Abstract

This paper examines the enhancement of quadrotor efficiency through adaptive control to address the critical need for Fault-Tolerant Control (FTC) in quadrotors amidst operational uncertainties and component inefficiency. State-of-the-art adaptive FTC strategies often assume the uncertainties to be bounded by a constant a priori; however, imprecise knowledge of inertial system parameters lead to state-dependent uncertainties which do not follow such assumption. Remain unattended, statedependent uncertainties can lead to instability, especially under actuator faults. The proposed adaptive FTC offers actuator fault mitigation while tackling unknown (statedependent) uncertainties via suitably designed adaptive laws. Additionally, real-time fault detection and control allocation are used simultaneously to avoid conservative control application. The closed-loop system stability is studied analytically and the effectiveness of the proposed solution is verified on a realistic simulator in comparison to the 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

In this article, a novel adaptive trajectory tracking controller is designed for a payload-carrying spacecraft under full state constraints. The proposed controller can tackle state-dependent uncertainties without a priori knowledge of their structures and upper bounds. The controller ensures time-varying constraints on all states and their time derivatives. The closed-loop stability of the proposed scheme is verified analytically via the Lyapunov method, and real-life experiments using a robotic testbed validated the effectiveness of the proposed adaptive controller over the state-of-the-art.

Abstract

In this work we propose a new practical synchronization protocol for multiple Euler Lagrange (EL) systems without structural linear-in-the-parameters (LIP) knowledge of the uncertainty and where the agents can be interconnected before control design by unknown state-dependent interconnection terms. This setting is meant to overcome two standard a priori assumptions in the literature concerning uncertainty with LIP structure and absence of interaction among agents before designing the synchronization protocol. To overcome these assumptions, we propose an adaptive distributed control mechanism having the purpose of estimating the coefficients of the resulting state-dependent uncertainty structure.

Abstract

In this article, a novel adaptive controller is designed for Euler-Lagrangian systems under predefined time-varying state constraints. The proposed controller could achieve this objective without a priori knowledge of system parameters and, crucially, of state-dependent uncertainties. The closed-loop stability is verified using the Lyapunov method, while the overall efficacy of the proposed scheme is verified using a simulated robotic arm compared to the state of the art.

Abstract

Reconfigurable robots provide an attractive option for cleaning tasks, thanks to their better area coverage and adaptability to changing environment. However, the ability to change morphology creates drastic changes in the reconfigurable robot dynamics, and existing control design techniques do not take this into account. Neglecting configuration changes can lead to performance degradation and, in the worst scenarios, instability. This paper proposes to embed the changes arising from reconfiguration in the control design, via a switched uncertain Euler-Lagrangian model. Accordingly, a novel switched adaptive design is proposed for trajectory tracking. Closed-loop stability is assured using the multiple Lyapunov function framework, and the design is implemented and validated on a self-reconfigurable pavement cleaning mobile robot (PANTHERA). Note to Practitioners —Self-reconfigurable mobile cleaning robots, which can change their configurations as per the application requirements, are now predominantly used for cleaning and maintenance operations because of their better area coverage, less manpower requirement and consistent performance. However, the state-of-the-art control strategies for conventional robots cannot always ensure stability and performance under the simultaneous effects of configuration changes and uncertainties. The switched Euler-Lagrange model formulated in this work can capture the configuration changes of the robot and the proposed switched adaptive controller can tackle uncertainties of each configurations of the robot. The simulation and experimental results clearly show the potential issues of the state-of-the-art methods and the remarkable benefits of the proposed approach.

Abstract

This work proposes a resource efficient robust control scheme for missile-target engagement scenarios subjected to external disturbances. The robustness is achieved by using an annular event-triggered artificial time-delayed control (ET-TDC) methodology with input saturation. The ET-TDC philosophy uses the TDC strategy through a dynamic predefined triggering mechanism which overcomes the requirement to update the control periodically for every sampling instant, unlike conventional TDC and other robust control schemes. Thus, the proposed methodology, can tackle uncertainties with minimal a priori knowledge while significantly reducing the over-utilization of system resources. In addition, the adopted event triggered mechanism facilitates further conservation of energy which might be crucial for mid-to-long range engagement scenarios. The closed-loop stability is derived analytically and the simulation

Abstract

In this article, a novel adaptive controller is designed for Euler-Lagrangian systems under predefined time-varying state constraints. The proposed controller could achieve this objective without a priori knowledge of system parameters and, crucially, of state-dependent uncertainties. The closed-loop stability is verified using the Lyapunov method, while the overall efficacy of the proposed scheme is verified using a simulated robotic arm compared to the state of the art.

Abstract

Crucial phases in aerial transportation and de- livery of suspended payloads are the clasping and unclasping of the payload to the cable. During these phases, along with the uncertainties in the quadrotor and in the environment, the inevitable payload swings induced by the human interaction or by other external interaction will create additional state- dependent uncertainties; such uncertainties pose a significant challenge in terms of control. If they continue unabated, these uncertainties can cause safety hazard for the quadrotor, the payload and, most importantly, for the human operating the clasping/unclasping tasks. As the state-of-the-art adaptive controllers cannot tackle such uncertainties or considers them as bounded terms, this paper presents an adaptive anti-swing controller where all uncertainties are taken in a state-dependent form. This choice is made to better capture uncertain clasping and unclasping operations of the suspended payload. The closed-loop stability is studied analytically and the real-time experiments confirm significant performance improvements for the proposed scheme over the state of the art.

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.