An optimal indirect control of underwater vehicle

Paifelman, Elena; Pepe, Gianluca; Carcaterra, Antonio

doi:10.1080/00207179.2019.1590737

Cited by 18 publications

(15 citation statements)

References 45 publications

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“…Recently the Mechatronics and Vehicle Dynamics research group of Sapienza university is working on the autonomous vehicle project and is developing different control strategies deriving from the principle of the optimum [22,23,24]. The VFC is a dynamic compensator that is obtained by the solution of Pontryagin’s minimum principle for nonlinear dynamical systems [25,26].…”

Section: Optimal Control Strategymentioning

confidence: 99%

Experimenting Sensors Network for Innovative Optimal Control of Car Suspensions

Pepe

Roveri

Carcaterra

2019

Sensors

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This paper presents an innovative electronically controlled suspension system installed on a real car and used as a test bench. The proposed setup relies on a sensor network that acquires a large real-time dataset collecting the car vibrations and the car trim and, through a new controller based on a recently proposed theory developed by the authors, makes use of adjustable semi-active magneto-rheological dampers. A BMW series 1 is equipped with such an integrated sensors-controller-actuators device and an extensive test campaign, in real driving conditions, is carried out to evaluate its performance. Thanks to its strategy, the new plant enhances, at once, both comfort and drivability of the car, as field experiments show. A benchmark analysis is performed, comparing the performance of the new control system with the ones of traditional semi-active suspensions, such as skyhook devices: the comparison shows very good results for the proposed solution.

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Section: Optimal Control Strategymentioning

confidence: 99%

Experimenting Sensors Network for Innovative Optimal Control of Car Suspensions

Pepe

Roveri

Carcaterra

2019

Sensors

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“…The first presents the viscoelastic mathematical beam model, using the Caputo's fractional derivatives. The PD(N) feedback optimal control algorithm is derived in the second section, and in the third, numerical results show the effective performance of the proposed control for a bridge deflection control when it is subject to earth-quake excitation and impulsive disturbances, still maintaining low the cost function in comparison with the LQR method and the implicit Pontryagin solution for IDEs already formulated by authors [20][21][22].…”

Section: Introductionmentioning

confidence: 96%

Optimal Feedback Control Law for Viscoelastic Materials With Memory Effects

Pepe¹,

Paifelman²,

Carcaterra³

2020

XI International Conference on Structural Dynamics

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Viscoelastic materials have excellent properties of absorbing vibrational energy which makes their use very attractive in structural, aerospace and biomechanics engineering applications. The macroscopic dynamical behaviour of such materials depends on the time history, or memory, of the strain. The stress-strain viscoelastic relation can be described by a convolution integral with a memory kernel, according to Boltzmann's formulation of hereditary elasticity, or by using Caputo or Riemann-Liouville fractional derivatives. In order to emphasize the vibrations damping attitude of these materials, by actively controlling their stress-strain behaviour, novel optimal control logics are required which involve memory effects. This paper deals with a feedback control strategy applied to a structural-dynamic problem described by integral-differential equations. It is shown how to obtain a feedback control, called PD (N) , i.e. Proportional-Nth-order-Derivatives control, by using a variational approach. Numerical simulations show how the PD (N) controller is an effective tool to improve the viscoelastic materials performance.

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“…Along with scientific advancement, the inclusion of robust and indirect adaptive control laws has been used to solve control problems that present uncertainties in their dynamics in a distributed environment 13,14 . Some of the latest results presented for these kinds of scenarios are worked from reference‐based control approaches.…”

Section: Introductionmentioning

confidence: 99%

Indirect adaptive synchronization for a heterogeneous multiagent network

Arévalo-Castiblanco

Tellez‐Castro

Mojica‐Nava

2022

Adaptive Control & Signal

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This article studies the problem of indirect adaptive state feedback control for a heterogeneous MIMO network with input and matched uncertainties. A mechanical network composed of two-wheeled robots is proposed to validate the developed methodology. Within the network, a reference system is defined, and each robot has wireless communication between them. Likewise, for control design, specific adaptive laws are adopted through matching conditions to approximate the dynamics of each agent, which are partially unknown and constructed. The inclusion of an optimal adaptive modification is considered for the suppression of input uncertainties. This methodology, along with the distributed adaptive control and identification, is validated with Lyapunov-based stability analysis. The presented control scheme guarantees that all closed-loop signals are bounded and ensures that synchronization errors converge asymptotically to zero. Finally, simulations oriented to the application problem mentioned are presented to validate the effectiveness of the control scheme.

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An optimal indirect control of underwater vehicle

Cited by 18 publications

References 45 publications

Experimenting Sensors Network for Innovative Optimal Control of Car Suspensions

Experimenting Sensors Network for Innovative Optimal Control of Car Suspensions

Optimal Feedback Control Law for Viscoelastic Materials With Memory Effects

Indirect adaptive synchronization for a heterogeneous multiagent network

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