surface electrode signals. The performance of the intramuscular EMG-based KNN controller in the VR control task showed no degradation over time, and was stable over the 6 different arm postures. Both the EMG-based KNN controller and the intact hand-based controller had 100% hand posture matching success rates, but the intact hand-based controller was slightly superior in regards to speed (trial time used) and directness of the VR hand control (path efficiency). Conclusions Chronically implanted intramuscular electrodes provide negligible crosstalk, high SNR, and substantial VR control performance, including the ability to use a fixed controller over 12 weeks and under different arm positions. This approach can thus be a highly effective platform for advanced, multi-DOF prosthetic control.

This paper addresses the problem of velocity tracking control for various fully-actuated robotic vehicles. The presented method, which is based on transformation of equations of motion allows one to use, in the control gain matrix, the dynamical couplings existing in the system. Consequently, the dynamics of the vehicle is incorporated into the control process what leads to fast velocity error convergence. The stability of the system under the controller is derived based on Lyapunov argument. Moreover, the robustness of the proposed controller is shown too. The general approach is valid for 6 DOF models as well as other reduced models of vehicles. Simulation results on a 6 DOF indoor airship validate the described velocity tracking methodology.

This paper proposes the center distributed embroidery machine structure with 1,500 RPM, 52 heads for productivity and large sized embroidery goods. The synchronous velocity controller is adopted for control of the 2-axis distributed embroidery machine and the DOB(Disturbance Observer) is also adopted for minimizing disturbances caused by needle cams. For driving experiments of 2-axis center distributed driving structure, two conventional 26 heads 1,500RPM embroidery machines are used. It was shown that the center distributed driving structure with 2-axis synchronous control can be one way for implementing a large embroidery machine.

A chewing robot based on a six-bar linkage is developed to reproduce a variety of human chewing trajectories in 2-D space. In order to mimic human chewing behaviors for the purpose of objective measurements of food texture changes during chewing and preparation of food bolus, a compliance control is considered for the robot. In this paper, the dynamics of the robot is derived and validated through both simulations and experiments. Food models of peanuts and marshmallow are developed to simulate the food force-compression profiles. The compliance control of the robot is realized by a combination of an adaptive fuzzy velocity controller and a fuzzy adaptation mechanism for type of foods. By means of experimental comparisons between the chewing velocity and force results with food models, it is found that the compliance control proposed is able to control the chewing velocity in adapting to the food texture. The chewing robot developed is a valuable potential tool for evaluating food properties and bolus preparation in the field of food sciences.

This paper addresses the stability analysis on S Plane Control in terms of both position and velocity control. Employing Lyapunov stability theory and T-passivity theory, this paper proves the stability of the position controller based on S Plane Control, and on this ground, the stability analysis of the velocity controller based on S Plane Control is done. Finally, the S Plane Control results obtained from the sea trials are given.

In chemical, medical or biology laboratories Fume Hoods are basic facilities which can protect researchers from dangerous gas as blowing the contaminated air outside. After the air inside the laboratory room is sucked into the hume hood, then, it is blew out by a fan rotated by an AC induction motor. In addition, a damper controls the inside opening of a duct, which the air flows through. The face velocity, air velocity through the front door, have to be kept constant as the set value even though the opening of the door is varied. However, conventional fume hood used to be operated by operator`s manual switches. So that, in this paper an automatic control system is developed which controls the face velocity by adjusting the rotating speed of the blow motor and the opening of the damper. Experiments show that this developed system can be used at such laboratories.

In consider of docking task of AUV and underwater moving platform, an AUV tracking controller is designed. In this controller, target trajectory is calculated to determine AUV expected velocity and heading angle, during which, Lyapunov stability law is followed. An AUV heading angle and velocity controller is designed based on active disturbance rejection controller (ADRC) considered the perturbation of system model establishment and external disturb. The controller performed well, as is proved in the simulation experiment, that target trajectory is tracked effectively by trajectory tracking controller.

