LTV-MPC based path planning of an autonomous vehicle via convex optimization

Mousavi, Mohsen; Heshmati, Zainabolhoda; Moshiri, Behzad

doi:10.1109/iraniancee.2013.6599610

Cited by 28 publications

(14 citation statements)

References 17 publications

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“…Similar to the approach used in [14], the range of enemy defenses are considered as obstacles using linear inequality constraints. Normally, imposing a constraint on the system states based on the location of obstacles creates a space of feasible solutions that is non-convex [14].…”

Section: B Enemy Avoidancementioning

confidence: 99%

“…Normally, imposing a constraint on the system states based on the location of obstacles creates a space of feasible solutions that is non-convex [14]. Therefore, the optimization in Section IV-A would become non-convex and difficult to solve.…”

Section: B Enemy Avoidancementioning

confidence: 99%

“…Theorem 1: Considering quadratic objective function (8), linear model (10) and linear, matrix inequality constraints (14) and (18), when formulated as an MPC, the system will be exponentially stable.…”

Section: Stabilitymentioning

confidence: 99%

“…These can be considered in two broad categories: those which discourage collisions as part the objective function [12]; or those which represent obstacles as a constraint on the system states [13]. Using linear, time-varying system dynamics, [14] created linear inequality constraints from points tangent to the Gaussian distributions of uncertainty around obstacles. These constraints are updated as the system evolves.…”

Section: Introductionmentioning

confidence: 99%

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A predictive motion planner for guidance of autonomous UAV systems

Jardine

Givigi

2016

2016 Annual IEEE Systems Conference (SysCon)

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This paper investigates Unmanned Aerial Vehicle (UAV) systems motion planning for ground attack missions involving enemy defenses. The UAV dynamics are modeled as a unicycle, linearized using dynamic extension and expanded over a finite prediction horizon as a piece-wise affine function. The motion planning problem is then formulated as a constrained, convex minimization in the form of Linear Quadratic Model Predictive Control (LQMPC). Avoidance of enemy defenses is achieved using linear inequality constraints. The design is tested in a simulated ground attack mission involving a layered enemy defense system using MATLAB. Preliminary results demonstrate the feasibility of using LQMPC to guide a UAV in ground attack missions involving complex enemy defenses.

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Section: B Enemy Avoidancementioning

confidence: 99%

Section: B Enemy Avoidancementioning

confidence: 99%

Section: Stabilitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A predictive motion planner for guidance of autonomous UAV systems

Jardine

Givigi

2016

2016 Annual IEEE Systems Conference (SysCon)

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“…Currently, model predictive control (MPC) is a widely used path tracking control method [7][8][9][10][11][12][13][14][15][16]. The biggest advantage of MPC over other control methods, such as pure pursuit control [17], feedforward-feedback control [18], and sliding mode control [19][20][21][22], is that it can take constraints of the system into account explicitly [23].…”

Section: Introductionmentioning

confidence: 99%

Path Tracking of Mining Vehicles Based on Nonlinear Model Predictive Control

et al. 2019

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Path tracking of mining vehicles plays a significant role in reducing the working time of operators in the underground environment. Because the existing path tracking control of mining vehicles, based on model predictive control, is not very effective when the longitudinal velocity of the vehicle is above 2 m/s, we have devised a new controller based on nonlinear model predictive control. Then, we compare this new controller with the existing model predictive controller. In the results of our simulation, the tracking accuracy of our controller at the longitudinal velocity of 4 m/s is close to that of the existing model predictive controller, at the longitudinal velocity of 2 m/s. When longitudinal velocity is 4 m/s, the existing model predictive controller cannot drive the mining vehicle to track the given path, but our nonlinear model predictive controller can, and the maximum displacement error and heading error are 0.1382 m and 0.0589 rad, respectively. According to these results, we believe that this nonlinear model predictive controller can be used to improve the performance of the path tracking of mining vehicles.

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