Download PDF by Adnan Tahirovic, Gianantonio Magnani: Passivity-Based Model Predictive Control for Mobile Vehicle

By Adnan Tahirovic, Gianantonio Magnani

ISBN-10: 1447150481

ISBN-13: 9781447150480

ISBN-10: 144715049X

ISBN-13: 9781447150497

Passivity-based version Predictive regulate for cellular automobile Navigation represents an entire theoretical method of the adoption of passivity-based version predictive regulate (MPC) for self sufficient motor vehicle navigation in either indoor and outside environments. The short additionally introduces research of the worst-case state of affairs that will ensue throughout the activity execution. many of the questions replied within the textual content comprise:

• the right way to use an MPC optimization framework for the cellular automobile navigation process;

• how one can warrantly secure job final touch even in complicated environments together with concern avoidance and sideslip and rollover avoidance; and

• what to anticipate within the worst-case state of affairs during which the roughness of the terrain leads the set of rules to generate the longest attainable route to the target.

The passivity-based MPC method presents a framework during which quite a lot of complicated automobiles should be accommodated to procure a more secure and extra realizable software through the path-planning level. in the course of job execution, the optimization step is consistently repeated take into consideration new neighborhood sensor measurements. those ongoing alterations make the trail generated quite strong compared to suggestions that repair the total course sooner than job execution. as well as researchers operating in MPC, engineers attracted to motor vehicle course making plans for a few reasons: rescued undertaking in detrimental environments; humanitarian demining; agriculture; or even planetary exploration, will locate this SpringerBrief to be instructive and helpful.

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Extra info for Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning

Sample text

28). 28), the condition on the derivative of the energy storage function along the trajectories of the closed-loop system is obtained as follows: 2 V˙ = gv1 vv p ≤ − v arctan(kv v). 29) Hence, V˙ is negative semidefinite and V˙ = 0 if and only if v = 0. Then, by zero-state observability, y(t) ≡ 0 ⇒ xss ≡ 0. Therefore, according to the invariance principle, the origin of the subsystem represented by xss is globally asymptotically stable. 3 Design of the PB/MPC Motion Planner 23 of the virtual potential NF(r) and of the kinetic energy term 21 v 2 , will decrease along the trajectories of the controlled system, moving the vehicle toward the goal position.

In Proceedings of the 18th IFAC World Congress, pp. 5944–5949 (2011) 3. A. Tahirovic, G. Magnani, Y. Kuwata, An approximate of the cost-to-go map on rough terrains. in Proceedings of the IEEE International Conference on Mechatronics (2013) 4. J. Aguilar, F. A. Aguilar, F. Carvajal, Effects of terrain morphology, sampling density, and interpolation methods on grid DEM accuracy. Photogram. Eng. Remote Sens. 71(7), (2005), pp. 805–816 5. A. Stentz, The focussed D* algorithm for real-time replanning.

2). 4) easily follow. 2 Completely Known Rough Terrain with Obstacles If the terrain is completely known then the navigation function is computed only once, hence M = 1. 9) k NF(r0 ), vmin where Tgoal ≤ Tgoal−max . 10) 44 4 Some Limitations and Real-Time Implementation NF1 − NF0 = NF(r0 ) − NF(rgoal ) = NF(r0 ). If NF = NF(x, y) is the navigation function given by Cartesian coordinates, and NF = NF(ρ, θ) being the navigation function given by polar coordinates, where x = ρ cos θ and y = ρ sin θ, then the following theorem gives the shape of the worst case scenario generated by the PB/MPC optimization framework for completely known rough terrain with obstacles.

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Passivity-Based Model Predictive Control for Mobile Vehicle Motion Planning by Adnan Tahirovic, Gianantonio Magnani

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