Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Discrete-Time Systems

Dong, Lu; Zhong, Xiangnan; Sun, Changyin; He, Haibo

doi:10.1109/tnnls.2016.2541020

Cited by 166 publications

(45 citation statements)

References 41 publications

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“…Remark It can be seen that the discrete‐time event‐triggered system is asymptotically stable with triggering condition under Assumption . Compared with the existing work, the new triggering condition needs fewer assumptions to stabilize the discrete‐time systems.…”

Section: Triggering Condition and Stability Analysismentioning

confidence: 99%

“…Example First, apply the method to the mass‐spring‐damper system . The state‐space model is given as follows:

({, \begin{matrix} array \dot{x_{1}} = x_{2}, \\ array \dot{x_{2}} = - \frac{b}{m} - \frac{k_{s1}}{m} + \frac{F_{ext}}{m}, \end{matrix})

where m =1 kg is the mass of the body and

k_{sans-serif s1} = 9

N/m is the linear spring constant.…”

Section: Examplesmentioning

confidence: 99%

“…Theorem 2. If the event-triggered systems update the weights of NNs through the update laws (33) and (37), no matter whether the triggering condition is satisfied or not, the closed-loop state x(k) and the network weight approximation errors w c andw a are UUB under the conditions that…”

Section: Action Networkmentioning

confidence: 99%

“…First, apply the method to the mass-spring-damper system. 37 The state-space model is given as follows:…”

Section: Examplementioning

confidence: 99%

“…The difficulty of solving the event‐triggered optimal control problems using HDP algorithm is that many hypotheses need to be established to stabilize the system. Compared with the existing work, main contributions of this paper include the following. A novel triggering condition of event‐triggered HDP algorithm is studied, which needs fewer assumptions to stabilize the system.…”

Section: Introductionmentioning

confidence: 99%

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A novel triggering condition of event‐triggered control based on heuristic dynamic programming for discrete‐time systems

Wang

Wei

Liu

2018

Optim Control Appl Methods

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Summary In this paper, an event‐triggered heuristic dynamic programming algorithm for discrete‐time nonlinear systems with a novel triggering condition is studied. Different from traditional heuristic dynamic programming algorithms, the control law in this algorithm will only be updated when the triggering condition is satisfied to reduce the computational burden. Three neural networks are employed, which are model network, action network, and critic network. Model functions, control laws, and value functions are estimated using neural networks, respectively. The main contribution of this algorithm is the novel triggering condition with simpler form and fewer assumptions. Additionally, a proof of the stability for discrete‐time systems using Lyapunov technique is given. Finally, two simulations are shown to verify the effectiveness of the developed algorithm.

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Section: Triggering Condition and Stability Analysismentioning

confidence: 99%

“…Example First, apply the method to the mass‐spring‐damper system . The state‐space model is given as follows:

({, \begin{matrix} array \dot{x_{1}} = x_{2}, \\ array \dot{x_{2}} = - \frac{b}{m} - \frac{k_{s1}}{m} + \frac{F_{ext}}{m}, \end{matrix})

where m =1 kg is the mass of the body and

k_{sans-serif s1} = 9

N/m is the linear spring constant.…”

Section: Examplesmentioning

confidence: 99%

Section: Action Networkmentioning

confidence: 99%

“…First, apply the method to the mass-spring-damper system. 37 The state-space model is given as follows:…”

Section: Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A novel triggering condition of event‐triggered control based on heuristic dynamic programming for discrete‐time systems

Wang

Wei

Liu

2018

Optim Control Appl Methods

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Robot ℋ2 Neural Control Using Reinforcement Learning

Yu¹,

Perrusquía²

2021

Human‐Robot Interaction Control Using Reinforcement Learning

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Adaptive optimal tracking control with novel event‐triggered formulation for a type of nonlinear systems

Wang

Qiao

2022

Adaptive Control & Signal

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In this article, an event-based intelligent critic algorithm is developed to address the optimal tracking control problem for a type of discrete-time nonlinear systems. The nonlinear optimal tracking control design is replaced by solving the optimal regulation problem of the error system. Then, the generalized value iteration algorithm is employed to obtain the admissible tracking control law with off-line learning. Next, a novel triggering condition is designed to reduce the update times of the controller and improve the resource utilization. It is emphasized that this triggering condition is established based on the iteration of the time-triggered mechanism. Moreover, in order to realize the cost guarantee of the error system, the real cost function is proved to possess a predetermined upper bound. By analysis, it is shown that the error system is asymptotically stable while the tracking error and the sampling signal are uniformly ultimately bounded during the process of training neural networks. Finally, two examples are conducted to demonstrate the effectiveness of the proposed algorithm.

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Adaptive Event-Triggered Control Based on Heuristic Dynamic Programming for Nonlinear Discrete-Time Systems

Cited by 166 publications

References 41 publications

A novel triggering condition of event‐triggered control based on heuristic dynamic programming for discrete‐time systems

A novel triggering condition of event‐triggered control based on heuristic dynamic programming for discrete‐time systems

Robot ℋ2 Neural Control Using Reinforcement Learning

Adaptive optimal tracking control with novel event‐triggered formulation for a type of nonlinear systems

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