Wenchao Meng

Adaptive neural control of high-order uncertain nonaffine systems: A transformation to affine systems approach ☆

Abstract This brief investigates the adaptive neural network (NN) control of a class of high-order nonaffine nonlinear systems with completely unknown dynamics. Since the control terms appear within the unknown nonlinearity, traditional control schemes and stability analysis are usually rendered extremely complicated. Our main contribution includes a novel system transformation that converts the nonaffine system into an affine system through a combination of a low-pass filter and state transformation. As a result, the state-feedback control of the nonaffine system can be viewed as the output-feedback control of an affine system in normal form. The transformed system becomes linear with respect to the new input while the traditional backstepping approach is not needed thus allowing the synthesis to be extremely simplified. It is theoretically proven that all the signals in the closed-loop system are uniformly ultimately bounded (UUB). Simulation results are provided to demonstrate the performance of the developed controller.

Adaptive Power Capture Control of Variable-Speed Wind Energy Conversion Systems With Guaranteed Transient and Steady-State Performance

This paper deals with the power capture control of variable-speed wind energy conversion systems. The control objective is to optimize the capture of wind energy by tracking the desired power output. Arbitrary steady-state performance is achieved in the sense that the tracking error is guaranteed to converge to any predefined small set. In addition, to maximize the wind energy capture, transient performance is enhanced such that the convergence rate can be larger than an arbitrary value, which further limits the maximum overshoot. First, an adaptive controller is designed for the case where known aerodynamic torque is assumed. Then, by utilizing an online approximator to estimate the uncertain aerodynamics, the need for the exact knowledge of the aerodynamic torque is waived to imitate the practical experience. With the aid of a novel output error transformation technique, both of the proposed controllers are capable of shaping the system performance arbitrarily on transient and steady-state stages. Meanwhile, it is also proved that all the signals in the closed-loop system are bounded via Lyapunov synthesis. Finally, the feasibility of the proposed controllers is demonstrated on an 1.5-MW three-blade wind turbine using the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code developed by the National Renewable Energy Laboratory.

Adaptive Neural Control of a Class of Output-Constrained Nonaffine Systems

In this paper, we present a novel tracking controller for a class of uncertain nonaffine systems with time-varying asymmetric output constraints. Firstly, the original nonaffine constrained (in the sense of the output signal) control system is transformed into a output-feedback control problem of an unconstrained affine system in normal form. As a result, stabilization of the transformed system is sufficient to ensure constraint satisfaction. It is subsequently shown that the output tracking is achieved without violation of the predefined asymmetric time-varying output constraints. Therefore, we are capable of quantifying the system performance bounds as functions of time on both transient and steady-state stages. Furthermore, the transformed system is linear with respect to a new input signal and the traditional backstepping scheme is avoided, which makes the synthesis extremely simplified. All the signals in the closed-loop system are proved to be semi-globally, uniformly, and ultimately bounded via Lyapunov synthesis. Finally, the simulation results are presented to illustrate the performance of the proposed controller.

Guaranteed Performance Control of DFIG Variable-Speed Wind Turbines

This brief presents a novel power control strategy for variable-speed wind turbines equipped with doubly fed induction generators (DFIGs). The control objective is to optimize the extracted power from wind while regulating the stator reactive power to meet grid requirements. First, in order to optimize the extracted power, an adaptive control technique is designed to drive the electromagnetic torque to follow its reference generated by the maximum power point tracking algorithm. Subsequently, aiming at satisfying reactive power requirements on the grid side, an adaptive reactive power controller is proposed to manipulate the stator reactive power to follow a given desired reactive power determined by the grid. Compared with most existing studies, we are capable of quantifying and further guaranteeing the system performance on both transient and steady-state stages. All signals in the closed-loop system are proved to be bounded via standard Lyapunov synthesis. Finally, the effectiveness of the proposed scheme is validated on a 1.5-MW DFIG-based wind turbine using the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) simulator developed by the National Renewable Energy Laboratory.

Adaptive Neural Control of Nonlinear MIMO Systems With Time-Varying Output Constraints

In this paper, adaptive neural control is investigated for a class of unknown multiple-input multiple-output nonlinear systems with time-varying asymmetric output constraints. To ensure constraint satisfaction, we employ a system transformation technique to transform the original constrained (in the sense of the output restrictions) system into an equivalent unconstrained one, whose stability is sufficient to solve the output constraint problem. It is shown that output tracking is achieved without violation of the output constraint. More specifically, we can shape the system performance arbitrarily on transient and steady-state stages with the output evolving in predefined time-varying boundaries all the time. A single neural network, whose weights are tuned online, is used in our design to approximate the unknown functions in the system dynamics, while the singularity problem of the control coefficient matrix is avoided without assumption on the prior knowledge of control inputs bound. All the signals in the closed-loop system are proved to be semiglobally uniformly ultimately bounded via Lyapunov synthesis. Finally, the merits of the proposed controller are verified in the simulation environment.

