Adaptive Fuzzy Dynamic Surface Control for Multi-Machine Power System Based on Composite Learning Method and Disturbance Observer

Adaptive fuzzy dynamic surface control of nonlinear systems with input saturation and time-varying output constraints

This paper aims at exploring an adaptive fuzzy dynamic surface control (DSC) with prespecified tracking performance for a class of nonlinear systems in strict‐feedback form. Incorporating DSC technique into fuzzy logic systems (FLSs), it is shown that the design procedure and the computational burden can be greatly reduced. Moreover, by introducing a performance function in controller design, the prespecified tracking performance, i.e. the convergence rate, the allowed maximum overshoot and the steady state error, can be achieved. Simulation results are presented to demonstrate the efficiency of the proposed scheme.Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society

In this paper, a dynamic surface control (DSC) based adaptive fuzzy backstepping method is proposed for AUV (autonomous underwater vehicle) systems. The DSC is utilized to solve the “explosion of complexity” of traditional backstepping, the fuzzy logic systems(FLSs) are used to approximate unknown nonlinear function of AUV systems and the adaptive backstepping is employed to design controllers, and Matlab is used to conduct the simulation. The proposed control method can achieve position tracking effectively. The simulation results show that the adaptive fuzzy controller can overcome the influences of parameter uncertainties and load disturbance as well as achieve a good control effect on AUV system. This study has lots of practical application value.

In this paper, an adaptive fuzzy dynamic surface control scheme is proposed for an uncertain n-link robot manipulator with input saturation. The fuzzy logic system is used to approximate the uncertain dynamics of the robotic manipulator. Also, a hyperbolic tangent function-based saturation model and mean value theorem are employed to design the proposed scheme based on the dynamic surface control approach. Stability analysis of the proposed approach guarantees that all signals of the closed-loop system are semi-globally uniformly ultimately bounded. Simulation results on an uncertain 2-joint robot manipulator verify the ability of the proposed approach to deal with both the saturation nonlinearity and uncertain dynamics.

ABSTRACTIn this paper, a state observer-based adaptive fuzzy dynamic surface control is developed for uncertain discrete-time non-linear pure-feedback multiple-input-multiple-output (MIMO) systems with network-induced time-delay. The uncertainties are approximated by a set of adaptive fuzzy logic systems, with the adjusted parameters updated by a simplified recursive least squares estimation algorithm, combined with a state observer. For a constant known network-induced time-delay, the proposed modified dynamic surface control utilising the predicted system states, expands the acceptable network-induced time-delay and stable operating range for a discrete-time non-linear pure-feedback MIMO system in the network. The simulation results indicate that the presented method is effective.

This paper is concerned with the problem of adaptive fuzzy dynamic surface control (DSC) for the permanent magnet synchronous motor (PMSM) system with chaotic behavior, disturbance and unknown control gain and parameters. Nussbaum gain is adopted to cope with the situation that the control gain is unknown. And the unknown items can be estimated by fuzzy logic system. The proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output eventually converges to a small neighborhood of the desired reference signal. Finally, the numerical simulations indicate that the proposed scheme can suppress the chaos of PMSM and show the effectiveness and robustness of the proposed method.

In this paper, an adaptive robust stabilization problem is dealt with for a class of uncertain strict‐feedback nonlinear systems in the presence of unknown structure uncertainties, external disturbances, and unknown time‐varying virtual control coefficients. It is not required to know the upper bounds of external disturbances, as well as the upper and lower bounds of unknown time‐varying virtual control coefficients. The controller is designed by adopting backstepping. In addition, to avoid suffering from the problem of ‘explosion of terms’, a dynamic surface control approach is employed by introducing the first‐order low‐pass filter. Furthermore, at every step of the backstepping design procedure, fuzzy logic systems are used to approximate the unknown structure uncertainties. In particular, the norms of weight matrices and the upper bounds of approximation errors of fuzzy logic systems are supposed to be unknown. It is also shown that the proposed controller can guarantee the uniform boundedness of uncertain strict‐feedback nonlinear systems. Finally, the simulation for a single‐link manipulator actuated by a brush DC motor is carried out to illustrate the validity of the proposed controller.

In this paper, a novel nonlinear hybrid observers (HOB) based dynamic surface control (DSC) system, which integrates nominal and compensation controller, is designed for autonomous quadrotor robot perturbed by the uncertainties, e.g., external disturbances and measurement-delay. In this hybrid control system, the nominal controller based on dynamic surface control superior to the backstepping technique is the main controller, and the disturbance observer (DOB) is incorporated as the compensation controller to eliminate the effect of uncertainties caused by model mismatch and external disturbances. In addition, in order to relax the impact of measurement-delay induced by sensors of poor quality and heavy computation burden, the measurement-delay observer (MOB) is derived. Next, stability of the closed-loop control system is analytically proved based on Lyapunov theorem. Finally, comparative controllers are evaluated on simulation and experimental environments to demonstrate the effectiveness and merits of the proposed controller for quadrotor robot against the uncertainties.

