Robust adaptive intelligent sliding model control for a class of uncertain chaotic systems with unknown time-delay

Abstract

In this paper, a robust adaptive intelligent sliding model control (RAISMC) scheme for a class of uncertain chaotic systems with unknown time-delay is proposed. A sliding surface dynamic is appropriately constructed to guarantee the reachability of the specified sliding surface. Within this scheme, neurofuzzy network (NFN) is utilized to approximate the unknown continuous function. The robust controller is an adaptive controller used to dispel the unknown uncertainty and approximation errors. The adaptive parameters of the control system are tuned on-line by the derived adaptive laws based on a Lyapunov stability analysis. Using appropriate Lyapunov-Krasovskii (L-K) functional in the Lyapunov function candidate, the uncertainty caused by unknown time delay is compensated and the global asymptotic stability of the error dynamics system in the specified switching surface is accomplished. Finally, the proposed RAISMC system is applied to control a Hopfield neural network, Cellular neural networks, Rössler system, and to achieve synchronization between the Chen system with two time delays with Rössler system without time delay. The results are representative of outperformance of the proposed method in all cases.

Synchronization of Unknown Uncertain Chaotic Systems Via Adaptive Control Method

“…Consider the synchronization error system (7) with linear control inputs. If this system is controlled by the controllers (8) and (9), then the error system trajectories will converge to zero.…”

“…The other main aspect of the chaotic systems is that their trajectories are always locally unbounded and globally bounded, which result in unbalanced attractors [1,2]. Recently, various synchronization approaches of the chaotic systems have been suggested, such as linear feedback control [3], optimal control [4], sliding mode control [5][6][7][8][9][10], finite-time control [11][12][13][14][15][16], adaptive control [17][18][19][20], and so on.…”

Dynamic-free robust adaptive intelligent fault-tolerant controller design with prescribed performance for stable motion of quadruped robots

“…To overcome the above-mentioned problems, adaptive intelligent–based MFC approach can be a good candidate for handling of faults and uncertainties in FTC problems (Liu et al, 2012; Farid & Bigdeli, 2013). The intelligent networks as universal approximators have been emerged as a promising setup for controlling systems with unknown nonlinearities (Farid & Bigdeli, 2012). In recent years, there has been a growing interest in using Takagi-Sugeno fuzzy network (TSFN) in the control community (Li et al, 2018).…”

Nonlinear Robust Adaptive Deterministic Control for Flexible Hypersonic Vehicles in the Presence of Input Constraint

“…A potential drawback of this method is that the upper bound of the model uncertainties has to be known exactly in advance, which may be difficult to obtain exactly in practice. Sliding Mode Control (SMC) as a key choice for handling bounded uncertainties has been applied in flight control systems. For example, an adaptive sliding mode controller was designed based on the linearized model for flexible hypersonic vehicles .…”