Backstepping-based Adaptive Control Research Articles

Backstepping-based adaptive control of underactuated AUV subject to unknown dynamics and zero tracking errors

This paper introduces a zero-error tracking control solution for underactuated autonomous underwater vehicle (AUV) facing unknown dynamic and environmental disturbances. The proposed approach integrates the backstepping method with adaptive techniques and neural networks to effectively address the challenges posed by uncertainties and external disturbances, thereby enhancing the robustness and adaptability of AUV. Subsequently, a boundary estimation method is employed to dynamically update a single adaptive parameter online, leading to a significant reduction in computational workload. To achieve superior asymptotic tracking performance, a different coordinate transformation of fusion scalar function is designed. Moreover, a rigorous analysis of the proposed control scheme is conducted using the Lyapunov-based method, establishing that the tracking error converges asymptotically to zero. Simulation experiments are executed to illustrate the efficacy of the proposed control scheme.

Safety-enhanced observer-based adaptive fuzzy synchronization control framework for teleoperation systems

Due to the lake of enough information about the remote environment, safety and performance play critical role in the teleoperation systems. However, some applications such as tele-robotic surgery require high performances and the outcomes of delayed leader-follower communication and external forces measurement can be catastrophic in such area. In hence, to guarantee the predefined performances for the synchronization error and enhance the safeness of operation in the presence of system uncertainties, external disturbances and time-varying delay, the error constrained control is employed to the proposed controller design mechanism by the Barrier Lyapunov function (BLF). The key idea of this paper is to develop a new adaptive fuzzy torque observer and relax the system from the practical limitations of force sensors. Moreover, the main contribution of this paper is to deal with time-delay’s derivative of communication channels and lessen the computational complexity in the backstepping-based adaptive controller by means of the dynamic surface control and the BLF strategy is embedded to enforce bounds on the leader-follower position synchronization errors. In addition, the stability analysis of the observer and controller together is studied via the BLF in a unified farmwork. Finally, the evaluation of performance of the proposed observer-based adaptive fuzzy synchronization controller, is investigated by the help of various scenarios and comparison.

Backstepping-Based Adaptive Control of a Class of Uncertain Incommensurate Fractional-Order Nonlinear Systems With External Disturbance

Normally, achieving asymptotic convergence in the presence of unknown arbitrarily bounded external disturbance requires noncontinuous control signals. In this article, an adaptive backstepping controller for a class of incommensurate fractional-order nonlinear systems with parametric uncertainties and external time-varying disturbance is proposed to realize asymptotic perfect output tracking. Appropriate adaptive laws are designed to deal with the uncertainties and unknown bounded disturbance. In the designed adaptive backstepping control scheme, an auxiliary function is adopted to obtain a smooth control signal. It is theoretically shown that asymptotic output tracking to a given reference signal is achieved while ensuring global system stability in the sense that all the closed-loop signals are bounded. Simulation and experimental studies demonstrate the effectiveness of the proposed backstepping control scheme.

Backstepping-based adaptive control of a quadrotor UAV with guaranteed tracking performance

In this paper, a backstepping based indirect adaptive control design and an alternative direct adaptive control scheme, both with guaranteed transient and steady-state tracking performances, are proposed for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV). Backstepping techniques, combined with a prescribed performance function based error transformation, are employed in both designs to achieve the bounded transient and steady-state tracking errors of the strict-feedback position system which comprises both lateral position and altitude dynamics. The effects of parametric inertia and drag uncertainties on attitude regulation are compensated using a least squares based parameter identification algorithm in the indirect adaptive control design, and using a constructive Lyapunov analysis approach in the direct adaptive control scheme. The stability of the closed-loop system for both designs is proven via Lyapunov analysis. Simulation and experimental test results are provided to verify the effectiveness of the proposed control designs.

Adaptive Neural Quantized Control for a Class of MIMO Switched Nonlinear Systems With Asymmetric Actuator Dead-Zone.

