Adaptive Tracking Control System Research Articles

Disturbance observer based adaptive heading control for unmanned surface vehicle with event‐triggered and signal quantization

SummaryThis article delves into the adaptive heading tracking control of unmanned surface vehicle (USV) by incorporating an event‐triggered mechanism and signal quantization. The primary objective is to save communication resources while alleviating the burden of signal transmission. To address time‐varying external perturbations inherent in the control system, a disturbance observer is employed for precise estimation. Additionally, a linear model is introduced to delineate the procedure of quantization. By furnishing the controller with purpose‐designed quantized control input, the adaptive tracking control system can effectively track desired input without requiring any prior knowledge of the quantized parameters. The article substantiates its claims by demonstrating the system's stability in the absence of quantization considerations and the bounded nature of quantization errors through a series of presented lemmas. Further, the stability of the USV heading control system, integrated with an event‐triggered mechanism and signal quantization, is proofed in accordance with Lyapunov stability theory. Finally, the proposed strategy's efficacy and practical applicability are validated through experimental simulations.

An adaptive tracking control method for the all-wheel-driving and active-steering articulated vehicle with n-units

To achieve the running control of the all-wheel-driving and active-steering articulated vehicles (AWDASAVs) with n-units, an adaptive tracking control method is proposed in this paper, which includes a real-time target trajectory generation and an adaptive tracking control system. Firstly, the AWDASAV kinematics model is derived, and then the front-axle trace as the target trajectory is computed for all rear-axle steering by using data compressing and filtering, coordinate transformation, and local spline differences, which has small data storage and high computational efficiency and makes it easier to use in AWDASAV. Secondly, an adaptive tracking control system composed of an adaptive active steering controller and a differential distribution controller is designed to achieve accurate trajectory tracking and coordinated movement for AWDASAV. Finally, the AWDASAV simulation model with five-units was built in ADAMS by code development for cross-validation simulation, and the simulations with two cases at various speeds are carried out to verify the simulation model and control method. To further investigate the proposed method, the influence of three parameters on the tracking control performance and comparison with different control methods are carried out. The results exhibit excellent tracking control performance.

Synchronization and tracking control of a novel 3 dimensional chaotic system

In this article, a novel three dimensional chaotic systems is presented. An extensive analysis including Lyapunov exponents, dissipation, symmetry, rest points with their properties is introduced. An adaptive tracking control system for the proposed chaos system has been designed. Also, synchronization system for two identical systems has been designed. The simulation results showed the effectiveness of the designed tracking and synchronization control systems.

Self-Organizing Recurrent Fuzzy Wavelet Neural Network-Based Mixed $\rm{H}_{2}\rm{/ H}_{\rm{\infty }}$ Adaptive Tracking Control for Uncertain Two-Axis Motion Control System

In this paper, an intelligent adaptive tracking control system (IATCS) based on the mixed $H_{2}/ H_{\rm{\infty }}$ approach for achieving high precision performance of a two-axis motion control system is proposed. The two-axis motion control system is an X-Y table driven by two permanent-magnet linear synchronous motors (PMLSMs) servo drives. The proposed control scheme incorporates a mixed $H_{2}/ H_{\rm{\infty }}$ controller, a self-organizing recurrent fuzzy-wavelet-neural-network controller (SORFWNNC), and a robust controller. The SORFWNNC is used as the main tracking controller to adaptively estimate an unknown nonlinear dynamic function (UNDF) that includes the lumped parameter uncertainties, external disturbances, cross-coupled interference, and frictional force. Furthermore, a robust controller is designed to deal with the approximation error, optimal parameter vectors, and higher order terms in Taylor series. Besides, the mixed $H_{2}/ H_{\rm{\infty }}$ controller is designed such that the quadratic cost function is minimized and the worst case effect of the UNDF on the tracking error must be attenuated below a desired attenuation level. The online adaptive control laws are derived based on Lyapunov theorem and the mixed $H_{2}/ H_{\rm{\infty }}$ tracking performance so that the stability of the IATCS can be guaranteed. The experimental results confirm that the proposed IATCS grants robust performance and precise dynamic response to the reference contours regardless of external disturbances and parameter uncertainties.

