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In this paper, an approximation-based adaptive tracking control approach is proposed for a class of multiinput multioutput nonlinear systems. Based on the method of neural network, a novel adaptive controller is designed via backstepping design process. Furthermore, by introducing Nussbaum function, the issue of unknown control directions is handled. In the backstepping design process, the dynamic surface control technique is employed to avoid differentiating certain nonlinear functions repeatedly. Moreover, in order to reduce the number of adaptation laws, we do not use the neural networks to directly approximate the unknown nonlinear functions but the desired control signals. Finally, we provide two examples to illustrate the effectiveness of the proposed approach.

A robotic exoskeleton is a nonlinear system, which is subjected to parametric uncertainties and external disturbances. Due to this reason, it is difficult to obtain the exact model of the system, and without knowledge of the system, it cannot be compensated accurately. In this study, time delay estimation (TDE)-based model-free fractional-order nonsingular fast terminal sliding mode control (MFF-TSM) is proposed for the lower-limb robotic exoskeleton in the existence of uncertainties and external disturbance. The main characteristic of the proposed scheme is that it controls the system without relying on the knowledge of exoskeleton dynamics. At first, the fractional-order (FO) with nonsingular fast terminal sliding mode control (NFTSM) is adopted to provide a precise trajectory tracking performance, fast finite-time speed of convergence, singularity-free and chatter-free control inputs. And then, the proposed controller employs TDE, to make the controller model independent, which directly estimates the uncertain exoskeleton dynamics with external disturbances. Later, asymptotical stability analysis of the overall system and finite-time convergence are investigated and ensured using Lyapunov theorem. Finally, the simulation results are conducted to validate the efficacy of the proposed control method.

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