Source seeking with non-holonomic unicycle without position measurement and with tuning of forward velocity

Zhang, Chunlei; Arnold, Dan; Razavi-Ghods, Nima; Siranosian, Antranik Antonio; Krstić, Miroslav

doi:10.1016/j.sysconle.2006.10.014

Cited by 211 publications

(152 citation statements)

References 13 publications

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“…If T v and λ are chosen according to (8) and (9), respectively, such that λ is uniformly bounded and strictly positive, then…”

Section: Convergence To the Steady-statementioning

confidence: 99%

“…Under the conditions of Theorem 1 and for T v and λ chosen according to (8) and (9), respectively, such that 0 < λ min ≤ λ ≤ λ max ,…”

Section: Theoremmentioning

confidence: 99%

“…Control algorithms for networks of vehicles inspired by collective behavior such as fish schooling and chemotaxis are designed in [6,7]. An extremum seeking strategy is adopted in [8]. Statistical approaches have also been proposed for the case of turbulent environments, but assuming the availability of vehicle's position measurements [9,10].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Optimotaxis: A Stochastic Multi-agent Optimization Procedure with Point Measurements

Mesquita

Hespanha

Åström

Hybrid Systems: Computation and Control

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Abstract. We consider the problem of seeking the maximum of a scalar signal using a swarm of autonomous vehicles equipped with sensors that can take point measurements of the signal. Vehicles are not able to measure their current position or to communicate with each other. Our approach induces the vehicles to perform a biased random walk inspired by bacterial chemotaxis and controlled by a stochastic hybrid automaton. With such a controller, it is shown that the positions of the vehicles evolve towards a probability density that is a specified function of the spatial profile of the measured signal, granting higher vehicle densities near the signal maxima.

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“…If T v and λ are chosen according to (8) and (9), respectively, such that λ is uniformly bounded and strictly positive, then…”

Section: Convergence To the Steady-statementioning

confidence: 99%

“…Under the conditions of Theorem 1 and for T v and λ chosen according to (8) and (9), respectively, such that 0 < λ min ≤ λ ≤ λ max ,…”

Section: Theoremmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimotaxis: A Stochastic Multi-agent Optimization Procedure with Point Measurements

Mesquita

Hespanha

Åström

Hybrid Systems: Computation and Control

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“…To achieve this, we utilize a model-free control algorithm known as Extremum Seeking (ES) [7]. ES has been used in a variety applications 978-1-4673-8040-9/15/$31.00 ©2015 IEEE including: robotic motion control [8]- [9], photovoltaic MPPT [10], wind energy conversion [11], and automotive engine control [12]. The algorithm utilizes a periodic probing signal to perform real time optimization over objective functions which can be nonconvex (provided the probing signal is sufficiently exciting).…”

Section: Introductionmentioning

confidence: 99%

Extremum Seeking control of smart inverters for VAR compensation

Arnold

Negrete-Pincetic

Stewart

et al. 2015

2015 IEEE Power &Amp; Energy Society General Meeting

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Abstract-Reactive power compensation is used by utilities to ensure customer voltages are within pre-defined tolerances and reduce system resistive losses. While much attention has been paid to model-based control algorithms for reactive power support and Volt Var Optimization (VVO), these strategies typically require relatively large communications capabilities and accurate models. In this work, a non-model-based control strategy for smart inverters is considered for VAR compensation. An Extremum Seeking control algorithm is applied to modulate the reactive power output of inverters based on real power information from the feeder substation, without an explicit feeder model. Simulation results using utility demand information confirm the ability of the control algorithm to inject VARs to minimize feeder head real power consumption. In addition, we show that the algorithm is capable of improving feeder voltage profiles and reducing reactive power supplied by the distribution substation.

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“…Next, to our best knowledge, there is no solution dealing with various types of robots in a unified way. In fact, the existing results have focused on specific robots, e.g., integrators in [3], nonholonomic unicycles in [2], [4], [5], and underwater vehicles [1].…”

Section: Introductionmentioning

confidence: 99%

Nonholonomic source seeking in switching random fields

Azuma

Sakar

Pappas

2010

49th IEEE Conference on Decision and Control (CDC)

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Abstract-We consider the problem of designing controllers for nonholonomic mobile robots converging to the source (minimum) of a field. In addition to the mobility constraints posed by the nonholonomic dynamics, we assume that the field is completely unknown to the robot and the robot has no knowledge of its own position. Furthermore, the field is randomly switching. In this paper, we combine ideas from stochastic approximations and nonholonomic control, in order to address this challenging problem. In particular, we develop a rotation-invariant and forward-sided version of the simultaneous-perturbation stochastic algorithm, which is much more suitable for sensorfree navigation. Based on this algorithm, we design source seeking controllers for nonholonomic robots and prove convergence to the unknown source with probability 1. The proposed controllers are demonstrated by numerical simulations.

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Source seeking with non-holonomic unicycle without position measurement and with tuning of forward velocity

Cited by 211 publications

References 13 publications

Optimotaxis: A Stochastic Multi-agent Optimization Procedure with Point Measurements

Optimotaxis: A Stochastic Multi-agent Optimization Procedure with Point Measurements

Extremum Seeking control of smart inverters for VAR compensation

Nonholonomic source seeking in switching random fields

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