Observer-based tracking control for MIMO pure-feedback nonlinear systems with time-delay and input quantisation

This paper presents an adaptive fuzzy controller for the robust backstepping control of a class of uncertain nonlinear systems in pure-feedback form. The proposed approach incorporates the Nussbaum gain function (NGF) and the dynamic surface control (DSC) into the existing adaptive fuzzy control scheme. The major features of the proposed method are that: 1) the two problems, control directions and control singularity, are well solved by use of the NGF; 2) the problem of ldquoexplosion of complexityrdquo inherent to the existing backstepping methods is eliminated by introducing the DSC technique; 3) the proposed control scheme has the adaptive mechanism with fewer adaptive laws, which results in less computation time burden. The performance of the proposed approach is demonstrated through a simulation example.

Combining dynamic surface control with backstepping, a robust adaptive neural network control is proposed for a class of nonlinear systems in pure-feedback form with unmodeled dynamics and unknown dead-zones. The restriction of the control gain is relaxed by utilizing integral-type Lyapunov function. Using the radial basis function (RBF) neural networks (NNs) to approximate the unknown continuous functions, and with the help of Young's inequality, only one learning parameter needs to be tuned online in the whole controller design. The burdensome computation is alleviated. By theoretical analysis, the closed-loop control system is shown to be semi-globally uniformly ultimately bounded. Simulation results verify the effectiveness of the proposed approach.

This paperinvestigates the distributed consensus control for nonlinear multi-agent systems with input quantization in a directed communication topology. A nonsingular adaptive finite-time control (NAFTC) scheme is proposed by combining the filtered backstepping method with neural network control. Both chattering and singularity problems of the control signals are avoided thanks to its [Formula: see text] continuity, so that the contradiction between discontinuous- and [Formula: see text] continuous finite-time control with input quantization does not exist in our scheme. At the same time, a novel inequality for the input-output relationship of hysteresis quantizer is derived, which can simply handle the input quantization without requiring additional stability analysis. What is more, some common assumptions, such as Lipschitz assumption and Holder-continuity assumption, are not required in our scheme. Finally, stability analysis and numerical simulation are provided.

This paper is concerned with the problem of output feedback adaptive tracking control for a class of uncertain nonlinear time-delay systems (NTDS) with both uncertain output function and unknown control directions. By introducing a linear state transformation, the original system is transformed into a new system for which output feedback control design becomes feasible. Without the precise information on output function, an input-driven high-gain state observer is presented to estimate the unavailable states of the new system. Moreover, some radial basis function neural networks (RBFNNs) are employed to approximate the lumped unknown nonlinear functions during the procedure of control design. Then, by combining the Lyapunov–Krasovskii functional method, the Nussbaum gain function approach and the backstepping technique, a novel adaptive output feedback control scheme including only one parameter to be updated is developed for such systems. As a result, all signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) while the tracking error can remain in a small neighbourhood of zero. Finally, two simulation examples are given to validate the effectiveness and applicability of the proposed design method.

This paper aims at addressing the problem of finite time exact tracking control for a class of unknown nonlinear systems in pure feedback form while guaranteeing the closed-loop stability. The uncertain nonlinear system considered in this paper is not only in completely non-affine form but also explicitly dependent on the time and therefore, can cover a general class of nonlinear systems. By incorporating high order sliding mode controller into backstepping design procedure, a new robust control scheme with novel integrators is proposed, which is able to steer the tracking error to zero. Moreover, the closed loop stability can be proved with the help of the constructed integrators in every design step. Two simulation examples are presented to illustrate the correctness and effectiveness of the proposed control scheme.

This article addresses the tracking control problem of uncertain fractional-order nonlinear systems in the presence of input quantization and external disturbance. An adaptive backstepping scheme is proposed by combining with radial basis function (RBF) neural networks (NNs), fractional-order disturbance observer (FODO), and backstepping method. The RBF NNs are used to approximate the unknown nonlinearities of fractional-order systems. The FODO is designed to compensate for disturbance and uncertain parameters. The hysteresis quantizer is used to avoid chattering that possibly appears in actual application. The stability of the proposed controller is proved by fractional-order Lyapunov method. In addition, all the signals in the closed-loop system are bounded. The effectiveness of the proposed method is confirmed by the simulation results.

