Formation control of underactuated ships with elliptical shape approximation and limited communication ranges

Duc, Khac

doi:10.1016/j.automatica.2011.11.013

Cited by 59 publications

(15 citation statements)

References 13 publications

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“…Recently, most results regarding the formation problem concentrate on the latter one. Those control strategies include the leader-follower strategy [5]- [9], the distributed control strategy [10]- [14], the graph-based strategy [15], [16], and the intelligent control strategy [17], [18]. The leaderfollower strategy is the most popular method in the formation control of multirobot systems.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network

Wang

Cheng

Hou

et al. 2016

IEEE Trans. Neural Netw. Learning Syst.

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The optimal formation problem of multirobot systems is solved by a recurrent neural network in this paper. The desired formation is described by the shape theory. This theory can generate a set of feasible formations that share the same relative relation among robots. An optimal formation means that finding one formation from the feasible formation set, which has the minimum distance to the initial formation of the multirobot system. Then, the formation problem is transformed into an optimization problem. In addition, the orientation, scale, and admissible range of the formation can also be considered as the constraints in the optimization problem. Furthermore, if all robots are identical, their positions in the system are exchangeable. Then, each robot does not necessarily move to one specific position in the formation. In this case, the optimal formation problem becomes a combinational optimization problem, whose optimal solution is very hard to obtain. Inspired by the penalty method, this combinational optimization problem can be approximately transformed into a convex optimization problem. Due to the involvement of the Euclidean norm in the distance, the objective function of these optimization problems are nonsmooth. To solve these nonsmooth optimization problems efficiently, a recurrent neural network approach is employed, owing to its parallel computation ability. Finally, some simulations and experiments are given to validate the effectiveness and efficiency of the proposed optimal formation approach.

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Section: Introductionmentioning

confidence: 99%

“…Furthermore, in [13], a formation algorithm based on the small-gain theorem was presented for multiple unicycles, which was robust to the position measurement error. In [14], the formation control of a group of underactuated ships was investigated. In addition, the collision avoidance was also considered in the formation controller design.…”

Section: Introductionmentioning

confidence: 99%

Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network

Wang

Cheng

Hou

et al. 2016

IEEE Trans. Neural Netw. Learning Syst.

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“…By proposing modified Sontag's formula, the control design is less tedious than those proposed for strict feedback systems in [17]. Since the Lyapunov functions are not restricted to quadratic or quartic forms, future work is to utilize the control design techniques in this paper to improve control performance of those in [34], [35], [36] proposed for underactuated surface ships and underwater vehicles by 1) addressing system both state-dependent and state-independent stochastic disturbances, and 2) considering optimality.…”

Section: Control Design Summarizationmentioning

confidence: 99%

“…Let t ⋄ s denote the time when u ⋄ 0 is switched from (35) to (36). With the above control design procedure, the infinitesimal generator (30) can be written as…”

Section: ) If |ϑmentioning

confidence: 99%

Global inverse optimal stabilization of stochastic nonholonomic systems

Duc

2015

Systems & Control Letters

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Optimality has not been addressed in existing works on control of (stochastic) nonholonomic systems. This paper presents a design of optimal controllers with respect to a meaningful cost function to globally asymptotically stabilize (in probability) nonholonomic systems affine in stochastic disturbances. The design is based on the Lyapunov direct method, the backstepping technique, and the inverse optimal control design. A class of Lyapunov functions, which are not required to be as nonlinearly strong as quadratic or quartic, is proposed for the control design. Thus, these Lyapunov functions can be applied to design of controllers for underactuated (stochastic) mechanical systems, which are usually required Lyapunov functions of a nonlinearly weak form. The proposed control design is illustrated on a kinematic cart, of which wheel velocities are perturbed by stochastic noise.

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“…Theoretical development in this paper sets a foundation, which can be used to design a formation control system for various agents with a long and narrow shape in practice. For such an application, the reader is referred to Do (2011), where a combination of the development in this paper and several control techniques in Do and Pan (2009) and Do (2010) is presented to design a formation control system for multiple underactuated surface ships with an elliptical shape.…”

Section: Introductionmentioning

confidence: 99%

Formation control of multiple elliptical agents with limited sensing ranges

Duc

2012

Automatica

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This paper presents a design of cooperative controllers that force a group of N mobile agents with an elliptical shape and with limited sensing ranges to perform a desired formation. The controllers guarantee no collisions between any agents in the group. The desired formation can be stabilized at feasible reference trajectories with bounded time derivatives. The formation control design is based on an algebraic separation condition between ellipses, Lyapunov's method, and smooth or p-times differentiable step functions. These functions are introduced and incorporated into novel potential functions to solve the collision avoidance problem without the need of switchings under the agents' limited sensing ranges.

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Formation control of underactuated ships with elliptical shape approximation and limited communication ranges

Cited by 59 publications

References 13 publications

Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network

Optimal Formation of Multirobot Systems Based on a Recurrent Neural Network

Global inverse optimal stabilization of stochastic nonholonomic systems

Formation control of multiple elliptical agents with limited sensing ranges

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