This paper presents trajectory generation and output-feedback control strategies for an underactuated Flying Inverted Pendulum (FIP) system operating under unknown time-varying disturbances. The proposed approach introduces a novel scheme that employs two Extended High Gain Observers (EHGOs) to efficiently estimate the inverted pendulum’s linear and angular velocities, as well as the unknown disturbances, independently. The control strategy integrates a nonlinear hierarchical Lyapunov-based framework with EHGOs to tackle complex trajectory-tracking missions. Leveraging the differential flatness property of the FIP system, a versatile trajectory generation method is developed for window-crossing maneuvers. This work marks a step toward achieving closed-loop integration of trajectory generation and nonlinear control for the aggressive maneuvering of an underactuated FIP. The robustness and effectiveness of the proposed algorithms are validated through numerical simulations and experimental tests, using an underactuated aerial vehicle as a benchmark.

In this paper, we propose a novel event-triggered prescribed-time tracking (PTT) control method for an underactuated unmanned aerial vehicle (UAV). Unlike existing PTT control methods, where the transient behavior of the system depends solely on the controller gains, we design a novel reference error trajectory (RET) using a polynomial to guide the convergence of the tracking error, particularly during the transient phase. Based on this RET and within the backstepping framework, we design a thrust reference. Additionally, we introduce a novel event-triggered mechanism based on the tracking error to update the torque applied to the UAV. To streamline intricate mathematical expressions and improve robustness to external disturbances, a second-order linear system is used as a low-pass filter within the backstepping design. Finally, simulation results are provided to demonstrate the effectiveness of the proposed approach and validate the accuracy of the theoretical predictions.

An adaptive tracking control policy is investigated for uncertain nonlinear systems under the finite-time asymmetric output constraints (FTAOCs). Unlike common output constraints, FTAOCs are characterized as constraints that are initially imposed during system operation and are then removed after a certain time. To tackle this challenge, we have designed novel shift and barrier functions that transform FTAOCs into guarantees of boundedness for an auxiliary variable. Additionally, we have developed an adaptive estimation algorithm to estimate unknown parameters and proposed an adaptive control strategy. Simulation studies on the Resistance-inductance-capacitance (RLC) circuits have been conducted to demonstrate the feasibility of our proposal. In comparison with state-of-the-art methods, our algorithm offers the flexibility to simultaneously address both unconstrained and constrained requirements of nonlinear systems, without requiring revisions to the controller structure.

This paper investigates the tracking control of a multi-quadrotor slung-load system (MQSLS), incorporating a dynamic model that simultaneously accounts for underactuation, nonlinearity, dynamic coupling, and unmodeled dynamics. We introduce a novel force distribution algorithm, which bifurcates the cooperative control to two distinct components: slung-load position control; and cable configuration control along with quadrotor attitude control. Employing this strategy results in a cooperative controller that: (i) relaxes the constraints on cable configuration; and (ii) requires only up to the third time derivative of the reference trajectory. The proposed control scheme ensures almost asymptotic stability of the overall closed-loop error system under unknown constant disturbances affecting both quadrotors and payload. A comprehensive set of simulations and experimental results validates the effectiveness of the proposed control strategy.

This article proposes an event-triggered quantitative prescribed performance neural adaptive control method for autonomous underwater vehicles (AUVs). At kinematic level, to achieve a quantitative predetermined tracking performance without violating user-defined transient indices, a quantitative prescribed performance control (QPPC) scheme is devised, where the overshoot of the transient tracking response can be specified by a quantitative design relationship. To pursue a tradeoff between tracking accuracy and resource saving, a hybrid threshold-based event-triggered mechanism (HTETM) is designed and incorporated into the AUV controller design procedure. Additionally, a modified echo state neural network (MESNN) is employed for disturbance estimation, where intermittent system information produced by the HTETM is used for online learning, resulting in that both the communication data throughput between the controller and actuators and the online computational load can be diminished synchronously. Finally, a control law is devised at dynamic level to compensate for the triggered error induced by the aperiodic sampling of HTETM. Simulation results are provided and analyzed to validate the effectiveness of the proposed control strategy with application to an omni directional intelligent navigator.