This article presents a model-based velocity controller able to induce a chaotic motion on n-degrees of freedom flexible joint robot manipulators. The proposed controller allows the velocity link vector of a robot manipulator to track an arbitrary, chaotic reference vector field. A rigorous theoretical analysis based on Lyapunov’s theory is used to prove the asymptotic stability of the tracking error signals when using the proposed controller, which implies that a chaotic motion is induced to the robotic system. Experimental results are provided using a flexible joint robot manipulator of two degrees of freedom. Finally, by using Poincaré maps and Lyapunov exponents, it is shown that the behavior exhibited by the robot joint positions is chaotic.

This paper proposed an ECAM (Electronic cam) control system which has simple and general structure. The proposed cam controller adopted the linear and polynomial curve-fitting method to generates a smooth cam profile curve function. Smooth motion trajectory of master actuator guarantees the good performance of slave motion and has an important effect on the interpolation quality of ECAM. The auto-tuning PID velocity controller was applied to overcome the uncertainties in ECAM, and the gains of the controller are updated continuously to ensure the consistency of system performance under varying working conditions. The robustness of system against the varying load torque disturbances and noises is guaranteed by using the load torque disturbance observer to suppress the disturbance on master actuator. The velocity compensator was applied to compensate the degradation of performance of slave motion caused from the varying driving speed of master motion. The stability and validity of the proposed ECAM control system was verified by simulation results.

This paper introduces the dynamic inversion with the Lyapunov function method. This is for a guidance and control system of a chase UAV with the velocity controller which is designed based on a dynamic inversion approach. The guidance and control system employs the pure pursuit guidance to determine the UAV's orientation, and a two-time scale dynamic inversion technique to control the attitude. All together, they enable the UAV to keep formation flight.

This paper presents a framework to deal with outdoor navigation using an AR.Drone Parrot quadrotor. The proposed system runs in a centralized computer, the ground station, responsible for the communication with the unmanned aerial vehicle (UAV) and for synthesizing the control signals during flight missions. The outdoor navigation is performed through using a layered control architecture, where a high-level control algorithm, designed from the kinematic differential equations describing the movement of the UAV, is used to generate reference signals for a low-level velocity controller. To feedback the controllers, the sensorial data provided by the AR.Drone onboard sensors and a GPS module are fused through a Kalman Filter, allowing getting a more reliable estimate of the UAV state. Finally, experimental results are presented, which demonstrate the effectiveness of the proposed framework.

This paper addresses the problem of path following for underactuated MSVs with dynamical uncertainty and time-varying ocean disturbances. The path is made up of straight-line segments connected by n waypoints. A robust adaptive path following strategy is proposed based on a LOS guidance scheme and a novel velocity controller. Specifically, the velocity controller employs an NN to identify the dynamical uncertainty and time-varying ocean disturbances. The predictor errors are utilized to construct the novel iterative updating laws for NN weights. Using a Lyapunov-Krasovskii functional, it is proven that the underactuated MSV is able to follow a piecewise linear path, and tracking errors converge to a small neighborhood of the origin. Simulation result is given to show the effectiveness of the proposed method.

An adaptive dynamic surface control scheme is proposed for a class of longitudinal dynamics of a hypersonic flight vehicle with unknown input nonlinearity, which comprises dead-zones and sector nonlinearities. The dynamic inversion velocity controller and the dynamic surface attitude controller are proposed, the difficulty caused by the input nonlinear has been overcome. The uncertain nonlinear functions in the flight vehicle model are approximated by neural networks. Moreover, only one parameter needs to be updated online at each step, the computation burden and the explosion of complexity are greatly reduced. It is proved that all signals of the closed-loop system are uniformly ultimately bounded. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

A velocity control approach based on the incremental backstepping (IB) is proposed to enhance the accuracy of the velocity control for the small unmanned aerial vehicles (SUAVs).The velocity control model is first formulated based on the SUAV movement equations and the thrust model. Then, the IB method is applied to the velocity control law design. Feedback of acceleration eliminates sensitivity to model mismatch and other uncertainties. The use of Lyapunov analysis guarantees system stability. A thrust model and a velocity controller, based on IB, are formulated in a SUAV. The proposed method has been verified effective in the simulation and flight tests conducted by the authors.

