Guanyu Lai

Adaptive Neural Control for a Class of Nonlinear Time-Varying Delay Systems With Unknown Hysteresis

This paper investigates the fusion of unknown direction hysteresis model with adaptive neural control techniques in face of time-delayed continuous time nonlinear systems without strict-feedback form. Compared with previous works on the hysteresis phenomenon, the direction of the modified Bouc-Wen hysteresis model investigated in the literature is unknown. To reduce the computation burden in adaptation mechanism, an optimized adaptation method is successfully applied to the control design. Based on the Lyapunov-Krasovskii method, two neural-network-based adaptive control algorithms are constructed to guarantee that all the system states and adaptive parameters remain bounded, and the tracking error converges to an adjustable neighborhood of the origin. In final, some numerical examples are provided to validate the effectiveness of the proposed control methods.

Fuzzy Adaptive Inverse Compensation Method to Tracking Control of Uncertain Nonlinear Systems With Generalized Actuator Dead Zone

This paper solves the problem of adaptive fuzzy inverse compensation control for an uncertain nonlinear system whose actuator is subjected to generalized dead-zone nonlinearity. By defining a continuous connection function and combining with the mean-value theorem, the generalized dead zone is first decomposed into a nominal asymmetric dead zone multiplying an uncertain continuous input function. Afterward, a smooth inversion and its parameterization are further proposed such that a new expression of adaptive asymmetric dead-zone compensation error is established in Theorems 1 and 2. With such an expression, the fuzzy systems can be successfully embedded into a compensation structure to indirectly handle uncertain input dynamics. In addition, a separation scheme is developed to construct two online estimators. Based on the above design procedure, an adaptive inverse compensator for generalized dead zone is built eventually. With the backstepping iteration design of compensator input, an adaptive fuzzy controller is developed to establish the closed-loop system stability. Finally, two simulations are conducted to illustrate the effectiveness and applicability of the proposed control scheme.

Fuzzy adaptive control of nonlinear uncertain plants with unknown dead zone output

This paper focuses on a tracking problem for a class of strict-feedback nonlinear systems with unknown dead-zone output and unmeasurable states. Based on the adaptive backstepping technique, a new adaptive fuzzy control scheme is proposed. It is shown that the proposed control scheme guarantees that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded, the tracking error converges to a small neighborhood of the origin. Moreover, the proposed control method requires only one adaptive law for an nth-order system and the structure of the controllers is very simple, so they are easy to be implemented. The main advantage of this paper is that the unknown dead-zone nonlinearity in the output mechanism of strict-feedback systems is considered. By introducing a Nussbaum function, the problem of unknown virtual control coefficient is resolved, which is caused by the nonlinearity in the output mechanism. Finally, simulation results are provided to show the effectiveness of the proposed approach.

Adaptive Neural Output Feedback Control of Output-Constrained Nonlinear Systems With Unknown Output Nonlinearity

This paper addresses the problem of adaptive neural output-feedback control for a class of special nonlinear systems with the hysteretic output mechanism and the unmeasured states. A modified Bouc-Wen model is first employed to capture the output hysteresis phenomenon in the design procedure. For its fusion with the neural networks and the Nussbaum-type function, two key lemmas are established using some extended properties of this model. To avoid the bad system performance caused by the output nonlinearity, a barrier Lyapunov function technique is introduced to guarantee the prescribed constraint of the tracking error. In addition, a robust filtering method is designed to cancel the restriction that all the system states require to be measured. Based on the Lyapunov synthesis, a new neural adaptive controller is constructed to guarantee the prescribed convergence of the tracking error and the semiglobal uniform ultimate boundedness of all the signals in the closed-loop system. Simulations are implemented to evaluate the performance of the proposed neural control algorithm in this paper.

Adaptive Fuzzy Tracking Control of Nonlinear Systems With Asymmetric Actuator Backlash Based on a New Smooth Inverse

This paper is concentrated on the problem of adaptive fuzzy tracking control for an uncertain nonlinear system whose actuator is encountered by the asymmetric backlash behavior. First, we propose a new smooth inverse model which can approximate the asymmetric actuator backlash arbitrarily. By applying it, two adaptive fuzzy control scenarios, namely, the compensation-based control scheme and nonlinear decomposition-based control scheme, are then developed successively. It is worth noticing that the first fuzzy controller exhibits a better tracking control performance, although it recourses to a known slope ratio of backlash nonlinearity. The second one further removes the restriction, and also gets a desirable control performance. By the strict Lyapunov argument, both adaptive fuzzy controllers guarantee that the output tracking error is convergent to an adjustable region of zero asymptotically, while all the signals remain semiglobally uniformly ultimately bounded. Lastly, two comparative simulations are conducted to verify the effectiveness of the proposed fuzzy controllers.

