A Nonsmooth Newton Method for Linear Model-Predictive Control in Tracking Tasks for a Mobile Robot With Obstacle Avoidance

Britzelmeier, Andreas; Gerdts, Matthias

doi:10.1109/lcsys.2020.2996959

Cited by 20 publications

(10 citation statements)

References 21 publications

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“…The above problem, also known as the optimal control problem, is typically solved by classical direct or indirect methods. The work in [17] shows a clear example of this category. In that proposal, a dynamic control problem is solved in the path-planning for a spherical mobile robot.…”

Section: Introduction 1a Review Of Path-planning Methodsmentioning

confidence: 98%

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

et al. 2021

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Mobile robots are currently exploited in various applications to enhance efficiency and reduce risks in hard activities for humans. The high autonomy in those systems is strongly related to the path-planning task. The path-planning problem is complex and requires in its formulation the adjustment of path elements that take the mobile robot from a start point to a target one at the lowest cost. Nevertheless, the identity or the number of the path elements to be adjusted is unknown; therefore, the human decision is necessary to determine this information reducing autonomy. Due to the above, this work conceives the path-planning as a Variable-Length-Vector optimization problem (VLV-OP) where both the number of variables (path elements) and their values must be determined. For this, a novel variant of Differential Evolution for Variable-Length-Vector optimization named VLV-DE is proposed to handle the path-planning VLV-OP for mobile robots. VLV-DE uses a population with solution vectors of different sizes adapted through a normalization procedure to allow interactions and determine the alternatives that better fit the problem. The effectiveness of this proposal is shown through the solution of the path-planning problem in complex scenarios. The results are contrasted with the well-known A* and the RRT*-Smart path-planning methods.

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Section: Introduction 1a Review Of Path-planning Methodsmentioning

confidence: 98%

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

et al. 2021

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“…To avoid redundancy, these algorithms are denoted in the sequel as Alg.1, Alg.2, and Alg.4 respectively. The motion of the vehicle is formulated in curvilinear coordinates relative to a reference path [22] as…”

Section: Numerical Experimentsmentioning

confidence: 99%

Sensitivity updates for linear-quadratic optimization problems in multi-step model predictive control

Emam

Gerdts

2023

J. Phys.: Conf. Ser.

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The paper discusses how parametric sensitivity analysis can be used in certain model predictive control (MPC) schemes. The sensitivity analysis will be performed with regard to the initial state measurement and update schemes will be derived that speed-up the computations. Throughout we restrict the discussion to linear-quadratic optimal control problems in discrete time, which frequently arise in tracking tasks with MPC. The derived tools from sensitivity analysis can be embedded into MPC schemes with a prediction step and multi-step MPC schemes with re-optimization and prediction step. Numerical experiments illustrating the sensitivity analysis are presented.

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“…In air-ground cooperative control, the tracking control scheme designing of AMRs is a complex problem with internal constraints, such as actuator saturation [11]. The tracking control scheme under these constraints can be designed based on a model predictive control (MPC) method, which is a receding horizon control method to handle constraints through optimal procedures [12][13][14]. Some trajectory tracking control strategies under the MPC method applying to an omni-directional AMR were investigated [15,16].…”

Section: Introductionmentioning

confidence: 99%

Air–ground trajectory tracking for autonomous mobile robot based on model predictive hybrid tracking control and multiple harmonics time‐varying disturbance observer

Xiong

Chen

Wei

et al. 2022

IET Control Theory & Appl

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This paper studies a model predictive hybrid tracking control scheme under a multiple harmonics time-varying disturbance observer for a discrete-time dynamics nonholonomic autonomous mobile robot (AMR) with disturbance. To solve the robust tracking control problem of the AMR and unmanned aerial vehicle (UAV) air-ground cooperative, a hybrid tracking control strategy combined with improved model predictive control (MPC) method is presented. First, a time-varying air-ground cooperative tracking control model based on the nonholonomic constraints AMR and UAV is established by polar coordinate transformation. Second, to estimate disturbances of the time-varying model, a multiple harmonics disturbance observer with time-varying gains is designed. A hybrid tracking control scheme for the AMR based on the estimated states and MPC method with relaxing factor and kinematics constraints is proposed. Finally, experimental results show the effectiveness of the proposed control strategy.

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A Nonsmooth Newton Method for Linear Model-Predictive Control in Tracking Tasks for a Mobile Robot With Obstacle Avoidance

Cited by 20 publications

References 21 publications

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant

Sensitivity updates for linear-quadratic optimization problems in multi-step model predictive control

Air–ground trajectory tracking for autonomous mobile robot based on model predictive hybrid tracking control and multiple harmonics time‐varying disturbance observer

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