Abstract
To facilitate accurate tracking in unknown/uncertain environments, this paper proposes a simple learning (SL) strategy for feedback linearization control (FLC) of aerial robots subject to uncertainties. The SL strategy minimizes a cost function defined based on the closed-loop error dynamics of the nominal system via the gradient descent technique to find the adaptation rules for feedback controller gains and disturbance estimate in the feedback control law. In addition to the derivation of the SL adaptation rules, the closed-loop stability for a second-order uncertain nonlinear system is proven in this paper. Moreover, it is shown that the SL strategy can find the global optimum point, while the controller gains and disturbance estimate converge to a finite value which implies a bounded control action in the steady-state. Furthermore, utilizing a simulation study, it is shown that the simple learning-based FLC (SL-FLC) framework can ensure desired closed-loop error dynamics in the presence of disturbances and modeling uncertainties. Finally, to validate the SL-FLC framework in real-time, the trajectory tracking problem of a tilt-rotor tricopter unmanned aerial vehicle under uncertain conditions is studied via three case scenarios, wherein the disturbances in the form of mass variation, ground effect, and wind gust, are induced. The real-time results illustrate that the SL-FLC framework facilitates a better tracking performance than that of the traditional FLC method while maintaining the nominal control performance in the absence of modeling uncertainties and external disturbances, and exhibiting robust control performance in the presence of modeling uncertainties and external disturbances.
Highlights
Owing to the recent advances in automation, computation power, sensors and actuation technology, unmanned aerial vehicles (UAVs) have been explored for a wide variety of applications ranging from search and rescue [1], [2], package delivery [3], [4], traffic monitoring [5], collaboration control [6], and exploration tasks in an unknown environment [7]
CONTRIBUTIONS Motivated by the limitations of the aforementioned methodologies, we develop and implement a simple learning strategy-based feedback linearization control (SL-FLC) algorithm in this study
The efficacy of the proposed simple learning-based FLC (SL-FLC) framework is first validated via a simulation study where the control problem of a second-order dynamical system is considered
Summary
Owing to the recent advances in automation, computation power, sensors and actuation technology, unmanned aerial vehicles (UAVs) have been explored for a wide variety of applications ranging from search and rescue [1], [2], package delivery [3], [4], traffic monitoring [5], collaboration control [6], and exploration tasks in an unknown environment [7]. The aforementioned methodologies show impressive results in the presence of uncertainties and disturbances, they sacrifice nominal performance, i.e., tracking accuracy, in their absence Another popular control approach for uncertain systems is the use of inherently robust controllers, e.g., sliding mode controller (SMC) [17]–[20]. Unlike the aforementioned learning techniques, the proposed simple learning strategy does not require any training data, and it learns the system behavior online Another nonlinear control approach using dynamic NNbased input-output feedback linearization has been proposed in [38]. Whereas PSO requires powerful computing platforms to obtain the optimal controller gains in real-time operation, the proposed simple learning strategy can obtain the optimal controller gains in less than a millisecond on an inexpensive computing platform This aspect is crucial for fast robotic applications, wherein there is a tendency to VOLUME 8, 2020. 3) Extensive experimental evaluation of the proposed framework for three different disturbance scenarios
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