Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design

Bian, Tao; Jiang, Zhong‐Ping

doi:10.1016/j.automatica.2016.05.003

Cited by 213 publications

(84 citation statements)

References 59 publications

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“…This inequality takes a similar form as (21), and it follows from the proof of Proposition 1 that A − B  T P 0 ( 0 ) + 1 2 I 6 is Hurwitz. Thus, the theorem is proven, and it remains to prove (29).…”

Section: Design Of the Initial Stabilizing Gainmentioning

confidence: 68%

Data‐driven–based attitude control of combined spacecraft with noncooperative target

Jiang

Zhou

et al. 2019

Intl J Robust & Nonlinear

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Summary In this paper, the attitude control of combined spacecraft with noncooperative target is studied. For the linearized system of the attitude control system, which possesses uncertainties on both its control and system matrices caused by the noncooperative target, this paper proposes an approximated iteration method to obtain the optimal controller such that the closed‐loop system possesses a prescribed convergence rate. An explicit stabilizing gain is established to initiate the iteration method. Based on the proposed iteration method and the initial stabilizing gain, a data‐driven algorithm is proposed to obtain the optimal controller for the attitude control system without using the system parameter information. A simulation is given to verify the effectiveness of the proposed method.

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Section: Design Of the Initial Stabilizing Gainmentioning

confidence: 68%

Data‐driven–based attitude control of combined spacecraft with noncooperative target

Jiang

Zhou

et al. 2019

Intl J Robust & Nonlinear

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“…and it is always possible to satisfy (22). For a more general result, please see theorem 4.4.14 in the work of Horn and Johnson.…”

Section: Hence One Hasmentioning

confidence: 99%

“…One has the conventional centralized optimization as well as more recent distributed approaches aiming at flexibility, reconfigurability, and robustness. The optimal control of a single dynamic system, also called a single‐player game , is the simplest dynamic optimization problem. However, this approach is usually found lacking in robustness to external disturbances acting on the system and possibly subsystem failures.…”

Section: Introductionmentioning

confidence: 99%

“…In this sense, RL implies a cause‐and‐effect relationship between policies and costs, and as such, RL‐based frameworks enjoy optimality and adaptivity. Over the past few years, dynamic programming is utilized to develop RL techniques for adaptive‐optimal control of dynamic systems (see other works to name a few). Hence, RL is by now fairly standard in solving the single‐player optimal and robust control problems .…”

Section: Introductionmentioning

confidence: 99%

“…Over the past few years, dynamic programming is utilized to develop RL techniques for adaptive‐optimal control of dynamic systems (see other works to name a few). Hence, RL is by now fairly standard in solving the single‐player optimal and robust control problems . However, as of recently, RL can also be used to tackle even more complicated optimization structures like multiple interconnected dynamics system, namely, a multiplayer game, where a single centralized dynamics contains the dynamics of all players .…”

Section: Introductionmentioning

confidence: 99%

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Differential graphical games for H_∞ control of linear heterogeneous multiagent systems

Yaghmaie

Movric

Lewis

et al. 2019

Intl J Robust & Nonlinear

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Summary Differential graphical games have been introduced in the literature to solve state synchronization problem for linear homogeneous agents. When the agents are heterogeneous, the previous notion of graphical games cannot be used anymore and a new definition is required. In this paper, we define a novel concept of differential graphical games for linear heterogeneous agents subject to external unmodeled disturbances, which contain the previously introduced graphical game for homogeneous agents as a special case. Using our new formulation, we can solve both the output regulation and H∞ output regulation problems. Our graphical game framework yields coupled Hamilton‐Jacobi‐Bellman equations, which are, in general, impossible to solve analytically. Therefore, we propose a new actor‐critic algorithm to solve these coupled equations numerically in real time. Moreover, we find an explicit upper bound for the overall scriptL2‐gain of the output synchronization error with respect to disturbance. We demonstrate our developments by a simulation example.

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Connected cruise control with delayed feedback and disturbance: An adaptive dynamic programming approach

Huang

Gao

Jiang

2017

Adaptive Control & Signal

Self Cite

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This paper studies the connected cruise control problem for a platoon of human-operated and autonomous vehicles. The autonomous vehicles can receive motional data, ie, headway and velocity information from other vehicles by wireless vehicle-to-vehicle communication. The use of wireless communications in information exchange between vehicles inevitably causes input delay in the platooning system. Meanwhile, unpredictable behaviors of the leading vehicle constitute exogenous disturbance for the system. An adaptive optimal control problem with input delay and disturbance is formulated, and a novel data-driven control solution is proposed such that each vehicle in the platoon can achieve safe distance and desired velocity. By adopting an adaptive dynamic programming technique with sampled-data system theory, a data-driven adaptive optimal control approach is proposed for autonomous vehicles by the learning strategies of policy iteration without the accurate knowledge of the dynamics of all human drivers and vehicles. The efficacy of the proposed controller is substantiated by rigorous analysis and validated by simulation results in different scenarios. KEYWORDSadaptive dynamic programming, connected vehicles, time-delayed input INTRODUCTIONTransportation safety and congestion have been the two crucial problems in the major cities around the world for a long time. In the last decade, extensive efforts were devoted to seek innovative solutions for improving transportation efficiency and reducing accidents. Adaptive cruise control (ACC) has been proposed as an advanced driver assistance system to help solve these problems, where a vehicle is equipped with radars or lasers to acquire velocity and distance information from its preceding vehicle to keep a safe space and give the driver more comfort and convenience. 1-3 Nonetheless, the evaluation conducted by Werf et al 4 showed that the impact of ACC to highway systems is limited regarding traffic capacity and smoothness. By the combination of vehicle-to-vehicle (V2V) communication and ACC, cooperative ACC has demonstrated its potential to obtain better performance with a smaller intervehicular gap and improved traffic flow stability, 5 allowing connected vehicles actively exchange information. Several control methods have been investigated for designing cooperative ACC systems, such as model predictive control, 6 robust control, 7 optimal control, 8 and reinforcement learning. 9 356

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Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design

Cited by 213 publications

References 59 publications

Data‐driven–based attitude control of combined spacecraft with noncooperative target

Data‐driven–based attitude control of combined spacecraft with noncooperative target

Differential graphical games for H_∞ control of linear heterogeneous multiagent systems

Connected cruise control with delayed feedback and disturbance: An adaptive dynamic programming approach

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Value iteration and adaptive dynamic programming for data-driven adaptive optimal control design

Cited by 213 publications

References 59 publications

Data‐driven–based attitude control of combined spacecraft with noncooperative target

Data‐driven–based attitude control of combined spacecraft with noncooperative target

Differential graphical games for H∞ control of linear heterogeneous multiagent systems

Connected cruise control with delayed feedback and disturbance: An adaptive dynamic programming approach

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Differential graphical games for H_∞ control of linear heterogeneous multiagent systems