Adaptive Fuzzy Tracking Control of Nonlinear Time-Delay Systems With Dead-Zone Output Mechanism Based on a Novel Smooth Model

This paper presents a novel fuzzy adaptive controller for controlling a class of dead-zone output nonlinear systems with time delays. A new approximate model is first designed to describe a special dead-zone phenomenon encountered by the output mechanism of nonlinear systems, and the proposed smooth model can be conveniently fused with available adaptive fuzzy control techniques. In addition, the coupling effect that the dead-zone output and the time-delayed states coexist in a common coupling function makes the tracking control design more complicated. To further address this difficulty, a compensation method using mean-value theorem with Lyapunov-Krasovskii function is presented in this paper. By using the proposed output dead-zone model, and based on Lyapunov synthesis, a new optimized algorithm is developed to guarantee the prescribed convergence of tracking error and the boundedness of all the signals in the closed-loop systems. Simulations have been implemented to verify the performance of the proposed fuzzy adaptive controller.

Adaptive asymptotic tracking control of uncertain nonlinear system with input quantization

Abstract Asymptotic tracking control of uncertain nonlinear system with input quantization is an important, yet challenging issue in the field of adaptive control. So far, there is still no result available in addressing this issue even for the case of time-invariant reference signal. In this paper, we solve this problem by proposing a new tuning function control scheme which is designed on the basis of a novel decomposition of hysteresis quantizer. It is proved that the proposed scheme ensures the global boundedness of all closed-loop signals and the asymptotic convergence of tracking error to zero. Moreover, an explicit bound for the L 2 -norm of the tracking error is derived, which shows that the transient performance can also be improved with the proposed scheme.

Asymmetric Actuator Backlash Compensation in Quantized Adaptive Control of Uncertain Networked Nonlinear Systems

This paper mainly aims at the problem of adaptive quantized control for a class of uncertain nonlinear systems preceded by asymmetric actuator backlash. One challenging problem that blocks the construction of our control scheme is that the real control signal is wrapped in the coupling of quantization effect and nonsmooth backlash nonlinearity. To resolve this challenge, this paper presents a two-stage separation approach established on two new technical components, which are the approximate asymmetric backlash model and the nonlinear decomposition of quantizer, respectively. Then the real control is successfully separated from the coupling dynamics. Furthermore, by employing the neural networks and adaptation method in control design, a quantized controller is developed to guarantee the asymptotic convergence of tracking error to an adjustable region of zero and uniform ultimate boundedness of all closed-loop signals. Eventually, simulations are conducted to support our theoretical results.

Adaptive Position/Attitude Tracking Control of Aerial Robot With Unknown Inertial Matrix Based on a New Robust Neural Identifier

This paper presents a novel adaptive controller for controlling an autonomous helicopter with unknown inertial matrix to asymptotically track the desired trajectory. To identify the unknown inertial matrix included in the attitude dynamic model, this paper proposes a new structural identifier that differs from those previously proposed in that it additionally contains a neural networks (NNs) mechanism and a robust adaptive mechanism, respectively. Using the NNs to compensate the unknown aerodynamic forces online and the robust adaptive mechanism to cancel the combination of the overlarge NNs compensation error and the external disturbances, the new robust neural identifier exhibits a better identification performance in the complex flight environment. Moreover, an optimized algorithm is included in the NNs mechanism to alleviate the burdensome online computation. By the strict Lyapunov argument, the asymptotic convergence of the inertial matrix identification error, position tracking error, and attitude tracking error to arbitrarily small neighborhood of the origin is proved. The simulation and implementation results are provided to evaluate the performance of the proposed controller.

Adaptive fuzzy quantized control of time-delayed nonlinear systems with communication constraint ☆

Abstract This paper explores the problem of quantized fuzzy adaptive tracking control for a class of time-delayed uncertain nonlinear systems with communication constraint. The control signal is first quantized in a newly proposed asymmetric hysteresis-type quantizer before it is transmitted over the networks with limited bandwidth. Then one of major difficulties that block the controller design is that the real control signal is hidden in the coupling dynamics between nonaffine system nonlinearity and rate-dependent hysteretic quantizer. To remove this obstacle, a control separation method established mainly on a new nonlinear decomposition of quantizer is proposed. Unlike previous linear decomposition of quantizer, the nonlinear one does not recourse to some conservative assumptions either on control input or system nonlinearities. In combination with fuzzy backstepping technique and Lyapunov–Krasovskii functional, an adaptive fuzzy controller is developed to guarantee the boundedness of all closed-loop signals, and the asymptotic convergence of tracking error to an adjustable region of zero. Finally, two simulations associated with practical control systems are conducted to verify the effectiveness and applicability of the proposed control protocol.