In this study, first, the hierarchical vehicle control system of a mobile robot moving along a wall is proposed. The hierarchical vehicle control system consists of a desired signal generator of wheel velocities using two laser displacement sensors and a wheel velocity controller. The wheel velocity controller consists of PI control and disturbance observer. Then, the numerical simulations and the experiments have been carried out, and the high-precision trajectory tracking control performance of the present hierarchical vehicle control system is confirmed.

Null space velocity control is essential for achieving good behaviour of a redundant manipulator. Using the dynamically consistent pseudo-inverse, the task and null space motion and forces are decoupled. The paper presents a globally stable null space velocity controller and the gradient projection technique in conjunction with the dynamically consistent pseudo-inverse. The physical meaning and influence of the compensation terms in null the space velocity controller are explained. The performance of the proposed null space controller is tested on 4. d.o.f planar redundant manipulator interacting with the environment.

Currently, the achievable feedback control performance for servo-screw-presses is limited by its elastic structure. A possible approach to compensate this limitation is the extension of the velocity controller. This structural extension has the potential to improve the controller performance significantly due to a better damping of low frequencies. In this paper, the extension of the control structure by a state space feedback in the velocity control loop is examined. Considering that, the velocity control extension is investigated at a test rig which consists of a servo axis and a two mass system. Furthermore, a simulation model for the test rig is designed. The results of the simulated and experimentally obtained results of the test rig are presented and evaluated in order to make a prediction for the transferability to the simplified model of the servo-screw-press.

We present a control framework for robot systems to perform dynamic manipulation tasks, with robotic batting as an example. A properly preplanned trajectory for manipulators is recognized to be important to increase the probability of success. A nominal path for a manipulator to travel in a swing motion is computed while optimizing the stochastic dynamic manipulability measure to cope with the unpredictable deviation of the ball. A nominal trajectory along the path is then planned in a minimum-time fashion for the benefit of catching the ball as accurately as possible, the behavior of which is observed visually and estimated on-line. A control structure consisting interpolatively of a path velocity controller to follow the nominal trajectory and a resolved-acceleration-natured ball-deviation compensating controller, together with a ball motion prediction, is proposed. The effectiveness of the proposed approach is examined using computer simulations. The strategy provided in this paper will be useful to ma...

To establish empirical verification of a stabilizing controller for nonholonomic systems, the authors implement a hybrid control concept on a 2-DOF mobile robot. Practical issues of velocity control are also addressed through a velocity controller which transforms the mobile robot to a new system with linear and angular velocity inputs. Experiments in the physical meaning of different controller components provide insights which result in significant improvements in controller performance. >

Motion control usually includes the precise control of position/velocity and acceleration control while accurate acceleration control is not useful in many applications. Traditional position/velocity control is derived by feedback based, PID controllers. However the disturbance observer further increases the stability and robustness of the controller. In this paper we assess the effectiveness of the Disturbance Observer working together with a tuned PID controller. Velocity response is compared with and without the disturbance observer. The proposed disturbance observer based velocity controller produce better results than the traditional velocity controller.

International audience; We study an important class of tracking problems for unmanned air vehicles (UAVs) with input constraints. The states give the position with respect to an inertial coordinate system, the heading angle, and the inertial velocity. The controls are the commanded heading angle and inertial velocity. We design controllers that give global tracking control. Two key features are the bounds on our controllers and our proof that the tracking is input-to-state stable with respect to additive actuator errors on the velocity controller, under a restriction on the perturbation that respects airspeed constraints. We allow a wide class of reference trajectories that satisfy a nondegeneracy condition on a weighted sum of the reference velocity and reference acceleration. We illustrate our work in simulations.