The paper presents a new output feedback adaptive control scheme for multi-input and multi-output (MIMO) non-linear systems whose non-linear terms depend only on the output variable based on the backstepping approach with vector form. The assumption on the prior knowledge of the high frequency gain matrix in existing results is reduced and the new required condition for the high frequency gain matrix can be easily checked for certain plants so that the proposed method is widely applicable. This control scheme guarantees the global stability of the closed-loop systems and makes the tracking error be arbitrary small. A simulation example is included to demonstrate the proposed algorithm.

A crucial role is played by steering-angle prediction in the control of autonomous vehicles (AVs). It mainly includes the prediction and control of the steering angle. However, the prediction accuracy and calculation efficiency of traditional YOLOv5 are limited. For the control of the steering angle, angular velocity is difficult to measure, and the angle control effect is affected by external disturbances and unknown friction. This paper proposes a lightweight steering angle prediction network model called YOLOv5Ms, based on YOLOv5, aiming to achieve accurate prediction while enhancing computational efficiency. Additionally, an adaptive output feedback control scheme with output constraints based on neural networks is proposed to regulate the predicted steering angle using the YOLOv5Ms algorithm effectively. Firstly, given that most lane-line data sets consist of simulated images and lack diversity, a novel lane data set derived from real roads is manually created to train the proposed network model. To improve real-time accuracy in steering-angle prediction and enhance effectiveness in steering control, we update the bounding box regression loss function with the generalized intersection over union (GIoU) to Shape-IoU_Loss as a better-converging regression loss function for bounding-box improvement. The YOLOv5Ms model achieves a 30.34% reduction in weight storage space while simultaneously improving accuracy by 7.38% compared to the YOLOv5s model. Furthermore, an adaptive output feedback control scheme with output constraints based on neural networks is introduced to regulate the predicted steering angle via YOLOv5Ms effectively. Moreover, utilizing the backstepping control method and introducing the Lyapunov barrier function enables us to design an adaptive neural network output feedback controller with output constraints. Finally, a strict stability analysis based on Lyapunov stability theory ensures the boundedness of all signals within the closed-loop system. Numerical simulations and experiments have shown that the proposed method provides a 39.16% better root mean squared error (RMSE) score than traditional backstepping control, and it achieves good estimation performance for angles, angular velocity, and unknown disturbances.

Robust non-fragile boundary control for non-linear parabolic PDE systems with semi-Markov switching and input quantization

This chapter proposes a servo control of multiple-input multiple-output (MIMO) nonlinear systems approximated by piecewise bilinear (PB) models. The approximated model is found to be fully parametric. The input-output (I/O) feedback linearization is applied to stabilize PB control systems. The controller can be represented as a Look-Up-Table (LUT). We apply the servo control based on PB models to the Caltech ducted fan model. Although the controller is simpler than the conventional I/O feedback linearizing controller, the control performance based on PB model is the same as the conventional one. The example is shown to confirm the feasibility of our proposals by computer simulations.

Adaptive disturbance rejection neural output feedback control of hydraulic manipulator systems

A novel alleviating fuzzy control algorithm for a class of nonlinear stochastic systems in pure-feedback form

In this paper, a fuzzy adaptive output feedback control approach is developed for a class of SISO strict-feedback nonlinear systems with unmeasured states, unmodeled dynamics, and dynamical disturbances. In the backstepping recursive design, fuzzy logic systems are used to approximate the unknown nonlinear functions, a fuzzy adaptive high-gain observer is designed to estimate the unmeasured states; a dynamic signal is incorporated into the control scheme to dominate the dynamic uncertainties. Using the states estimates and combining the backstepping design technique, a fuzzy adaptive output feedback control is constructed recursively. It is proved that the proposed fuzzy adaptive output feedback control scheme can guarantee the all signals in the closed-loop system are semiglobally uniformly ultimately bounded (SUUB), and the observer and tracking error converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated via an example.

International audience

A direct adaptive control framework for nonlinear uncertain systems with input quantizers is developed. The proposed framework is Lyapunov-based and guarantees ultimate boundedness (practical stability) of the closed-loop system. Specifically, the input quantizers are logarithmic and characterized by sector-bound conditions with the conic sector adjusted at each time instant by the adaptive controller in conjunction with the system response. Furthermore, for practical reasons we assume that the input logarithmic quantizers have deadzone around zero control input. Finally, a numerical example is provided to demonstrate the efficacy of the proposed approach