Event-Triggered Optimal Control for Partially Unknown Constrained-Input Systems via Adaptive Dynamic Programming

Zhu, Yuanheng; Zhao, Dan; He, Haibo; Ji, Junhong

doi:10.1109/tie.2016.2597763

Cited by 182 publications

(72 citation statements)

References 23 publications

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“…The assumption has been widely used in the literature. 22,33,34 Assumption 3. The augmented optimal control * (x) is locally Lipschitz continuous, ie, there exists a positive constant K such that…”

Section: Event-triggered Robust Controller Design Via Solving the Evementioning

confidence: 99%

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An adaptive critic approach to event‐triggered robust control of nonlinear systems with unmatched uncertainties

Yang

Wei

2018

Intl J Robust & Nonlinear

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In this paper, we develop a novel event-triggered robust control strategy for continuous-time nonlinear systems with unmatched uncertainties. First, we build a relationship to show that the event-triggered robust control can be obtained by solving an event-triggered nonlinear optimal control problem of the auxiliary system. Then, within the framework of reinforcement learning, we propose an adaptive critic approach to solve the event-triggered nonlinear optimal control problem. Unlike typical actor-critic dual approximators used in reinforcement learning, we employ a unique critic approximator to derive the solution of the event-triggered Hamilton-Jacobi-Bellman equation arising in the nonlinear optimal control problem. The critic approximator is updated via the gradient descent method, and the persistence of excitation condition is necessary. Meanwhile, under a newly proposed event-triggering condition, we prove that the developed critic approximator update rule guarantees all signals in the auxiliary closed-loop system to be uniformly ultimately bounded. Moreover, we demonstrate that the obtained event-triggered optimal control can ensure the original system to be stable in the sense of uniform ultimate boundedness. Finally, a F-16 aircraft plant and a nonlinear system are provided to validate the present event-triggered robust control scheme.

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Section: Event-triggered Robust Controller Design Via Solving the Evementioning

confidence: 99%

“…These assumptions are common techniques, which are often utilized in the literature for stability analyses. 22,33,[39][40][41][42] G(x) is the augmented control matrix defined as in (7).…”

Section: Stability Analysismentioning

confidence: 99%

An adaptive critic approach to event‐triggered robust control of nonlinear systems with unmatched uncertainties

Yang

Wei

2018

Intl J Robust & Nonlinear

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“…In robot control field, the DRL methods in continuous action spaces can establish the mapping from image inputs to the control policy which are concise [9] [10]. It has many applications in manipulator controling [11], intelligent driving [12] and games [13] [14]. In game decision making, the DRL methods in discrete action spaces, such as AlphaGo [15], show the strong search capability within the high-dimensional decision space.…”

Section: Introductionmentioning

confidence: 99%

Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment

Zhang

Zhao

2020

IEEE Trans. Neural Netw. Learning Syst.

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158

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This paper investigates the automatic exploration problem under the unknown environment, which is the key point of applying the robotic system to some social tasks. The solution to this problem via stacking decision rules is impossible to cover various environments and sensor properties. Learning based control methods are adaptive for these scenarios. However, these methods are damaged by low learning efficiency and awkward transferability from simulation to reality. In this paper, we construct a general exploration framework via decomposing the exploration process into the decision, planning, and mapping modules, which increases the modularity of the robotic system. Based on this framework, we propose a deep reinforcement learning based decision algorithm which uses a deep neural network to learning exploration strategy from the partial map. The results show that this proposed algorithm has better learning efficiency and adaptability for unknown environments. In addition, we conduct the experiments on the physical robot, and the results suggest that the learned policy can be well transfered from simulation to the real robot.

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“…Such controllers from the field of adaptive control have a longer history of investigating how to achieve a tradeoff between nominal performance as well as learning objectives. One challenge is that numerical algorithms for solving the dual control problem in higher dimensional spaces are often based on approximate dynamic programming, which turn out to be rather computational expensive [21], [38]. There is thus a great need for searching different approximations that can lead to tractable formulations without losing dual features.…”

Section: Introductionmentioning

confidence: 99%

A Scheme for Simultaneous Optimal Tracking Control and Experiment Design

Feng

Jiang

2020

IEEE Access

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This paper is about the formulation and numerical solutions of simultaneous optimal experiment design and optimal tracking control problems. Our motivating example is a robot arm that is mounted on a kitchen wall over the hotplates in order to assist humans with cooking. The robot arm can take over simple tasks such as stirring, automatic seasoning, or adding ingredients to a pot following a given recipe. Here, one of the main challenges is that the robot has to learn about the mass, inertial and other properties of the objects it is picking up while satisfying control tasks. Thus, we are facing a classical dual control problem, where system excitation for the purpose of learning has to be trade-off with other objectives such as tracking performance. After reviewing existing approaches, we propose a new formulation which allows to implement a trade-off between experiment design as well as tracking objectives in a systematic way by exploiting recent ideas from the field of economic experiment design. The approach is tested with a dynamic robot arm model performing a simple but illustrative cooking maneuver where learning and control goals are in conflict. INDEX TERMS Experiment design, tracking control, dual control.

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Event-Triggered Optimal Control for Partially Unknown Constrained-Input Systems via Adaptive Dynamic Programming

Cited by 182 publications

References 23 publications

An adaptive critic approach to event‐triggered robust control of nonlinear systems with unmatched uncertainties

An adaptive critic approach to event‐triggered robust control of nonlinear systems with unmatched uncertainties

Deep Reinforcement Learning-Based Automatic Exploration for Navigation in Unknown Environment

A Scheme for Simultaneous Optimal Tracking Control and Experiment Design

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