ENERGY-EFFICIENT COOPERATIVE ADAPTIVE CRUISE CONTROL OF PLATOONING - - PowerPoint PPT Presentation

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ENERGY-EFFICIENT COOPERATIVE ADAPTIVE CRUISE CONTROL OF PLATOONING - - PowerPoint PPT Presentation

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ENERGY-EFFICIENT COOPERATIVE ADAPTIVE CRUISE CONTROL OF PLATOONING VEHICLES Weinan Gao 1 , Zhong-Ping Jiang 1 , IEEE Fellow , Kaan Ozbay 2 1. Control and Networks Lab, Department of Electrical and Computer Engineering, New York University 2.

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ENERGY-EFFICIENT COOPERATIVE ADAPTIVE CRUISE CONTROL OF PLATOONING VEHICLES

Weinan Gao1, Zhong-Ping Jiang1, IEEE Fellow, Kaan Ozbay2

  • 1. Control and Networks Lab, Department of Electrical and Computer Engineering,

New York University

  • 2. Department of Civil and Urban Engineering, and the Center for Urban Science and

Progress (CUSP), New York University

Nov 15, 2016

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SLIDE 2

Background and Motivation

Background: There are over 1 million road traffic deaths worldwide every year. 90% of accidents are attributed to human errors Americans were stuck in traffic for 8 billion hours in 2015. ”We are the first generation that can end poverty, the last that can end climate change.” – Ban Ki-Moon. It’s imperative to develop next-generation cruise controllers for connected vehicles to increase the safety, reliability, connectivity and autonomy. [Challenge]:

1

Parametric variations → Unknown system parameters

2

Uncertain models of to-be-followed vehicles

3

Energy efficient → Eco-friendly

4

Input(acceleration) saturation

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Background and Motivation

1

Adaptive control approaches for platooning vehicles are not optimal [Swaroop, Hedrick & Choi 2001], [Kwon & Chua 2014].

2

Optimal control methods are usually model-based and not data-driven. [Jovanovic & Bamieh 2005], [Waschl, Kolmanovsky, Steinbuch, & del Re 2014].

3

Reinforcement-learning-based controllers cannot guarantee the stability of the closed-loop systems [Ng et al. 2008], [Desjardins & Chaib-draa 2011]

We develop a data-driven, non-model-based adaptive optimal controller for platooning vehicles by Adaptive Dynamics Programming (ADP). The issue of input saturation is also addressed.

1Gao, W.; Jiang, Z. P. & Ozbay, K. Data-driven Adaptive optimal control of connected vehicles, IEEE Transactions on Intelligent Transportation Systems, 2016. 3 / 14

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Background and Motivation

Dynamic Programming [Bellman 1957]

1 Curse of dimensionality 2 Curse of modeling

[Werbos 1968] pointed out that adaptive approximation to the HJB equation can be achieved by designing appropriate learning systems: approximate/adaptive dynamic programming (ADP)

1 Heuristic dynamic programming: approximate the optimal cost

function.

2 Dual dynamic programming: approximate the gradient of the

  • ptimal cost function.

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Review on ADP and Adaptive Optimal Control

The platoon can be modeled by the following systems ˙ x =Ax + Bu J (x0) = ∞

  • xT (τ)Qx(τ) + uT (τ)Ru(τ)
  • dτ

The optimal control policy u = −R−1BT P ∗x := −K∗x where P ∗ = P ∗T > 0 is the unique solution to Riccati equation AT P ∗ + P ∗A + Q − P ∗BR−1BT P ∗ = 0 Adaptive optimal control: find P ∗ and K∗ when A and B are unknown

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Review on ADP and Adaptive Optimal Control

[Jiang & Jiang 2012] Adaptive optimal control with unknown system matrices A and B

1 Start from an admissible K0. k ← 0. 2 xT (t1)Pkx(t1) − x(t0)T Pkx(t0) = −

t1

t0 xT (Q + KT k RKk)xdτ+

2 t1

t0 (u + Kkx)T RKk+1xdτ

3 k ← k + 1. Repeat Step 2.

Both can ensure lim

k→∞Pk = P ∗ and lim k→∞Kk = K∗.