We address the problem of trajectory tracking control of a quadrotor-slung-load system. A novel control framework is proposed that relaxes the differentiability class order of the reference. In lieu of traditional approaches that primarily regard the position of either the quadrotor or the load in the design of tracking controllers, in this work, we consider avirtualpoint located along the cable between the vehicle and the payload. The resulting control framework, which draws parallels with a typical single quadrotor dynamic system, is characterized by an utmost simplicity since it relies only on three error variables, and requires only up to the third time derivative of the reference trajectory. The overall control scheme ensures almost global asymptotic stability of the closed-loop system in the presence of constant external disturbances. Extensive experimental results featuring time-varying exogenous perturbations are presented to validate and illustrate the effectiveness of the proposed method.

Human-robot co-transportation allows for a human and a robot to perform an object transportation task cooperatively on a shared environment. This range of applications raises a great number of theoretical and practical challenges arising mainly from the unknown human-robot interaction model as well as from the difficulty of accurately model the robot dynamics. In this article, an adaptive impedance controller for human-robot co-transportation is put forward in task space. Vision and force sensing are employed to obtain the human hand position, and to measure the interaction force between the human and the robot. Using the latest developments in nonlinear control theory, we propose a robot end-effector controller to track the motion of the human partner under actuators' input constraints, unknown initial conditions, and unknown robot dynamics. The proposed adaptive impedance control algorithm offers a safe interaction between the human and the robot and achieves a smooth control behavior along the different phases of the co-transportation task. Simulations and experiments are conducted to illustrate the performance of the proposed techniques in a co-transportation task.

This article investigates a political party or an association social network where members share a common set of beliefs. In modeling it as a distributed iterative algorithm with network dynamics mimicking the interactions between people, the problem of interest becomes that of determining: 1) the conditions when convergence happens in finite time and 2) the corresponding steady-state opinion. For a traditional model, it is shown that finite-time convergence requires a complete topology and that by removing neighbors with duplicate opinions reduces in half the number of links. Finite-time convergence is proved for two novel models even when nodes contact two other nodes of close opinion. In a deterministic setting, the network connectivity influences the final consensus and changes the relative weight of each node on the final value. In the case of mobile robots, a similar communication constraint is present which makes the analysis of the social network so relevant in the domain of control systems as a guideline to save resources and obtain finite-time consensus. Through simulations, the main results regarding convergence are illustrated paying special attention to the rates at which consensus is achieved.

In this article, we address the task of cooperative path following control of multiple autonomous quadcopters in the presence of unknown external disturbances. Under the assumption that the communications among the vehicles are bidirectional and continuous, a synchronized path following strategy is proposed that regulates the speed of each vehicle along its path to reach consensus in relative position. Moreover, collision-free transient paths from the vehicle initial positions to a group of suitable selected positions along the desired paths are designed. Building on the backstepping technique, the proposed path following controller for each individual vehicle drives the quadcopter toward, and to stay within an arbitrarily small neighborhood of its corresponding desired path, achieving global uniformly ultimately boundedness. In addition, the devised controller guarantees that the actuations are bounded with respect to the position error. The controller is also made robust to external constant or slowly time-varying disturbances by the introduction of dynamic estimators for the disturbances. A projection operator is used to ensure that the estimates remain within the prescribed bounds and are sufficiently smooth to be backstepped. In order to validate the effectiveness and performance of the proposed methodology, we present and analyze both simulation and experimental results.

This paper addresses the landing problem of a vertical take-off and landing vehicle, exemplified by a quadrotor, on a moving platform using image-based visual servo control. Observable features on a flat and textured target plane are exploited to derive a suitable control law. The target plane may be moving with bounded linear acceleration in any direction. For control purposes, the image of the centroid for a collection of landmarks is used as position measurement, whereas the translational optical flow is used as velocity measurement. The proposed control law guarantees convergence to the desired landing spot on the target plane, without estimating any parameter related to the unknown height, which is also guaranteed to remain strictly positive. Moreover, convergence is guaranteed even in the presence of bounded and possibly time-varying disturbances, resulting, for example, from the motion of the target plane, measurement errors, or wind-induced force disturbances. To improve performance, an estimator for unknown constant force disturbances is also included in the control law. Simulation and experimental results are provided to illustrate and assess the performance of the proposed controller.