Intelligent mixed H2/H∞ adaptive tracking control system design using self-organizing recurrent fuzzy-wavelet-neural-network for uncertain two-axis motion control system

In this paper, an intelligent adaptive tracking control system (IATCS) based on the mixed H2/H∞ approach under uncertain plant parameters and external disturbances for achieving high precision performance of a two-axis motion control system is proposed. The two-axis motion control system is an X–Y table driven by two permanent-magnet linear synchronous motors (PMLSMs) servo drives. The proposed control scheme incorporates a mixed H2/H∞ controller, a self-organizing recurrent fuzzy-wavelet-neural-network controller (SORFWNNC) and a robust controller. The combinations of these control methods would insure the stability, robustness, optimality, overcome the uncertainties, and performance properties of the two-axis motion control system. The SORFWNNC is used as the main tracking controller to adaptively estimate an unknown nonlinear dynamic function that includes the lumped parameter uncertainties, external disturbances, cross-coupled interference and frictional force. Moreover, the structure and the parameter learning phases of the SORFWNNC are performed concurrently and online. Furthermore, a robust controller is designed to deal with the uncertainties, including the approximation error, optimal parameter vectors and higher order terms in Taylor series. Besides, the mixed H2/H∞ controller is designed such that the quadratic cost function is minimized and the worst case effect of the unknown nonlinear dynamic function on the tracking error must be attenuated below a desired attenuation level. The mixed H2/H∞ control design has the advantage of both H2 optimal control performance and H∞ robust control performance. The sufficient conditions are developed for the adaptive mixed H2/H∞ tracking problem in terms of a pair of coupled algebraic equations instead of coupled nonlinear differential equations. The coupled algebraic equations can be solved analytically. The online adaptive control laws are derived based on Lyapunov theorem and the mixed H2/H∞ tracking performance so that the stability of the proposed IATCS can be guaranteed. Furthermore, the control algorithms are implemented in a DSP-based control computer. From the experimental results, the motions at X-axis and Y-axis are controlled separately, and the dynamic behaviors of the proposed IATCS can achieve favorable tracking performance and are robust to parameter uncertainties.

Robust Adaptive Attitude Tracking on ${\rm SO}(3)$ With an Application to a Quadrotor UAV

This brief describes robust adaptive tracking control systems for the attitude dynamics of a rigid body. Both the attitude dynamics and the proposed control system are globally expressed on the special orthogonal group, to avoid complexities and ambiguities associated with other attitude representations, such as Euler angles or quaternions. By designing an adaptive law for the inertia matrix of a rigid body, the proposed control system can asymptotically follow an attitude command without the knowledge of the inertia matrix, and it is extended to guarantee boundedness of tracking errors in the presence of unstructured disturbances. These are illustrated by the experimental results of the attitude dynamics of a quadrotor unmanned aerial vehicle.

Robust Adaptive Control of a Three-Axis Motion Simulator With State Observers

The development of a nonlinear robust adaptive tracking control system for a three-axis motion simulator is presented in this paper. The motion simulator is used to test and calibrate certain spacecraft instruments within a hardware-in-the-loop environment. Permanent magnet synchronous motor (PMSM) drives are used as simulator actuators. The control system is developed based on Lyapunov stability theory for which only rotor position and stator current signals are required. By using mechanical and electrical state observers, the measurement of acceleration and load torque which is usually required when motor dynamics are considered, is avoided. The control system can be made adaptable to constant unknown motor parameters and load inertia and robust to unknown but bounded fast varying disturbances. Simulation and experimental results are presented to verify the stability and efficacy of the proposed control system.