Fault Detection for Fuzzy Semi-Markov Jump Systems Based on Interval Type-2 Fuzzy Approach

Summary In this paper, a class of interconnected systems is considered, where the nominal isolated systems are fully nonlinear. A robust decentralized sliding mode control based on static state feedback is developed. By local coordinate transformation and feedback linearization, the interconnected system is transformed to a new regular form. A composite sliding surface which is a function of the system state variables is proposed and the stability of the corresponding sliding mode dynamics is analyzed. A new reachability condition is proposed and a robust decentralized sliding mode control is then designed to drive the system states to the sliding surface in finite time and maintain a sliding motion thereafter. Both uncertainties and interconnections are allowed to be unmatched and are assumed to be bounded by nonlinear functions. The bounds on the uncertainties and interconnections have more general forms when compared with existing work. A MATLAB simulation example is used to demonstrate the effectiveness of the proposed method.

Section: Definition 2 (29) Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

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

Feng

Zhao

Yan

et al. 2020

“…Consider the nonlinear discrete-time system (1) with unmodeled dynamics. On the compact sets Ω z i , i = 1, … , n, and under Assumption 1, the virtual controllers are given by (38) and (49), the actual controller is designed as (59), and the update laws are shown as (35), (45), (56), and (65). If the design parameters are chosen appropriately, and the convergence factor is chosen as…”

Section: Stability Analysismentioning

confidence: 99%

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

Bai

Zhang

Zhou

et al. 2019

Summary In this paper, an adaptive reinforcement learning approach is developed for a class of discrete‐time affine nonlinear systems with unmodeled dynamics. The multigradient recursive (MGR) algorithm is employed to solve the local optimal problem, which is inherent in gradient descent method. The MGR radial basis function neural network approximates the utility functions and unmodeled dynamics, which has a faster rate of convergence than that of the gradient descent method. A novel strategic utility function and cost function are defined for the affine systems. Finally, it concludes that all the signals in the closed‐loop system are semiglobal uniformly ultimately bounded through differential Lyapunov function method, and two simulation examples are presented to demonstrate the effectiveness of the proposed scheme.

“…Subsequently, the corresponding results were further extended to solve consensus problem for second‐order consensus in linear MASs with time delays . More recently, the distributed adaptive consensus approaches have been reported for nonlinear delayed MASs by using fuzzy logic systems or neural networks (NNs) to approximate the unknown nonlinear functions …”

Section: Introductionmentioning

confidence: 99%

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

Xiao

Cao

Dong

et al. 2019

Summary In this paper, the consensus tracking problem is investigated for stochastic nonlinear multiagent systems with full state constraints and time delays. The barrier Lyapunov functions proposed for single‐agent constrained systems are constructively extended to solve the consensus problem for multiagent systems with the full state constraints. Some Lyapunov‐Krasovskii functionals are introduced to compensate for state time delays, which are inherent in the complicated nonlinear systems. Based on the variable separation technique, the difficulty arising from the nonstrict‐feedback structure is overcome. Under a directed communication topology, the distributed neuroadaptive control protocols are proposed to guarantee that all the follower agents follow the trajectory of the leader agent and the full state constraints are not violated. The effectiveness of the proposed distributed adaptive control approach is verified via simulation examples.