Low-Complexity Fuzzy Neural Control of Constrained Waverider Vehicles via Fragility-Free Prescribed Performance Approach

In harsh battlefield environments, tanks have to encounter some nonlinear characteristics including frictional moment, gear backlash and parameter drifts, etc. The existence of such nonlinear characteristics makes the controller design of tank gun control systems (TGCSs) challenging. In this paper, a barrier neuroadaptive control approach is proposed to handle the uncertainties and nonlinearities, so as to achieve satisfactory tracking performance for TGCSs. With a time-varying barrier Lyapunov function employed in controller design, the output error of TGCS is restricted within the preset bound during the control process. A radial basis function (RBF) neural network is built to approximate the uncertainties in tank gun control systems. An anti-windup control strategy is developed to deal with the input saturation nonlinearity, with a Nussbaum function used to compensate for the nonlinear term arising from input saturation. By reasonably applying filtering error into output-constrained adaptive backstepping control design, the three steps in the traditional backstepping control design are reduced to two steps. The asymptotic stability of the closed-loop TGCSs is proven by Lyapunov theory. Finally, a simulation example is presented to verify the effectiveness of the proposed control scheme.INDEX TERMS Tank gun control systems; adaptive control; input saturation; time-varying output constraint

Section: B Convergence Analysismentioning

confidence: 96%

A Barrier Neuroadaptive Dynamic Surface Control Approach for Tank Gun Control Systems With Input Saturation

Zhu

Cai

et al. 2023

“…However, we use the advantage of fixed time convergence to compensate for this deficiency. After T 2 , the proof of the PPC method we designed is basically the same as the proof of the existing works [4][5][6][7][8].…”

Section: B Design and Analysis Of An Adaptive Sliding-mode Fault-tole...mentioning

confidence: 97%

“…The first concern focuses on the transient performance which excellent for the safe navigation of ACV. For achieving higher quality transient performance and high precision tracking errors, the prescribed performance control (PPC) [4][5][6] has been the focus of recent research by experts. This is attributed to its ability to ensure that the target error is restricted to a predefined set [10].…”

Section: Introductionmentioning

confidence: 99%

Global Prescribed Performance-Based Fault-Tolerant Control for Trajectory Tracking of the ACV With Uncertainty

Bai

Deng

2023

This paper focuses on a fixed-time prescribed performance-based Fault-tolerant controller that guarantees the global performance for the Trajectory tracking control of the air cushion vehicle (ACV). In the presence of faults, time-varying parameters and environmental disturbances, the proposed control system in this paper has good robustness and control performance. Firstly, an integrated observer for disturbances and faults is proposed to suppress all uncertainties in the system. Then a novel prescribed performance function is designed that does not need to be constrained by the initial value of the target errors while ensuring that it has fixed-time convergence performance. Global prescribed performance control has the advantage of separating the error initial conditions from the prescribed performance function. At the same time, combined with the global sliding mode control method, the fast convergence and robustness of the whole system are guaranteed. Finally, the simulation results verify the effectiveness of the control system.INDEX TERMS Air cushion vehicle, prescribed performance control, trajectory tracking, faulttolerant control.

“…Therefore, the developed controller can guarantee the tracking performance and reduce the control cost as much as possible. 2) Unlike the existing RL-based tracking control approaches [52] and [53] for ideal system model, this paper considers both input constraints and model uncertainties. By establishing an augmented system and designing a novel discounted non-quadratic performance index function, an RL-based constrained robust tracking controller is developed and it is more appropriate in practice.…”

Section: Proof the Lyapunov Function Candidate Is Established Asmentioning

confidence: 99%

Optimal Drug Dosage Control Strategy of Immune Systems Using Reinforcement Learning

Chen

Zhang

2023

In this article, a reinforcement learning-based drug dosage control strategy is developed for immune systems with input constraints and dynamic uncertainties to sustain the number of tumor and immune cells in an acceptable level. First of all, the state of the immune system and the desired number of tumor and immune cells are constructed into an augmented state to derive an augmented immune system. By designing a discounted non-quadratic performance index function, the robust tracking control problem of immune systems with uncertainties is transformed into an optimal tracking control problem of nominal immune systems and the drug dosage can be limited within the specified range. Hereafter, a reinforcement learning algorithm and a critic-only structure are adopted to acquire the approximate optimal drug dosage control strategy. Furthermore, theoretical proof reveals that the proposed reinforcement learning-based drug dosage control strategy ensures the number of tumor and immune cells reaches the preset level under limited drug dosages and model uncertainties. Finally, simulation study verifies the availability of the developed drug dosage control strategy in different growth models of tumor cell.