This work addresses the design and experimental evaluation of a robust controller for a quadrotor landing maneuver comprising the approach to a landing slope and sliding on that slope, before coming to a complete halt. During the critical landing flight phase the dynamics of the vehicle change with the type of contact with the ground and a hybrid automaton, whose states reflect the several dynamic behaviors of the quadrotor, is employed to model the vehicle throughout the complete maneuver. The quadrotor landing problem is broken down into separate maneuver generation and robust trajectory tracking problems, which are combined to achieve a successful maneuver that is robust to possible uncertainties. Experimental results are provided to attest the feasibility of the proposed landing procedure.

In this paper, we tackle the problem of trajectory tracking for a particular class of underactuated vehicles with full torque actuation and a single force direction (thrust) that is fixed relative to a body attached frame. Additionally, we consider that thrust reversal is not available. We present the design of a hybrid controller that, under some given assumptions, is able to globally asymptotically stabilize the vehicle to a reference position trajectory while minimizing the angle to the desired attitude trajectory. This objective is achieved robustly and globally, in the sense that small perturbations do not lead to instability and it is achieved regardless of the initial state of the vehicle. The algorithm is tested in a experimental setup, using a small scale quadrotor vehicle and the VICON motion capture system.

This paper presents an on-line three dimensional trajectory planner for an unmanned vehicle following a Leader vehicle, where the Follower remains at a specified relative position with respect to the Leader. The planner defines the trajectory for a virtual vehicle that will be used as reference for the real Follower vehicle. The virtual Follower behaves like a three dimensional trailer attached to the Leader and its reference frame is used to specify the relative position vector. At the kinematic level, the proposed planner requires only the knowledge of the Leader's linear speed, but not its angular speed, and the Follower's trajectory can be generated on-line, in the sense that no a priori knowledge of the Leader's trajectory is required. Experimental results obtained with quadrotor vehicles are presented, which demonstrate the richness of the planned trajectories.

This paper presents a localization solution for the study of tagged marine animals (sources) based on direction and Doppler measurements. The source localization, in 3-D, is supported by a Portable Underwater Robotic Tool (PURT), comprising a Ultra-Short Baseline (USBL) aided INS navigation system, carried by a diver (agent). Furthermore, a Surface Robotic Tool (SRT) provides the agent with its absolute position expressed in inertial coordinates. The observability of the system is studied and realistic simulation results are presented, in the presence of measurement noise.

This paper presents the design, analysis, and experimental validation of a globally asymptotically stable (GAS) filter for simultaneous localization and mapping (SLAM), with application to unmanned aerial vehicles. The SLAM problem is formulated in a sensor-based framework and modified in such a way that the underlying system structure can be regarded as linear time varying for observability analysis and filter design purposes, from which a linear Kalman filter with GAS error dynamics follows naturally. The performance and consistency validation of the proposed sensor-based SLAM filter are successfully assessed with real data, acquired indoors, using an instrumented quadrotor.

This paper proposes novel cooperative navigation solutions for an Intervention Autonomous Underwater Vehicle (I-AUV) working in tandem with an Autonomous Surface Craft (ASC). The I-AUV is assumed to be moving in the presence of constant unknown ocean currents, and aims to estimate its position relying on measurements of its range to the ASC and of its depth relatively to the sea level. Two different scenarios are considered: in one, the ASC transmits its position and velocity to the I-AUV, while in the other the ASC transmits only its position, and the I-AUV has access to measurements of its velocity relative to the ASC. A sufficient condition for observability and a method for designing state observers with Globally Asymptotically Stable (GAS) error dynamics are presented for both problems. Finally, simulation results are included and discussed to assess the performance of the proposed solutions in the presence of measurement noise.

The features that can be performed by an electric vehicle depend essentially on the availability of energy and capacity of energy storage system of the electric vehicle,. Being the energy storage designed according to the requirements of the electric traction system, many additional features are somewhat limited. However, in certain situations, the energy stored on board can be used for conventional electrical networks, without affecting the performance of the electric vehicle. This article addresses the reactive power compensation as a feature inherent to the vehicle-to-grid connection. In this paper, the battery set on board of the electric vehicle supplies the traction system and a single phase inverter, which is connected to an ordinary AC grid. The inverter is controlled in order to supply current to the AC grid in order to compensate the AC grid power factor. The performed analysis is based on the PQ theory getting the current component that should be driven by the single phase inverter. This compensation, at this stage only compensates the fundamental component of the current and neglects the effects of the harmonics. This study aims to show that the electric vehicles can include the power factor compensation feature, without degrading their performance.

