Static output feedback control of switched nonlinear systems with time-varying delay and parametric uncertainties under asynchronous switching

Taghieh, Amin; Shafiei, Mohammad Hossein

doi:10.1177/0142331220969056

Cited by 9 publications

(2 citation statements)

References 37 publications

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“…Recently, Taghieh and Shafiei (2021a ) proposed an observer-based robust model predictive control scheme to control a class of switched nonlinear systems in the presence of time delay and parametric uncertainties under arbitrary switching in addition to proposing a static output feedback controller ( Taghieh and Shafiei, 2021b ) for a class of switched nonlinear systems subject to time-varying delay and uncertainties under asynchronous switching. Zhang et al (2022) addressed nonlinear systems with mismatched uncertainties under input/output quantization proposing adaptive output feedback control.…”

Section: Introductionmentioning

confidence: 99%

Treatise on Analytic Nonlinear Optimal Guidance and Control Amplification of Strictly Analytic (Non-Numerical) Methods

Sands

2022

Front. Robot. AI

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Optimal control is seen by researchers from a different perspective than that from which the industry practitioners see it. Either type of user can easily become confounded when deciding which manner of optimal control should be used for guidance and control of mechanics. Such optimization methods are useful for autonomous navigation, guidance, and control, but their performance is hampered by noisy multi-sensor technologies and poorly modeled system equations, and real-time on-board utilization is generally computationally burdensome. Some methods proposed here use noisy sensor data to learn the optimal guidance and control solutions in real-time (online), where non-iterative instantiations are preferred to reduce computational burdens. This study aimed to highlight the efficacy and limitations of several common methods for optimizing guidance and control while proposing a few more, where all methods are applied to the full, nonlinear, coupled equations of motion including cross-products of motion from the transport theorem. While the reviewed literature introduces quantitative studies that include parametric uncertainty in nonlinear terms, this article proposes accommodating such uncertainty with time-varying solutions to Hamiltonian systems of equations solved in real-time. Five disparate types of optimum guidance and control algorithms are presented and compared to a classical benchmark. Comparative analysis is based on tracking errors (both states and rates), fuel usage, and computational burden. Real-time optimization with singular switching plus nonlinear transport theorem decoupling is newly introduced and proves superior by matching open-loop solutions to the constrained optimization problem (in terms of state and rate errors and fuel usage), while robustness is validated in the utilization of mixed, noisy state and rate sensors and uniformly varying mass and mass moments of inertia. Compared to benchmark, state-of-the-art methods state tracking errors are reduced one-hundred ten percent. Rate tracking errors are reduced one-hundred thirteen percent. Control utilization (fuel) is reduced eighty-four percent, while computational burden is reduced ten percent, simultaneously, where the proposed methods have no control gains and no linearization.

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Section: Introductionmentioning

confidence: 99%

Treatise on Analytic Nonlinear Optimal Guidance and Control Amplification of Strictly Analytic (Non-Numerical) Methods

Sands

2022

Front. Robot. AI

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“…Since the delay phenomenon strikes the system's performance and engenders instability, an appropriate DSE and distributed controller should be applied to the MASs. Lyapunov-Krasovskii functional (LKF) as a useful tool is utilized to investigate the stability of time-delayed systems in [30][31][32][33][34]. LF consensus problem of time-delay double integrator systems under switching interconnection graphs has been investigated in [35].…”

Section: Introductionmentioning

confidence: 99%

H∞-based control of multi-agent systems: Time-delayed signals, unknown leader states and switching graph topologies

et al. 2022

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The paper investigates a leader-following scheme for nonlinear multi-agent systems (MASs). The network of agents involves time-delay, unknown leader’s states, external perturbations, and switching graph topologies. Two distributed protocols including a consensus protocol and an observer are utilized to reconstruct the unavailable states of the leader in a network of agents. The H∞-based stability conditions for estimation and consensus problems are obtained in the framework of linear-matrix inequalities (LMIs) and the Lyapunov-Krasovskii approach. It is ensured that each agent achieves the leader-following agreement asymptotically. Moreover, the robustness of the control policy concerning a gain perturbation is guaranteed. Simulation results are performed to assess the suggested schemes. It is shown that the suggested approach gives a remarkable accuracy in the consensus problem and leader’s states estimation in the presence of time-varying gain perturbations, time-delay, switching topology and disturbances. The H∞ and LMIs conditions are well satisfied and the error trajectories are well converged to the origin.

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A novel adaptive interval type-3 neuro-fuzzy robust controller for nonlinear complex dynamical systems with inherent uncertainties

et al. 2022

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Static output feedback control of switched nonlinear systems with time-varying delay and parametric uncertainties under asynchronous switching

Cited by 9 publications

References 37 publications

Treatise on Analytic Nonlinear Optimal Guidance and Control Amplification of Strictly Analytic (Non-Numerical) Methods

Treatise on Analytic Nonlinear Optimal Guidance and Control Amplification of Strictly Analytic (Non-Numerical) Methods

H∞-based control of multi-agent systems: Time-delayed signals, unknown leader states and switching graph topologies

A novel adaptive interval type-3 neuro-fuzzy robust controller for nonlinear complex dynamical systems with inherent uncertainties

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