Adaptive Neural Network Control for a Class of Nonlinear Systems With Function Constraints on States

Liu, Yanjun; Zhao, Wei; Li, Dapeng; Tong, Shaocheng; Chen, C. L. Philip

doi:10.1109/tnnls.2021.3107600

Cited by 185 publications

(67 citation statements)

References 50 publications

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“…In addition, since the improved LKF method and the improved quadratic function method are not utilized in this paper, there exists limitation in the proposed result; the conservatism of the proposed method can be further reduced by using the improved LKF method and the improved quadratic function method. However, to extend the proposed method to adaptive control issue, [28][29][30] reinforcement learning issue, [31][32][33] BAM neural networks, [34][35][36][37] and uncertain stochastic system 38 are the future work.…”

Section: 𝓁(T))mentioning

confidence: 99%

Stability analysis of systems with time‐varying delay via a delay‐product‐type integral inequality

Tan

Wang

2022

Math Methods in App Sciences

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This paper is concerned with the issue of stability analysis of systems with time‐varying delay. First, a delay‐product‐type (DPT) integral inequality is proposed, which combines the third‐order Bessel–Legendre (TOBL) integral inequality and the reciprocally convex (RC) inequality into a unified integral inequality; then, a tight upper bound on the time‐derivative of the Lyapunov–Krasovskii functional (LKF) can be obtained. Second, an augmented LKF is tailored for the use of the DPT integral inequality; then, more information about the instant state and the delayed states is taken into account. Third, by using the DPT integral inequality and the augmented LKF, some less conservative conditions that ensure the stability of systems with time‐varying delay are derived. Finally, simulations are provided to illustrate the advantage of our proposed method.

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Section: 𝓁(T))mentioning

confidence: 99%

Stability analysis of systems with time‐varying delay via a delay‐product‐type integral inequality

Tan

Wang

2022

Math Methods in App Sciences

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“…It is also worth mentioning that the state constraints here are expressed in terms of convex functions, which is a geometric characterization compared to other works using constraint boundary functions; e.g. [35], [36]. This approach is more amenable to our analysis of projection operator below while not too restrictive.…”

Section: B System Dynamicsmentioning

confidence: 99%

Scaled Consensus and Reference Tracking in Multiagent Networks With Constraints

Shang

2022

IEEE Trans. Netw. Sci. Eng.

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“…An extensive body of research exists on tracking algorithms for nonholonomic vehicles, including but not limited to Lyapunov method [32], sliding mode control [33], vector-field-orientation method [30], adaptive robust control [25], model predictive control [34], and adaptive neural networks [35]. Advanced design methods can be used when input and state constraints are present [36], [37]. Low complexity, guaranteed stability, tracking accuracy, and convenient integration with the safety control are the primary design criteria for the tracking control.…”

Section: Tracking Control With Guaranteed Stabilitymentioning

confidence: 99%

Exponential Barrier Functions for Safe Steering of Nonholonomic Vehicles With Actuator Time-Delay

Ghaffari

Desai

2022

IEEE Access

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This work provides a provably safe feedback control for nonholonomic vehicles that autonomously operate in an obstacle field. A barrier function with a tunable, exponential decay rate is used to obtain a safe steering envelope for the vehicle. The safe steering envelope adapts, in real-time, to the vehicle's velocity and its distance to the static obstacles. The safety control corrects steering commands provided by a nominal tracking control and prevents collisions between the vehicle and the obstacles. The safety and stability of the algorithm are proved analytically and verified via multiple experiments. The resulting safety control is modular and can work well with obstacles of different footprints. Since quick steering control is essential for successful vehicle navigation, a two-layer predictor is proposed to compensate for the time-delay in the vehicle dynamics. The two-layer predictor improves the control response time by as much as a factor of four. The safety and tracking control act on the vehicle kinematics, and the two-layer predictor improves the vehicle's dynamic performance. The proposed control structure has a closed-form with eight tunable parameters, which facilitates control calibration and tuning in large systems of vehicles. Extensive experiments are carried out on a nonholonomic vehicle to verify the effectiveness of the proposed algorithm.INDEX TERMS Collision avoidance, vehicle safety, unmanned autonomous vehicles.

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Adaptive Neural Network Control for a Class of Nonlinear Systems With Function Constraints on States

Cited by 185 publications

References 50 publications

Stability analysis of systems with time‐varying delay via a delay‐product‐type integral inequality

Stability analysis of systems with time‐varying delay via a delay‐product‐type integral inequality

Scaled Consensus and Reference Tracking in Multiagent Networks With Constraints

Exponential Barrier Functions for Safe Steering of Nonholonomic Vehicles With Actuator Time-Delay

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