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1. Distributed cooperative control of multiple UAVs with uncertainty

   https://doi.org/10.1007/s40435-022-01066-5  

   Ahmed, Shihab; Dong, Wenjie (October 2022, International Journal of Dynamics and Control)

   This paper considers the formation flying of multiple quadrotors with a desired orientation and a leader. In the formation flying control, it is assumed that the desired formation is time-varying and there are the system uncertainty and the information uncertainty. In order to deal with different uncertainties, a backstepping-based approach is proposed for the controller design. In the proposed approach, different types of uncertainties are considered in different steps. By integrating adaptive/robust control results and Laplacian algebraic theory, distributed robust adaptive control laws are proposed such that the formation errors exponentially converge to zero and the attitude of each quadrotor exponentially converges to the desired value. Simulation results show the effectiveness of the proposed algorithms. Keywords: Quadrotor · Distributed control · Cooperative control · Leader–follower control · Formation control 

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2. Tracking Control of UAVs with Uncertainty and Input Constraints

   Ahmed, Shihab; Dong, Wenjie (June 2022, Proceedings of the American Control Conference)

   This paper considers the position and attitude tracking control problem of a vertical take-off and landing unmanned aerial vehicle with uncertainty and input constraints. Considering the parametric and non-parametric uncertainties in the dynamics of systems, a robust adaptive tracking controller is proposed with the aid of the special structure of the dynamics of the system. Considering the uncertainty and input constraints, a robust adaptive saturation controller is proposed with the aid of an auxiliary compensated system. Simulation results show the effectiveness of the proposed algorithms. 

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3. Tracking Control of UAVs with Uncertainty and Input Constraints

   https://doi.org/10.23919/ACC53348.2022.9867463  

   Ahmed, Shihab; Dong, Wenjie (June 2022, IEEE)

   This paper considers the position and attitude tracking control problem of a vertical take-off and landing unmanned aerial vehicle with uncertainty and input constraints. Considering the parametric and non-parametric uncertainties in the dynamics of systems, a robust adaptive tracking controller is proposed with the aid of the special structure of the dynamics of the system. Considering the uncertainty and input constraints, a robust adaptive saturation controller is proposed with the aid of an auxiliary compensated system. Simulation results show the effectiveness of the proposed algorithms. 

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4. Adaptive NN-Based Boundary Control for Output Tracking of A Wave Equation with Matched and Unmatched Boundary Uncertainties

   https://doi.org/10.23919/ACC53348.2022.9867897  

   Zhang, Jingting; Stegagno, Paolo; Zeng, Wei; Yuan, Chengzhi (June 2022, Proceedings of the American Control Conference)

   This paper is focused on the output tracking control problem of a wave equation with both matched and unmatched boundary uncertainties. An adaptive boundary feedback control scheme is proposed by utilizing radial basis function neural networks (RBF NNs) to deal with the effect of system uncertainties. Specifically, two RBF NN models are first developed to approximate the matched and unmatched system uncertain dynamics respectively. Based on this, an adaptive NN control scheme is derived, which consists of: (i) an adaptive boundary feedback controller embedded by the NN model approximating the matched uncertainty, for rendering stable and accurate tracking control; and (ii) a reference model embedded by the NN model approximating the unmatched uncertainty, for generating a prescribed reference trajectory. Rigorous analysis is performed using the Lyapunov theory and the C0-semigroup theory to prove that our proposed control scheme can guarantee closed-loop stability and wellposedness. Simulation study has been conducted to demonstrate effectiveness of the proposed approach. 

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5. Scalable Robust Adaptive Control from the System Level Perspective

   https://doi.org/10.23919/ACC.2019.8814896  

   Ho, Dimitar; Doyle, John C. (July 2019, 2019 American Control Conference (ACC))

   We will present a new general framework for robust and adaptive control that allows for distributed and scalable learning and control of large systems of interconnected linear subsystems. The control method is demonstrated for a linear time-invariant system with bounded parameter uncertainties, disturbances and noise. The presented scheme continuously collects measurements to reduce the uncertainty about the system parameters and adapts dynamic robust controllers online in a stable and performance-improving way. A key enabler for our approach is choosing a time-varying dynamic controller implementation, inspired by recent work on System Level Synthesis [1]. We leverage a new robustness result for this implementation to propose a general robust adaptive control algorithm. In particular, the algorithm allows us to impose communication and delay constraints on the controller implementation and is formulated as a sequence of robust optimization problems that can be solved in a distributed manner. The proposed control methodology performs particularly well when the interconnection between systems is sparse and the dynamics of local regions of subsystems depend only on a small number of parameters. As we will show on a five-dimensional exemplary chain-system, the algorithm can utilize system structure to efficiently learn and control the entire system while respecting communication and implementation constraints. Moreover, although current theoretical results require the assumption of small initial uncertainties to guarantee robustness, we will present simulations that show good closed-loop performance even in the case of large uncertainties, which suggests that this assumption is not critical for the presented technique and future work will focus on providing less conservative guarantees. 

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