NN-based asymptotic tracking control for a class of strict-feedback uncertain nonlinear systems with output constraints

An asymptotic tracking control law is proposed for a class of strict-feedback nonlinear systems with unknown nonlinearities. A Barrier Lyapunov function in combination with backstepping is proposed to guarantee that the output trajectory is contained in a predefined set. A single neural network (NN), whose weights are tuned online, is utilized in our design to approximate the unknown functions in the system dynamics, while the singularity problem of the control gain function is avoided. Meanwhile, in order to compensate for the NN residual reconstruction error and system uncertainties, a robust term is introduced and asymptotic tracking stability is achieved. All the signals in the closed-loop system are proved to be bounded via Lyapunov synthesis and the output converges to the desired trajectory asymptotically without transgressing a given bound. Finally, the merits of the proposed controller are verified in the simulation environment.

Adaptive control of variable-speed wind energy conversion systems with inaccurate wind speed measurement

This paper deals with the power acquisition control of variable-speed wind energy conversion systems under inaccurate wind speed measurements. The control goal is to optimize the power capture from wind by tracking the maximum power curve. Firstly, the controller is designed for the case with known aerodynamic torque, which is a common assumption in many literatures. In this controller, the need for the exact knowledge of the system model is waived by using adaptive technologies. The chattering phenomenon in the generator torque, which can result in high mechanical stress, is avoided by adopting a modified robust term. Then, by utilizing an online approximator to learn an auxiliary term induced by the uncertain aerodynamics, the need for the exact knowledge of the aerodynamic torque is waived. Both of the proposed controllers are capable of providing good performance under inaccurate wind speed measurements. The control objective is obtained in the sense that the tracking error is guaranteed to converge to an arbitrarily small set. It is theoretically proved that all the signals in the closed-loop system are bounded via Lyapunov synthesis. Finally, the performance of our proposed controller is shown by simulating on a 1.5 MW three-blade wind turbine using the FAST (Fatigue, Aerodynamics, Structures, and Turbulence) code developed by the National Renewable Energy Laboratory.

Adaptive power acquisition control of variable-speed wind energy conversion systems under inaccurate wind speed measurement

In this paper, a novel nonlinear adaptive torque controller is proposed for variable-speed wind energy conversion systems to track the maximum power curve. Firstly, the controller is designed for the ideal case where known system parameters are assumed. Then, in the presence of uncertain internal system dynamics, the need for the exact knowledge of the system model is waived by using adaptive technologies. Furthermore, the chattering phenomenon in the generator torque which can result in high mechanical stress is avoided by adopting a modified robust term. Compared with existing methods, no accurate measurement of wind speed is required for the controller design. The control goal is achieved in the sense that the tracking error is guaranteed to converge to an arbitrarily small set. It is theoretically proved that all the signals in the closed-loop system are bounded via Lyapunov synthesis. Finally, the effectiveness and the merit of our proposed controller is shown by simulation on a 1.5MW three-blade wind turbine.

Distributed Control of Nonlinear Multiagent Systems With Asymptotic Consensus

An adaptive consensus algorithm is proposed for a class of nonlinear multiagent systems with completely unknown agent dynamics. Due to uncertainties in the agent’s dynamics, previous consensus approaches usually yield uniformly ultimately bounded consensus error. Our main contribution includes a novel robust consensus algorithm which can guarantee that the consensus error converges to zero asymptotically. In order to address the unknown dynamics, a two-layer neural network (NN) is utilized to learn the unknown dynamics in an online manner, and a robust continuous term is introduced to alleviate effects of the NN residual reconstruction error and external disturbances. The continuousness of the control signal is guaranteed to remove the actuator bandwidth requirement and avoid the caused chattering phenomenon. The proposed consensus algorithm is distributed in the sense that each agent only exchanges information with its neighbor agents. The asymptotic consensus result is achieved via Lyapunov synthesis. Furthermore, the proposed algorithm can also be extended to the case where the agents are required to form a prescribed formation. Finally, simulation studies on a nonlinear multiagent system are provided to demonstrate the performance of the scheme.