Adaptive hierarchical sliding mode control based on fuzzy neural network for an underactuated system

Abstract

We present an adaptive hierarchical sliding mode control based on fuzzy neural network for a class of underactuated systems to solve the problem of high-precision trajectory tracking. This system is viewed as several subsystems. One subsystem is used to design the first-layer sliding surface, which constructs the second-layer sliding surface with another subsystem. When the top layer includes all the subsystems, the design process is finished. Meanwhile, the equivalent control law and the switching control law are achieved at every layer. Because the hierarchical sliding mode control law relies excessively on the requirement of detailed information of the underactuated dynamic system, and because that method causes an inevitable chattering phenomenon, an online fuzzy neural network system is applied to mimic the hierarchical sliding mode control law. Moreover, the bounds of system uncertainties and modeling error caused by the fuzzy neural network system are estimated online by a robust term. The stability of the closed-loop system is guaranteed based on the Lyapunov theory and Barbalat's Lemma. Finally, the examples, a single-pendulum-type overhead crane system and an inverted pendulum system, are simulated to verify the effectiveness and robustness of the proposed method compared with some conventional methods.

A novel model-free adaptive terminal sliding mode controller for bridge cranes

“…A better control effect is obtained, even in the case of external interference. Therefore, a series of closed-loop control methods have been proposed, including model predictive control, 6 adaptive control, 7,8 output-feedback control, 9,10 intelligent control, [11][12][13][14] energy-based control [15][16][17][18] and sliding mode control. [19][20][21][22][23][24][25][26][27][28][29] The design of bridge crane control systems often involves the problems of model uncertainty, parameter perturbation and external interference in practical industrial applications, which can be solved by the sliding mode control efficiently.…”

“…A better control effect is obtained, even in the case of external interference. Therefore, a series of closed-loop control methods have been proposed, including model predictive control, 6 adaptive control, 7,8 output-feedback control, 9,10 intelligent control, 11–14 energy-based control 15–18 and sliding mode control. 19–29…”

Disturbance Observer-Based Super-Twisting Control for the Inertia Wheel Inverted Pendulum

“…Another SMC approach based on the LMI method is proposed to control a complex Tethered Satellite [6]. Other extensions of SMC based on fuzzy logic methods were also designed in the literature for UMSs in [7] [8] [9] [10].…”

Point-to-point motion control for flexible crane systems working in the deep sea

“…Although their specific models and characteristics are different, they all belong to typical underactuated systems with more degrees of freedom (DOFs) than their independent control inputs. [13][14][15][16][17][18][19][20][21][22] Underactuated systems have stronger nonlinearities with variables being coupled with each other, making it more difficult to analyze and solve their control problems compared with fully actuated systems. However, in terms of energy saving, structural complexity, and cost reduction, underactuated systems are superior to fully actuated ones.…”