This paper addresses the problem of robust take-off of a quadrotor unmanned aerial vehicle (UAV) in critical scenarios, such as in the presence of sloped terrains and surrounding obstacles. Throughout the maneuver, the vehicle is modeled as a hybrid automaton whose states reflect the different dynamic behaviors exhibited by the UAV. The original take-off problem is then addressed as the problem of tracking suitable reference signals in order to achieve the desired transitions between different hybrid states of the automaton. Reference trajectories and feedback control laws are derived to explicitly account for uncertainties in both the environment and the vehicle dynamics. Simulation results demonstrate the effectiveness of the proposed solution and highlight the advantages with respect to more standard open-loop strategies, especially for cases in which the slope of the terrain renders the take-off maneuver more critical to achieve.

This paper proposes an estimation algorithm for the determination of attitude installation matrix for laser detection and ranging systems (LADAR) mounted onboard autonomous vehicles, without requiring any prior knowledge on the terrain where the calibration mission is performed. The use of autonomous vehicles equipped with LADAR systems to conduct fully automatic surveys of terrain, infrastructures, or just to navigate safely in unknown environments, motivates the research on increasingly precise LADAR data acquisition and processing algorithms, for which the determination of the correct installation matrix is critical. The proposed methods rely on the minimization of the errors between several acquired data sets, by comparing each measured data set and a surface representation of the others. This error functional is minimized resorting to optimization tools for Riemannian manifolds enabling direct estimation of the installation matrix on the group of special orthogonal matrices SO(3). The proposed technique is extensively tested using simulated LADAR data sets under realistic acquisition conditions.

This paper presents an experimentally evaluated solution to the problem of estimating the attitude of a rigid body using rate gyros and a pan-tilt camera. A nonlinear attitude observer combines angular velocity measurements obtained from rate gyros with images of a planar scene provided by the camera. By directly exploiting the sensor information, a stabilizing feedback law is introduced, and exponential convergence to the origin of the estimation errors is shown. Additionally, an active-vision system is proposed that relies on an image-based exponentially input-to-state-stable control law for the pan and tilt angular rates of the camera to keep the features in the image plane. Using recent results in geometric numerical integration, a multirate implementation of the observer is proposed, which exploits the complementary bandwidth of the sensors. Practical considerations, such as the lens-distortion compensation and the computation of suitable observer feedback gains, are considered. Experimental results obtained with a high-accuracy motion rate table demonstrate the high level of performance attained by the proposed solution.

This paper presents the design and performance evaluation of a low-cost Attitude and Heading Reference System (AHRS) for autonomous vehicles. A single sensor pack, an Inertial Measurement Unit (IMU), provides all the data required to feed the attitude filter. The design is sensor-driven and departs from traditional solutions as no explicit representations of the attitude, e.g., Euler angles, quaternions, or rotation matrices, are considered in the filter design. Moreover, the proposed solution includes the estimation of rate gyros biases, systematic tuning procedures, and also allows for the inclusion of frequency weights to model colored noise on the different sensor channels. Due to its inherent structure, the filter is complementary, allows for temporary loss of sensor measurements, and also copes well with slowly time-varying rate gyros biases. The performance of the proposed algorithm is experimentally evaluated with a low-cost IMU and resorting to a high precision calibration table, which provides ground truth signals for comparison with the resulting filter estimates.

This paper addresses the problem of robust takeoff and landing control of a quadrotor UAV (Unmanned Aerial Vehicle). During the critical flight phases of takeoff and landing the vehicle dynamics change according to the possible existence of contact with the ground. To model the vehicle during the overall maneuver a hybrid automaton is used where each state corresponds to a different dynamic behavior exhibited by the UAV. The original takeoff and landing problems are then addressed as a problem of tracking suitable reference signals in order to achieve the desired transitions between different hybrid states of the automaton. Both reference trajectories and feedback control laws are derived to explicitly account for measurement noise and uncertainties, in both the environment and in the vehicle dynamics. Simulation results demonstrate the effectiveness of the proposed solution.

