Direct adaptive NN control of a class of nonlinear systems

Ge, Shuzhi Sam; Wang, Cong

doi:10.1109/72.977306

Cited by 544 publications

(210 citation statements)

References 20 publications

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“…신경망 기반 적응제어기법의 가장 큰 장점은 모델링된 시스템과 실제 시스템의 차이를 효과적으로 보상할 수 있 다는 것이다 [6][7][8][9][10]. 더욱이, 그러한 시스템의 차이가 구조가 없는 불확실한 요소(unstructured uncertainty)라 하더라도 효 과적으로 추정 및 보상이 가능하다.…”

Section: 을 제안하였다 본 논문에서는 실제 무인차량과 동역학 모 델과의 차이를 보상하기 위해 신경망을 활용한 적응제unclassified

NN-based Adaptive Control for a Skid-type Autonomous Unmanned Ground Vehicle

Shin¹,

Joo²

2014

Journal of Institute of Control, Robotics and Systems

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This study proposes a NN (Neural Networks)-based adaptive control method for a 6X6 skid-type UGV (Unmanned Ground Vehicle) with 6 in-wheel motors. The UGV experiences lots of uncertainties and, thus, the control performance can degrade significantly without a compensation of the unknown terms. To improve the control performance of the UGV, the NN is utilized to design the adaptive controller. Then, the designed overall force and moment are optimally distributed into 6 traction forces with the assumption that six vertical forces of the UGV are known exactly, because the six traction forces are original source to be excited to the UGV to move. Finally, numerical simulations with the TruckSim model are performed to validate the effectiveness of the proposed approach. Keywords

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Section: 을 제안하였다 본 논문에서는 실제 무인차량과 동역학 모 델과의 차이를 보상하기 위해 신경망을 활용한 적응제unclassified

NN-based Adaptive Control for a Skid-type Autonomous Unmanned Ground Vehicle

Shin¹,

Joo²

2014

Journal of Institute of Control, Robotics and Systems

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“…Lemma 8 (see [32,33] (15)-(40), the Semi-Globally Uniform Boundedness (SGUB) of 1 , 2 , ,̃is proven. The tracking error is limited as | 1 | ≤ lim .…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Neural Network Based Central Heating System Load Prediction and Constrained Control

Wang

Tu³

et al. 2018

Mathematical Problems in Engineering

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A neural network (NN) based heating system load prediction and control scheme are proposed. Different from traditional physical principle based load calculation method, a multilayer NN is incorporated with selected input features and trained to predict the heating load as well as the desired supply water temperature in heating supply loop. In this manner, a complicated load calculation model can be replaced by simple but efficient data-driven scheme and the response time to outdoor temperature variation can be enhanced. Moreover, in order to handle the input and output constraints in valve opening degree control task to achieve desired supply water temperature, Barrier Lyapunov candidate function and axillary system technique are involved. An additional NN is employed to approximate the system transfer function with reliable accuracy. The stability of the system is guaranteed through rigorous mathematical analysis. The excellent performance of the novelly proposed control over traditional PID is demonstrated via extensive simulation study. A quantitative case study is also conducted to verify the flexibility and validity of proposed load prediction strategy.

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“…This problem generally occurs in the design of neural networks (NNs) during backstepping procedure. To overcome this problem, some researchers used the intermediate variables as neural inputs to reduce the dimension of neural network input vector [20]. The method in [20] did work well but the problem remained unsolved.…”

Section: Introductionmentioning

confidence: 99%

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

Wang

2017

Complexity

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This paper focuses on neural learning from adaptive neural control (ANC) for a class of flexible joint manipulator under the output tracking constraint. To facilitate the design, a new transformed function is introduced to convert the constrained tracking error into unconstrained error variable. Then, a novel adaptive neural dynamic surface control scheme is proposed by combining the neural universal approximation. The proposed control scheme not only decreases the dimension of neural inputs but also reduces the number of neural approximators. Moreover, it can be verified that all the closed-loop signals are uniformly ultimately bounded and the constrained tracking error converges to a small neighborhood around zero in a finite time. Particularly, the reduction of the number of neural input variables simplifies the verification of persistent excitation (PE) condition for neural networks (NNs). Subsequently, the proposed ANC scheme is verified recursively to be capable of acquiring and storing knowledge of unknown system dynamics in constant neural weights. By reusing the stored knowledge, a neural learning controller is developed for better control performance. Simulation results on a single-link flexible joint manipulator and experiment results on Baxter robot are given to illustrate the effectiveness of the proposed scheme.

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Direct adaptive NN control of a class of nonlinear systems

Cited by 544 publications

References 20 publications

NN-based Adaptive Control for a Skid-type Autonomous Unmanned Ground Vehicle

NN-based Adaptive Control for a Skid-type Autonomous Unmanned Ground Vehicle

Neural Network Based Central Heating System Load Prediction and Constrained Control

Neural Learning Control of Flexible Joint Manipulator with Predefined Tracking Performance and Application to Baxter Robot

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