2Jiang, Y. & Jiang, Z. P. Computational adaptive optimal control for continuous-time linear systems with completely unknown dynamics, Automatica , 2012, 48, 2699-2704. 6 / 14

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Paramics Micro-Traffic Simulation Results

Double-loop ADP algorithm Traffic simulation architecture

1Gao, W.; Jiang, Z. P. & Ozbay, K. Data-driven Adaptive optimal control of connected vehicles, IEEE Transactions on Intelligent Transportation Systems, 2016. 7 / 14

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Paramics Micro-Traffic Simulation Results

−10 −5 5 10 500 1000 1500 2000 2500 3000 3500 4000 (a) Acceleration [m/s2] −10 −5 5 10 1000 2000 3000 4000 5000 6000 (b) Acceleration [m/s2]

t[s] 5 10 15 20 25 30 hi[m] 10 15 20 25 (a) h2 h3 h4 h5 t[s] 5 10 15 20 25 30 vi[m/s] 5 10 15 20 (b) v1 v2 v3 v4 v5

Histograms of accelerations Plots of headways and velocities

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Paramics Micro-Traffic Simulation Result

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Nonlinear and Adaptive Optimal Control of Platooning Vehicles

Employ global ADP (GADP)[Jiang and Jiang 2015] to solve a longstanding issue in ITS: how to take into account strong nonlinearity and unknown dynamics in the design of global adaptive optimal controllers. Contributions: Because of the strongly nonlinear dynamics of the platooning vehicles, we are not aware of any global solutions to adaptive optimal control of platooning vehicles with unknown dynamics. Different from existing adaptive control approaches of platooning vehicles [Swaroop, Hedrick & Choi 2001], [Kwon & Chua 2014] the online GADP approach learns a near-optimal controller iteratively via real-time state/input data. The neural network approximation is avoided for this kind of high-order platooning vehicle systems which dramatically decreases the computational burden.

3Gao, W. & Jiang, Z. P., Nonlinear and Adaptive Suboptimal Control of Connected Vehicles: A Global Adaptive Dynamic Programming Approach. Journal of Intelligent & Robotic Systems, 2016. 10 / 14

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Nonlinear and Adaptive Optimal Control of Platooning Vehicles

time (sec)

50 100 150

headway(m)

  • 2

2 4 h1-h* h2-h* h3-h*

time (sec)

50 100 150

velocity(m/sec)

  • 0.5

0.5 1 v1-v* v2-v* v3-v*

time (sec)

50 100 150

headway(m)

  • 2

2 4 h1-h* h2-h* h3-h*

time (sec)

50 100 150

velocity(m/sec)

  • 0.5

0.5 1 v1-v* v2-v* v3-v*

Initial control policy Learned control policy

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Nonlinear and Adaptive Optimal Control of Platooning Vehicles

10 5

x1

  • 5
  • 10
  • 10
  • 5

x2

5 ×1012 2.5 1 0.5 2 1.5 10

V0(x1,x2,0,0,0,0) V10(x1,x2,0,0,0,0)

10 5

x3

  • 5
  • 10
  • 10
  • 5

x4

5 ×1011 8 2 4 6 10 12 10

V0(0,0,x3,x4,0,0) V10(0,0,x3,x4,0,0)

10 5

x5

  • 5
  • 10
  • 10
  • 5

x6

5 ×1011 5 2 1 6 7 8 4 3 10

V0(0,0,0,0,x5,x6) V10(0,0,0,0,x5,x6)

Figure: Comparison of value functions V0 w.r.t. the initial control policy and V10 w.r.t. the learner control policy

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Thanks!

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Supplimental slides: Model of Vehicles

The optimal velocity model [Orosz, et al., 2010] of the ith human-driven vehicle is ˙ hi = vi−1 − vi, ˙ vi = αi(f(hi) − vi) + βi ˙ hi, (1) where i = 2, 3, · · · , n. αi and βi are human parameters with αi the headway gain and βi the relative velocity gain satisfying αi > 0, αi + βi > 0. f(·) indicates a range policy f(h) =    if h ≤ hs, vm(1 − cos(π h−hs

hg−hs ))/2

if hs < h < hg, vm if hg ≤ h. (2) which implies that the vehicle i remains standstill if hi ≤ hs. vi increases as hi increases in the range (hs, hg). Additionally, if hi ≥ hg, vehicle i aims to travel at the maximum velocity vm. In this paper, the goal for each driver is to actuate the vehicle at desired headway h∗ and velocity v∗ = f(h∗).

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