This paper reports our study on adaptive tracking control for a mobile-wheeled inverted pendulum with periodic disturbances and parametric uncertainties. With an appropriate reduced dynamic model, incorporating repetitive learning strategies with dynamic decoupling and related adaptive control techniques, a novel controller is successfully constructed to ensure that the output tracking errors of the system will stay within a small neighborhood around zero and all of the other signals are semiglobal uniform bounded. Meanwhile, only one parameter estimation is used for adaptive controller design, which overcomes the problem of over-parametrization. Furthermore, a required condition of period identifier mechanisms is proposed. Finally, detailed simulation results are presented to demonstrate the effectiveness of the proposed control schemes.

Adaptive Tracking Control of Wheeled Inverted Pendulums With Periodic Disturbances

This paper studies neural network-based tracking control of underactuated systems with unknown parameters and with matched and mismatched disturbances. Novel adaptive control schemes are proposed with the utilization of multi-layer neural networks, adaptive control and variable structure strategies to cope with the uncertainties containing approximation errors, unknown base parameters and time-varying matched and mismatched external disturbances. Novel auxiliary control variables are designed to establish the controllability of the non-collocated subset of the underactuated systems. The approximation errors and the matched and mismatched external disturbances are efficiently counteracted by appropriate design of robust compensators. Stability and convergence of the time-varying reference trajectory are shown in the sense of Lyapunov. The parameter updating laws for the designed control schemes are derived using the projection approach to reduce the tracking error as small as desired. Unknown dynamics of the non-collocated subset is approximated through neural networks within a local region. Finally, simulation studies on an underactuated manipulator and an underactuated vibro-driven system are conducted to verify the effectiveness of the proposed control schemes.

/pdf/adaptive-neural-network-tracking-control-for-underactuated-bvzgc3xnm6.pdf

Adaptive neural network tracking control for underactuated systems with matched and mismatched disturbances

A survey on underactuated robotic systems: Bio-inspiration, trajectory planning and control

This paper studies the issue of adaptive trajectory tracking control for an underactuated vibro-driven capsule system and presents a novel motion generation framework. In this framework, feasible motion trajectory is derived through investigating dynamic constraints and kernel control indexes that underlie the underactuated dynamics. Due to the underactuated nature of the capsule system, the global motion dynamics cannot be directly controlled. The main objective of optimization is to indirectly control the friction-induced stick–slip motions to reshape the passive dynamics and, by doing so, to obtain optimal system performance in terms of average speed and energy efficacy. Two tracking control schemes are designed using a closed-loop feedback linearization approach and an adaptive variable structure control method with an auxiliary control variable, respectively. The reference model is accurately matched in a finite-time horizon. The key point is to define an exogenous state variable whose dynamics is employed as a control input. The tracking performance and system stability are investigated through rigorous theoretic analysis. Extensive simulation studies are conducted to demonstrate the effectiveness and feasibility of the developed trajectory model and optimized adaptive control system.

/pdf/optimized-adaptive-tracking-control-for-an-underactuated-50glkzqynq.pdf

Optimized adaptive tracking control for an underactuated vibro-driven capsule system

This paper is concerned with a model predictive control (MPC) technique together with an adaptive robust scheme for the trajectory tracking of a wheeled inverted pendulum vehicle in the absence of platform velocity information, in addition to taking dynamic uncertainty and external disturbance into consideration. Specifically, to deal with velocity information loss and dynamic uncertainty, an extended state observer is introduced to evaluate the velocity vectors and model dynamics, where the uniformly ultimately bounded property of observer system can be guaranteed by using the Lyapunov stability theorem. With these observations, MPC is employed for the underactuated longitudinal subsystem to achieve longitudinal velocity tracking, as well as holding the pendulum-like vehicle body stability, and in particular taking the state and input saturation into account; at the same time, an adaptive robust controller is constructed for the rotational subsystem to realize the rotational velocity tracking, in which the adaptive laws can enhance the vehicle adaptability in diverse environment. In addition, a saturated trajectory generator with closed-loop characteristic is introduced so as to properly handle the velocity limitation and nonholonomic constraint simultaneously. The simulation results validate that the control system is robust against the external disturbance and model uncertainty, thereby demonstrating the effectiveness and robustness of the proposed control strategy.

Constrained Adaptive Robust Trajectory Tracking for WIP Vehicles Using Model Predictive Control and Extended State Observer

Indirect adaptive fuzzy control for a nonholonomic/underactuated wheeled inverted pendulum vehicle based on a data-driven trajectory planner

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Indirect adaptive fuzzy control for a nonholonomic/underactuated wheeled inverted pendulum vehicle based on a data-driven trajectory planner