Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints

Liu, Yan Jun; Li, Jing; Tong, Shaocheng; Chen, C. L. Philip

doi:10.1109/tnnls.2015.2508926

Cited by 441 publications

(224 citation statements)

References 81 publications

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“…(2) In construct to previous output-constraint neural/ fuzzy back-stepping control approach [15,25,38,40,41], the proposed control scheme does not need numerous NNs/FLS to construct virtual and practical control law in each step, only one neural network including one adaptive laws is required to approximate the lumped unknown function, thus deriving a low-computational control scheme.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

“…Compared with the previously proposed outputconstraint [40] or prescribed performance control [41,44,45] adaptive back-stepping approach, the proposed structure is extremely simple and the computational burden is really low since there is only actual controller required to be implemented and there exists single neural network to approximate the lump uncertainty. It also be noted that the issue of explosion of the complexity inherent in the traditional backstepping approach is completely removed without employing DSC, command filter, or differentiator technique.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

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Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Shi

2017

Mathematical Problems in Engineering

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This paper focuses on a single neural network tracking control for a class of nonlinear strict-feedback systems with input dead-zone and time-varying output constraint via prescribed performance method. To release the limit condition on previous performance function that the initial tracking error needs to be known, a new modified performance function is first constructed. Further, to reduce the computational burden of traditional neural back-stepping control approaches which require all the virtual controllers to be necessarily carried out in each step, the nonlinear items are transmitted to the last step such that only one neural network is required in this design. By regarding the input-coefficients of the dead-zone slopes as a system uncertainty and introducing a new concise system transformation technique, a composite adaptive neural state-feedback control approach is developed. The most prominent feature of this scheme is that it not only owes low-computational property but also releases the previous limitations on performance function and is capable of guaranteeing the output confined within the new form of prescribed bound. Moreover, the closed-loop stability is proved using Lyapunov function. Comparative simulation is induced to verify the effectiveness.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Shi

2017

Mathematical Problems in Engineering

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“…Thus, each sensor should process its own measurements independently in order to reduce state estimates. 29 Suppose that two tracks of telerobots are initialed in the ST2TA, then the state estimates from two heterogeneous sensors should be communicated together for completing multi-track fusion.…”

Section: Problem Statementsmentioning

confidence: 99%

Innovative sequential track-to-track algorithm for telerobots tracking based on heterogeneous sensors

Wang

2017

Advances in Mechanical Engineering

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In order to improve efficiency and accuracy of the existing telerobots tracking schemes, we present an innovative sequential track-to-track algorithm based on heterogeneous sensors in this article. Considering the effect of the difference of state estimation error on log-likelihood ratio, the modified sequential difference is first derived during the whole surveillance period. According to the chi-square test, the log-likelihood ratio under two hypotheses is further discussed using the weighted coefficient step by step. Subsequently, the extension for maneuvering telerobots tracking is derived based on the unscented Kalman filter. Finally, the numerical studies results indicate that the proposed sequential trackto-track algorithm has promising performance for tracking telerobots with various motion states.

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“…For output constraints, artificial potential field [15], prescribed performance control [16,17], model predictive control [18], and reference governor [19] are some of the existing strategies to handle this problem. In [20,21], Barrier Lyapunov Function (BLF) is introduced which needs less initial conditions and does not require explicit system solution. Based on these literature reports, in this paper, the input and output constraint problem is tentatively merged with rigorous mathematical stability analysis to ensure accurate heating temperature control and limited valve opening degree.…”

Section: Introductionmentioning

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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Neural Network Control-Based Adaptive Learning Design for Nonlinear Systems With Full-State Constraints

Cited by 441 publications

References 81 publications

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Constrained Adaptive Neural Control of Nonlinear Strict‐Feedback Systems with Input Dead‐Zone

Innovative sequential track-to-track algorithm for telerobots tracking based on heterogeneous sensors

Neural Network Based Central Heating System Load Prediction and Constrained Control

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