Nonlinear Model Predictive Formation Control: An Iterative Weighted Tuning Approach

Nascimento, Tiago P.; Costa, L.; Conceição, André Gustavo Scolari; Moreira, António Paulo

doi:10.1007/s10846-015-0183-5

Cited by 16 publications

(9 citation statements)

References 16 publications

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“…The works are given in the order of publication (years). All mentioned works contain description of vehicle dynamics or kinematics model (except for [34] where, however, a reference is made to model from another paper), results of simulation and/or empirical research, therefore these information is not included in the table. After analysis of data presented in the table one can notice that:…”

Section: Resultsmentioning

confidence: 99%

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State of the Art in Predictive Control of Wheeled Mobile Robots

Harasim

Trojnacki

2016

JAMRIS

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The paper is concerned with the problem of tracking control of wheeled mobile robots (WMRs) using predictive control systems. Various kinematic structures of WMRs important from the point of view of motion control are discussed. A hierarchical approach to the problem of motion control of this kind of robots is presented. The problems of trajectory tracking and path following control of WMRs are briefly discussed. The methods of predictive control of WMRs are described in detail and the following aspects relevant to predictive control are considered: kinematic structures of robots, slip of wheels and its compensation, assumed constraints, methods of optimization of the objective function, problems of model nonlinearity, linearization and discretization, stability of the control system and use of the state observers.

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Section: Resultsmentioning

confidence: 99%

“…Often model linearization is used in the neighborhood of the operating point, which ultimately boils down to use of algorithms relying on linear model. In contrast, the NMPC uses advanced methods of objective function optimization, which was discussed in the works:[3,7,10,19,28,29,31,34,38,44].…”

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confidence: 99%

State of the Art in Predictive Control of Wheeled Mobile Robots

Harasim

Trojnacki

2016

JAMRIS

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“…The Pioneer 3 simulated model 65 considers white noise in the values of position and velocities that are fed back to the controllers in order to have a more realistic simulation. The values of λ are λ 1 = 5, λ 2 = 3, and λ 3 = 1 for the straight trajectory and λ 1 = 1, λ 2 = 1.5, and λ 3 = 1 for the circularshaped trajectory and they were obtained through the method proposed by Nascimento et al 74 The robot model used in the simulation is that of a Pioneer 3 robot. Fig.…”

Section: Nmpc Example With Differential Drive Mobile Robotmentioning

confidence: 99%

Nonholonomic mobile robots' trajectory tracking model predictive control: a survey

Nascimento

Dórea²,

Gonçalves³

2018

Robotica

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SUMMARYModel predictive control (MPC) theory has gained attention with the recent increase in the processing power of computers that are now able to perform the needed calculations for this technique. This kind of control algorithms can achieve better results in trajectory tracking control of mobile robots than classical control approaches. In this paper, we present a review of recent developments in trajectory tracking control of mobile robot systems using model predictive control theory, especially when nonholonomicity is present. Furthermore, we point out the growth of the related research starting with the boom of mobile robotics in the 90s and discuss reported field applications of the described control problem. The objective of this paper is to provide a unified and accessible presentation, placing the classical model, problem formulations and approaches into a proper context and to become a starting point for researchers who are initiating their endeavors in linear/nonlinear MPC applied to nonholonomic mobile robots. Finally, this work aims to present a comprehensive review of the recent breakthroughs in the field, providing links to the most interesting and successful works, including our contributions to state-of-the-art.

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“…While addressing this problem, the stateof-art methodologies for obstacle avoidance in the context of cooperative target tracking have drawbacks. In [7], [8] obstacle avoidance is imposed as part of the weighted MPC based optimization objective, thereby providing no guaranteed avoidance. In [1] obstacle avoidance is a separate planning module, which modifies the generated optimization trajectory using potential fields.…”

Section: Introductionmentioning

confidence: 99%

“…. u R k * t (N) = arg min u R k t (0)...u R k t (N) (J DQMPC ) (3) subject to, x R k t (n + 1) ẋ R k t (n + 1) = A x R k t (n) ẋ R k t (n) + B(u R k t (n) + f R k t (n) + g g g), (4) u min ≤ u R k t (n) ≤ u max ,(5)x min ≤ x R k t (n) ≤ x max ,(6)x min ≤ẋ R k t (n) ≤ẋ max(7)…”

mentioning

confidence: 99%

Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

Tallamraju

Rajappa

Black

et al. 2018

2018 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR)

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In this work, we consider the problem of decentralized multi-robot target tracking and obstacle avoidance in dynamic environments. Each robot executes a local motion planning algorithm which is based on model predictive control (MPC). The planner is designed as a quadratic program, subject to constraints on robot dynamics and obstacle avoidance. Repulsive potential field functions are employed to avoid obstacles. The novelty of our approach lies in embedding these nonlinear potential field functions as constraints within a convex optimization framework. Our method convexifies non-convex constraints and dependencies, by replacing them as pre-computed external input forces in robot dynamics. The proposed algorithm additionally incorporates different methods to avoid field local minima problems associated with using potential field functions in planning. The motion planner does not enforce predefined trajectories or any formation geometry on the robots and is a comprehensive solution for cooperative obstacle avoidance in the context of multi-robot target tracking. Video of simulation studies: https://youtu.be/umkdm82Tt0M arXiv:1805.09633v1 [cs.RO]

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Nonlinear Model Predictive Formation Control: An Iterative Weighted Tuning Approach

Cited by 16 publications

References 16 publications

State of the Art in Predictive Control of Wheeled Mobile Robots

State of the Art in Predictive Control of Wheeled Mobile Robots

Nonholonomic mobile robots' trajectory tracking model predictive control: a survey

Decentralized MPC based Obstacle Avoidance for Multi-Robot Target Tracking Scenarios

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