Abstract

Quadrotors are increasingly used nowadays as a mode of aerial transport during relief operations, payload delivery etc. However, in addition to higher inertia, large size payload may interfere with prop wash (displaced mass of air by the propeller), leading to unstable behaviour. On the other hand, using different quadrotors according to payload size is not feasible either economically or operationally. Therefore, in this work, we have developed a unique scissor-mechanism based reconfigurable quadrotor which can adapt to various sizes of payloads. A robust controller is also proposed to negotiate the parametric variations stemming from the reconfigurations in the system. The effectiveness of the proposed mechanism is studied extensively via comparative experiments.

Abstract

This work introduces a new single-stage adaptive controller for Euler-Lagrange systems with nonholonomic con- straints. The proposed mechanism provides a simpler design philosophy compared to double-stage mechanisms (that ad- dress kinematics and dynamics in two steps), while achieving analogous stability properties, i.e. stability of both original and internal states. Meanwhile, we do not require direct access to the internal states as required in state-of-the-art single-stage mechanisms. The proposed approach is studied via Lyapunov analysis, validated numerically on wheeled mobile robot dy- namics and compared to a standard double-stage approach

Abstract

The high maneuverability of fixed-wing un- manned aerial vehicles (UAVs) exposes these systems to several dynamical and parametric uncertainties, severely affecting the fidelity of modeling and causing limited guid- ance autonomy. This article shows enhanced autonomy via adaptation mechanisms embedded in the guidance law: a vector-field method is proposed that does not require a priori knowledge of the UAV course time constant, cou- pling effects, and wind amplitude/direction. Stability and performance are assessed using the Lyapunov theory. The method is tested on software-in-the loop and hardware-in- the-loop UAV platforms, showing that the proposed guid- ance law outperforms state-of-the-art guidance controllers and standard vector-field approaches in the presence of significant uncertainty. Index Terms—Adaptive guidance, adaptive sliding-mode control, fixed-wing unmanned aerial vehicles (UAV), un- known dynamics, vector field (VF).

Abstract

As compared to conventional adaptive control, robust adaptive control aims to provide robustness against unmodeled dynamics, coupling effects, and other endogenous and exogenous disturbances. Robust adaptive control has been an active research area over more than three decades, and has flourished in many application domains. Despite the advances, some bottlenecks still need to be circumvented for further progressing in the field. This special issue collects recent advances in robust adaptive control from theoretical and application perspectives. Theoretical aspects in robust adaptive control addressed in this special issue are:• Relaxing the persistence of excitation (PE) condition, standard in adaptive control and estimation: Katuyar, Roy, and Bhasin propose a novel condition, called finite persistence excitation (f-PE), milder than PE in terms of excitation requirement and online verifiability.

Abstract

Quadrotors have become extremely popular in various application scenarios which include disaster response, maintenance etc. A crucial challenge in terms of control design surfaces when a quadrotor has to operate under space constraints (e.g. pipeline inspection from inside, payload delivery at a precise location) under parametric perturbations and environmental disturbances (e.g. wind, gust). To deal with such scenarios, a suitable controller should simultaneously ensure tracking accuracy within predefined bounds to avoid constraint violation and tackle uncertainties. However, the state-of-the-art designs are either inapplicable for quadrotor system which is under-actuated in nature, or are unable to tackle system parametric uncertainties while being under full state (i.e. position, attitude angle, linear velocity and angular velocity) constraints. To address such issues in literature, a robust controller is introduced in …

Abstract

Quadrotors find a vast potential use in delivery and disaster relief operations. Control becomes critical in such sce- narios, especially when quadrotors have to manoeuvre through constrained spaces or deliver payloads at precise locations in the presence of external disturbances and parametric uncertainties stemming from uncertain payloads. Therefore, the controller has to guarantee a predefined tracking accuracy not to violate the state constraints. On the other hand, conventional fixed-valued state constraints are not suitable in many scenarios such as (i) initial offset being well beyond the expected accuracy, (ii) system dynam- ics experiencing significant transients due to the dropping of the payload. However, to the best of the authors’ knowledge, state- of-the-art controllers do not provide any solution for an underac- tuated system like a quadrotor when the system needs to honour time-varying constraints under uncertainties. This work proposes a controller for quadrotors which is robust against external distur- bances and parametric variations and guarantees a time-varying predefined position, velocity, attitude, and attitude-rate accuracy. The closed-loop system stability is established analytically, and the effectiveness of the proposed controller is validated experimentally compared to the state-of-the-art under a precision payload delivery scenario.

