Formation control of multiple Euler-Lagrange systems via null-space-based behavioral control

Chen, Jie; Gan, Minggang; Huang, Jie; Dou, Lihua; Fang, Hao

doi:10.1007/s11432-015-5504-6

Cited by 68 publications

(38 citation statements)

References 34 publications

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“…Remark Comparing with the works of Chen et al and Zhou and Xia, the proposed control algorithm is strong and robust against model uncertainty and nonlinearity, whereas the algorithms in the aforementioned works() can only be applied to a linearizable nonlinear system. Furthermore, only 1 extracted adaptation parameter

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is utilized in this paper in contrast with q +1 and q parameters for each agent in the works of Chen et al and Zhou and Xia, respectively, where q denotes the dimension of parameter. Thus, from the works of Ge et al and Liu and Tong, it can be further concluded that the proposed control scheme can alleviate the computation burden because a few number of adaptive parameters are updated.…”

Section: Control Design For the Formation Reconfigurationmentioning

confidence: 90%

“…Thus, from the works of Ge et al and Liu and Tong, it can be further concluded that the proposed control scheme can alleviate the computation burden because a few number of adaptive parameters are updated. Moreover, the proposed approach guarantees the finite‐time convergence of the task errors and tracking errors, simultaneously, in contrast with the asymptotic convergence of the task errors in the works of Chen et al and Zhou and Xia …”

Section: Control Design For the Formation Reconfigurationmentioning

confidence: 98%

“…However, the algorithms are infeasible for the formation reconfiguration especially in obstacle environments. The position tracking control is essential and significant for SFF in deep‐space explorations, and a growing number of solutions have been presented (eg, see other works()); however, the existing solutions can only achieve the asymptotic/exponential stability in infinite convergence time, and therefore, they cannot meet the high‐precision control requirement in finite time. In addition, the aforementioned results neglect the adverse effect of obstacle/collision avoidances, which may cause the failure of tasks or even lead to disastrous consequences.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neural network–based reconfiguration control for spacecraft formation in obstacle environments

Zhou

Chen

Xia

et al. 2018

Intl J Robust & Nonlinear

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Summary This paper proposes an adaptive formation reconfiguration control scheme based on the leader‐follower strategy for multiple spacecraft systems. By taking the predesigned desired velocities and the trajectories as reference signals, a set of coordination tracking controllers is constructed by combining the reconstructed dynamic system and the neural network–based reconfiguration algorithm together. To avoid collisions between spacecraft and obstacles during the formation configuration process, the null space–based behavioral control is integrated into the control design. Since the spacecraft dynamics contains unknown nonlinearity and disturbance, it is challenging to make the system robust to uncertainties and improve the control precision simultaneously. To solve this problem, both the adaptive neural network strategy and the finite‐time control theory are employed. Finally, 2 simulation examples are carried out to verify the proposed algorithm, showing that the formation reconfiguration task can be executed successfully while achieving high control precision.

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{\overset{W}{true}}_{i}

Section: Control Design For the Formation Reconfigurationmentioning

confidence: 90%

Section: Control Design For the Formation Reconfigurationmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Neural network–based reconfiguration control for spacecraft formation in obstacle environments

Zhou

Chen

Xia

et al. 2018

Intl J Robust & Nonlinear

Self Cite

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“…In a partially observable environment, the robot may produce different observations for one state of the environment, because of incomplete information, also called imperfect information. The situation of imperfect information is often seen in multi-agent systems, where agent's communications are limited and interactions are complicated [4][5][6][7][8]. Imperfect information complicates the control design problem and may degrade the performance of the overall system.…”

mentioning

confidence: 99%

“…Usually, a simplified model leads to a simplified control system structure. Recent study considered the influence of model uncertainty and external disturbance in complex environment with obstacles [4]. The null-space-based (NSB) sketch, one of behavioral based approaches, was extended to the application for more complex dynamics.…”

mentioning

confidence: 99%

New advances in complex motion control for single robot systems and multi-agent systems

Fang

Chen

2016

Sci. China Technol. Sci.

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Motion control is a classic problem, which has constantly attracted attentions from the control and the robotics communities. The problem used to be tackled in idealistic settings or using simplified models to complete complex tasks. Now the wish to control robots in realistic environments has driven the researchers to consider more and more complex systems and scenarios. This brings new challenges to the motion control problems.One that may complicate the motion control problem is the observability of the environment. In a fully observable environment, the observation of the robot can reveals the current state of the environment, which can then be used to design control laws [1][2][3]. In a partially observable environment, the robot may produce different observations for one state of the environment, because of incomplete information, also called imperfect information. The situation of imperfect information is often seen in multi-agent systems, where agent's communications are limited and interactions are complicated [4][5][6][7][8]. Imperfect information complicates the control design problem and may degrade the performance of the overall system. Cooperation with human was adopted to improve the performance of a multi-agent system in complex environments [9][10].To develop practical robot applications, it is necessary to take the environment into consideration, and the more complex the environment is, the more difficult it will be when designing the control law. Complex environments lead to complicated mechanisms, which makes the design problem complex. Recent studies focused on the controller design problem in complex environments for holonomic and nonholonomic robot systems, where different approaches to deal with the complexity were also proposed. Ref.[1] considered how to design a unified theoretical framework for both holonomic and nonholonomic robot systems in stairclimbing. A novel control law called active tension control combined with the computed torque method was presented for holonomic or nonholonomic robotic systems. There is also a corresponding work on realistic environment, such as rough terrains, where most of the existing bio-inspired legged robots do not possess walking abilities [2]. Generation of adaptive multiple gaits in rough terrains was considered and a central pattern generator (CPG)-based locomotion control method was proposed, integrated with a contact force feedback function. Another recently proposed method to solve the complex motion control problem for a single robot is to improve robot's structure combined with new controller design [3]. For example, ref.[3] introduced a ±50° yawing head joint that functions as the neck for improving turning ability, which enables the robotic fish to carry out complex motions. Correspondingly, an improved central pattern generator (CPG) model was also proposed.In multi-agent systems, communications and cooperations are unavoidable. This complicates the motion control problem. Usually, a simplified model leads to a simplified control system st...

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Distributed region reaching consensus control for uncertain networked multi‐robot systems

Zhu,

Yang,

Zhang

et al. 2023

Z Angew Math Mech

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The main objective of this paper is to advance the concept of region reaching control for a single robot to the study of region reaching consistency for uncertain fully‐actuated multi‐robot systems formulated by Lagrange dynamics. By introducing a directed network communication topology and utilizing the pinning‐like control methods, a distributed region reaching consensus control strategy is proposed. The desired region shape can be specified by selecting an appropriate potential objective function. A feature of the developed region reaching consensus algorithm is fully distributed, which is also an important generalization of pinning‐like set point tracking scheme based on the typical proportion‐integration‐differentiation control scheme. More importantly, the scope of consistency in this paper can be explicitly defined, which is different from traditional consistency control algorithms. Subsequently, numerical examples are presented to illustrate the performance of the designed controllers.

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Formation control of multiple Euler-Lagrange systems via null-space-based behavioral control

Cited by 68 publications

References 34 publications

Neural network–based reconfiguration control for spacecraft formation in obstacle environments

Neural network–based reconfiguration control for spacecraft formation in obstacle environments

New advances in complex motion control for single robot systems and multi-agent systems

Distributed region reaching consensus control for uncertain networked multi‐robot systems

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