This paper addresses the design, development, and test of an integrated Ultra Short Baseline (USBL) and Inertial Navigation System (INS) to be used as a low cost navigation system for underwater robotic vehicles. An architecture for the open prototype is proposed, the acoustic array design and calibration is discussed, the inertial sensors package is presented, supporting acoustic signals to be used are briefly enumerated, and implementation issues are detailed. The system includes also as a by-product the design of a transponder that replies to the interrogations sent out by the integrated USBL/INS system. Preliminary sea tests, conducted in an harbor, are presented to assess the feasibility of the acoustic positioning system, using namely Direct Sequence Spread Spectrum (DSSS) coded signals.

This paper provides a methodology to model and execute coordinated missions involving multiple vehicles using Petri nets. Individual vehicle missions are defined by means of Petri nets and three constraints are added for coordination purposes: mutual exclusion, ordering and synchronisation. The proposed methodology generates a centralised net, checks if it is deadlock free and then obtains a decentralised Petri net for every vehicle minimising the communication between them. The resulting Petri nets implement the multi-vehicle mission control program that is responsible for coordinating in real-time the set of vehicles involved in the mission.

This paper presents the design and implementation of a mission control system (MCS) for an autonomous underwater vehicle (AUV) based on Petri nets. In the proposed approach the Petri nets are used to specify as well as to execute the desired autonomous vehicle mission. The mission is easily described using an imperative programming language called mission control language (MCL) that formally describes the mission execution thread. A mission control language compiler (MCL-C) able to automatically translate the MCL into a Petri net is described and a real-time Petri net player that allows to execute the resulting Petri net onboard an AUV are also presented.

A new sensor-based homing integrated guidance and control law is presented to drive an underactuated autonomous underwater vehicle (AUV) toward a fixed target, in three dimensions, using the information provided by an ultrashort baseline (USBL) positioning system. The guidance and control law is first derived using quaternions to express the vehicle's attitude kinematics, which are directly obtained from the time differences of arrival (TDOA) measured by the USBL sensor. The dynamics are then included resorting to backstepping techniques. The proposed Lyapunov-based control law yields global asymptotic stability in the absence of external disturbances and is further extended, keeping the same properties, to the case where constant known ocean currents affect the dynamics of the vehicle. Finally, a globally exponentially stable nonlinear TDOA and range-based observer is introduced to estimate the ocean current and uniform asymptotic stability is obtained for the overall closed-loop system. Simulations are presented illustrating the performance of the proposed solutions.

This paper proposes two estimation algorithms for the determination of attitude installation matrix for Laser Detection and Ranging systems (LADAR) mounted onboard autonomous vehicles. The use of autonomous vehicles equipped with LADAR systems to conduct fully automatic surveys of terrain, infrastructures, or just to navigate safely in unknown environments, motivates the research on increasingly precise LADAR data acquisition and processing algorithms, for which the determination of the correct installation matrix is critical. The proposed methods rely on the minimization of the errors between the measured data set and a representation of the real calibration surface. To minimize this error, two nonlinear optimization techniques are proposed, one that estimates the ZYX Euler angles and a second that uses optimization tools for Riemannian manifolds enabling direct estimation of the installation matrix on the group of special orthogonal matrices SO(3). The proposed techniques are extensively tested and their effectiveness compared resorting to simulated LADAR data sets under realistic noise conditions.

This paper presents a new ultra-short baseline (USBL) tightly-coupled integration technique to enhance error estimation in low-cost strap-down inertial navigation systems (INSs) with application to underwater vehicles. In the proposed strategy the acoustic array spatial information is directly exploited resorting to the extended Kalman filter implemented in a direct feedback structure. The determination and stochastic characterization of the round trip travel time are obtained resorting to pulse detection matched filters of acoustic signals modulated using spread-spectrum code division multiple access (CDMA). The performance of the overall navigation system is assessed in simulation and compared with a conventional loosely-coupled solution that consists of solving separately the triangulation and sensor fusion problems. From the simulation results it can be concluded that the proposed technique enhances the position, orientation, and sensors biases estimates accuracy