Neural-Based Decentralized Adaptive Finite-Time Control for Nonlinear Large-Scale Systems With Time-Varying Output Constraints

Decentralized sliding mode control for a class of nonlinear interconnected systems by static state feedback

“…Robust control schemes and adaptive control schemes are often designed to reject or reduce the effect of the uncertainties caused by the modeling errors within subsystems and the coupling among subsystems. Adaptive control can only be applied to some special systems, that is, parametric uncertainties satisfying linear growth conditions . Alternative robust control methods, such as H ∞ control, have strong robustness in dealing with large‐scale operating regions even in the presence of disturbance scenarios, but the design process is complex and the synthesis can be difficult and time‐consuming …”

“…Adaptive control can only be applied to some special systems, that is, parametric uncertainties satisfying linear growth conditions. 8,9 Alternative robust control methods, such as H ∞ control, 10,11 have strong robustness in dealing with large-scale operating regions even in the presence of disturbance scenarios, but the design process is complex and the synthesis can be difficult and time-consuming. 12,13 As one of the classical robust control methods, sliding mode control 14 is widely considered to be an effective method to deal with uncertainties due to its excellent performance characteristics and relatively simple design process.…”

Adaptive consensus control for stochastic nonlinear multiagent systems with full state constraints

“…[5][6][7] Based on fixed or switching topologies, preliminary results for the consensus problem were presented in previous works. [8][9][10][11][12] Among them, the leaderless problem was discussed by Xi et al 9 and leader-following problem was studied by Liu and Yang, 8 Olfati-saber et al, 10 and Liang et al 12 To address the consensus control problem, some discussions have been reported on this issue with the combination of finite-time synchronization, 13,14 event-triggered control, [15][16][17][18] adaptive optimal control, 19,20 and adaptive fault-tolerant control. [21][22][23] However, the aforementioned consensus methods were employed under the assumption that there are no stochastic disturbance and time delays in the controlled system.…”

Multigradient recursive reinforcement learning NN control for affine nonlinear systems with unmodeled dynamics

“…Affine system is a general class of systems, in which the state feedback variable or the control input can be linearly separated, for example, where the feedback state x i +1 ( k ) and control input u ( k ) can be linearly separated, describes the unmodeled dynamics. Many physical application system models can be described by this kind of affine system, for example, robotic manipulator, marine vessels, single‐link flexible manipulator, large‐scale systems, and wheeled inverted pendulums . Recently, many related results have been carried out for affine systems with unmodeled dynamics.…”

“…where the feedback state x i+1 (k) and control input u(k) can be linearly separated, i (x i (k)) describes the unmodeled dynamics. Many physical application system models can be described by this kind of affine system, for example, robotic manipulator, 31,32 marine vessels, 33,34 single-link flexible manipulator, 35,36 large-scale systems, 37,38 and wheeled inverted pendulums. 39 Recently, many related results have been carried out for affine systems with unmodeled dynamics.…”