Abstract

Effective design of autopilots for fixed-wing unmanned aerial vehicles (UAVs) is still a great challenge, due to unmodeled effects and uncertainties that these vehi- cles exhibit during flight. Unmodeled effects and uncertain- ties comprise longitudinal/lateral cross-couplings, as well as poor knowledge of equilibrium points (trimming points) of the UAV dynamics. The main contribution of this article is a new adaptive autopilot design, based on uncertain Euler–Lagrange dynamics of the UAV and where the control can explicitly take into account under-actuation in the dy- namics, reduced structural knowledge of cross-couplings and trimming points. This system uncertainty is handled via appropriately designed adaptive laws: stability of the con- trolled UAV is analyzed. Hardware-in-the-loop tests, com- parisons with an Ardupilot autopilot and with a robustified autopilot validate the effectiveness of the control design, even in the presence of strong saturation of the UAV actua- tors.

Abstract

In recent times, quadrotors have become immensely applicable in scenarios such as relief operations, infrastructure maintenance, search-and-rescue missions etc. A key control design challenge arises in these applications when the quadrotor has to manoeuvre through constrained spaces such as narrow windows, pipelines in the presence of external disturbances and parametric uncertainties: such conditions necessitate the controller to guarantee predefined tracking accuracy so as to not violate the constraints and simultaneously tackle uncertainties. However, state-of-the-art controllers dealing with constrained system motion are not applicable either for an underactuated system like quadrotor or for an uncertain system dynamics. This work proposes a robust controller that enables the quadrotor to follow a trajectory with predefined tracking accuracy in constrained space as well as to tackle uncertainties stemming from imprecise system modelling and external disturbances. The closed-loop system stability is analysed via the Barrier Lyapunov approach and the effectiveness of the proposed controller is validated via simulation with state of the art.

Abstract

Artificial-delay control is a method in which state and input measurements collected at an immediate past time instant (i.e., artificially delayed) are used to compensate the uncertain dynamics affecting the system at the current time. This article formulates an artificial-delay control method with adaptive gains in the presence of nonlinear (Euler–Lagrange) underactuation. The appeal of studying Euler–Lagrange dynamics is to capture many robotics applications of practical interest, as demonstrated via stability and robustness analysis and via robotic ship and robotic aerial vehicle test cases.

Abstract

This article proposes an adaptive-robust maneuvering control framework for a planar snake robot under the influence of parameter uncertainties. The entire control objective of maneuvering control can be viewed as the simultaneous establishments of two goals: one to maintain a time-varying body shape of the snake robot for consistent motion (called the outer layer) and the other dealing with the velocity and head-angle tracking of the same (called the inner layer). Unknown variations in the ground friction coefficients have been considered to be the primary source of time-varying uncertainties which affects the control performance in both the layers. Accordingly, an artificial time delay-based adaptive-robust control (ARC) framework, dual adaptive-robust time-delayed control (ARTDC), is proposed. The term dual signifies simultaneous application of ARTDC for the outer as well as the inner layer. ARTDC comprises of two segments: an artificial time delay-based time-delayed estimation (TDE) part and an ARC part. While TDE approximates the completely unknown friction forces, the ARC tackles the approximation error arising from the TDE. More importantly, compared with the existing ARC methodologies, the proposed ARTDC neither presumes the overall uncertainty to be upper bounded by a constant nor requires any prior knowledge of the bound of uncertainty to implement the controller. A Lyapunov function-based method has been adopted for analyzing the stability of the closed-loop system. Simulation studies affirm the improved performance of the ARTDC in contrast to the classical artificial delay-based methodology.