This paper concentrates on the adaptive state-feedback quantized control problem for a class of multiple-input-multiple-output (MIMO) switched nonlinear systems with unknown asymmetric actuator dead-zone. In this study, we employ different quantizers for different subsystem inputs. The main challenge of this study is to deal with the coupling between the quantizers and the dead-zone nonlinearities. To solve this problem, a novel approximation model for the coupling between quantizer and dead-zone is proposed. Then, the corresponding robust adaptive law is designed to eliminate this nonlinear term asymptotically. A direct neural control scheme is employed to reduce the number of adaptive laws significantly. The backstepping-based adaptive control scheme is also presented to guarantee the system performance. Finally, two simulation examples are presented to show the effectiveness of our control scheme.

Adaptive Backstepping Current Control of Active Power Filter Using Neural Compensator

A backstepping-based adaptive controller with neural compensator is designed for harmonic suppression in a three-phase active power filter (APF). The fundamental rule of backstepping method is to take some state variables as “virtual controls” and then design intermediate controller. An adaptive neural controller using radial basis function (RBF) is derived to estimate the APF system nonlinearity and strengthen the current’s tracking property and power grid quality. Simulations studies indicate the proposed backstepping-based adaptive neural controller has good current tracking behavior and increased power quality.

Asymptotic Fuzzy Tracking Control for a Class of Stochastic Strict-Feedback Systems

This paper presents an asymptotic tracking control design method for stochastic strict-feedback systems via fuzzy logic systems. In the existing results, stochastic controls are usually limited to be bounded in probability. However, how to realize the asymptotic tracking control for stochastic strict-feedback systems remains a control dilemma. This paper achieves the asymptotic tracking control by proposing a novel gain suppressing inequality approach. Specifically, the three-part construction is performed to achieve such control objective for stochastic systems. First, the novel gain suppressing inequality technique is developed to lay the foundation for carrying out the Lyapunov stability analysis. Second, the developed inequality technique is integrated with each step of the backstepping-based adaptive control design procedure. Third, analyses are provided to realize the asymptotic tracking control of stochastic strict-feedback systems.

Adaptive stabilization of [formula omitted] coupled linear hyperbolic systems with uncertain boundary parameters using boundary sensing

We design an adaptive control law stabilizing a class of systems of the form of n+1 linear partial differential equations (PDEs) of the hyperbolic type from boundary sensing only. We do this by combining a recently derived observer based on swapping design, with a backstepping-based adaptive control law with time-varying gains. Boundedness of all signals in the closed loop system and asymptotic convergence to zero pointwise in space for the system states are proved. The theory is demonstrated in a simulation.

Backstepping-based adaptive control for underactuated systems

A backstepping-based adaptive control scheme is presented for uncertain underactuated systems. On the basis of backstepping approach, virtual control components are first constructed and final control term is then derived by defining an integrated error surface. Proposed control scheme utilises wavelet neural network for the approximation of system uncertainties and thus relaxes the constraint of fully known system dynamics. Update laws for wavelet network parameters are derived in Lyapunov sense. Proposed control scheme guarantees the uniform ultimate boundedness of the closed loop adaptive system. Simulation included illustrates the effectiveness of the proposed approach.

Adaptive backstepping control of wheeled inverted pendulums models

In this paper, the state feedback control of wheeled inverted pendulum (WIP) used for mobile transportation has been investigated. The dynamic unstable balance and nonholonomic constraints inherent degrade the performance when the WIP operates in path-following mode. Through a suitable coordinates transformation, the WIP model is formulated into a parametric strict feedback form. Then, backstepping-based adaptive control is designed to achieve output tracking for the WIP. Simulation results are provided to show the effectiveness of the control proposed.

Control of uncertain non-linear systems via adaptive backstepping

Models of physical non-linear systems are prone to different kinds of uncertainties. This paper presents a backstepping-based adaptive control designed for a class of one degree-of-freedom uncertain non-linear systems. The true system does not need to be known for the control design. A functional description is assumed with uncertain coefficients and an uncertain residual function. These uncertainties are bounded and lump the discrepancies between the adopted description and the real behaviour. The adaptive controller is able to handle these uncertainties and make the closed loop globally uniformly ultimately bounded when the system is subject to an unknown excitation from which a bound is known. One goal is that the transient and asymptotic performances depend explicitly on the design parameters. This feature of the control scheme establishes the main difference with other control methods used to control non-linear systems, in particular chaotic systems. The efficiency of the approach is tested by numerical simulations on Duffing oscillators and systems with non-linear and hysteretic stiffness under external loads.