Friction and deadzone nonlinearities in the nonlinear dynamic systems act often the main obstacles for higher motion control. It is difficult to achieve effective motion compensation for these types of nonlinearities by traditional linear control methodology without the aid of a proper estimation scheme on nonlinearity. Here, we present an adaptive fuzzy dynamic surface control scheme combined with state and nonlinearity observer and sliding mode control to estimate immeasurable state and compensate undesirable uncertainty on motion control for a strict output feedback nonlinear dynamic system. The adaptive laws of the fuzzy, friction and deadzone observer are derived from the recursive steps of the dynamic surface control. The boundedness and convergence of the closed-loop system are guaranteed by a Lyapunov stability analysis. The performance of the proposed control scheme is verified through simulation and experiment on the robot manipulator.

The paper presents a global tracking fuzzy control design for strict feedback systems with unknown control affine nonlinearities. In particular, the so-called explosion of complexity problem alleviated via using a modified dynamic surface control (DSC) technique. The modification mainly involves a firstorder filter with a space-modulated input, which in turn ensures the boundedness of the filter states. However, the repeated differentiations in the auxiliary robust controller remains as the price of preserving the global stability. Nevertheless, since the adaptive fuzzy controller dominates most of the time, the overall controller still significantly improves the existing global designs in terms of computation and implementation. Simulation results are provided to demonstrate the validity of the proposed scheme.

This paper is devoted to addressing the issue of adaptive fuzzy control for single-input single-output (SISO) fractional-order nonlinear systems with uncertainty. In order to avoid the “explosion of terms” caused by solving the fractional derivative of virtual signal, a new fractional-order dynamic surface control (DSC) method is proposed for fractional-order systems. Based on backstepping technique and Lyapunov direct method for fractional derivatives, the stability of closed-loop system is proved, which can ensure that the tracking error can converge to a small region. Finally, a simulation example is provided to reveal the effectiveness of the proposed control scheme.

This note investigates a novel cooperative design for path following of the mixed-order underactuated surface vehicle (USV) and unmanned aerial vehicle (UAV) in presence of the structure uncertainties and external disturbances. The designed cooperative scheme is comprised of the 3 dimension (3D) mapping guidance and adaptive fuzzy control algorithm. The 3D mapping guidance is developed to provide the reference signals of the yaw degree of freedom for the USV and UAV by utilizing the equivalent mapping technique. To control the USV-UAV converge to the desired path, the adaptive fuzzy control algorithm is designed for the position and attitude loops by fusing the dynamic surface control (DSC) and the Backstepping techniques. The position loop can generate the thrust inputs for the USV-UAV and provide the desired rolling and pitching angles for the UAV by using nonlinear decoupling method. In the proposed algorithm, the model uncertainties are approximated by the fuzzy logic system and only eight norm-based adaptive parameters are required to be updated online. Based on the Lyapunov theorem, the stability of the position loop and the attitude loop for the USV-UAV systems is achieved and all signals in the cooperative systems are the semi-global uniform ultimate bounded (SGUUB). The superiorities and the effectiveness of the proposed strategy are verified through the numerical experiment on the MATLAB platform under the external disturbances.

In this paper, a hybrid fuzzy adaptive output feedback control design approach is proposed for a class of multiinput and multioutput strict-feedback nonlinear systems with unknown time-varying delays, unmeasured states, and input saturation. First, fuzzy logic systems are employed to approximate unknown nonlinear functions in the system. Next, a smooth function is used to approximate the input saturation and an adaptive fuzzy state observer is constructed to solve the problem of unmeasured states. Based on the designed adaptive fuzzy state observer, a serial-parallel estimation model is established. By applying adaptive fuzzy dynamic surface control technique and utilizing the prediction error between the system states observer model and the serial-parallel estimation model, a new fuzzy controller with the composite parameters adaptive laws is developed based on Lyapunov-Krasovskii functional. It is proved that all variables of the closed-loop system are bounded and the system outputs can follow the given bounded reference signals as close as possible. A simulation example is provided to further show the effectiveness of this novel control scheme.

Attitude tracking control for variable structure near space vehicles based on switched nonlinear systems

This chapter proposes a new modeling and control scheme for uncertain strict-feedback nonlinear systems based on adaptive fuzzy wavelet network (FWN) and dynamic surface control (DSC) approach. It designs adaptive FWN as a nonlinear-in-parameter approximator to approximate the uncertain dynamics of the system. Then, the proposed control scheme is developed by incorporating the DSC method to the adaptive FWN-based model. Stability analysis of the proposed scheme is provided and adaptive laws are designed to learn all linear and nonlinear parameters of the network. It is proven that all the signals of the closed-loop system are uniformly ultimately bounded and the tracking error can be made arbitrary small. The proposed scheme does not require any prior knowledge about dynamics of the system and offline learning. Furthermore, it eliminates the “explosion of complexity” problems and develops accurate model of the system and simple controller. Simulation results on the numerical example and permanent magnet synchronous motor are provided to show the effectiveness of the proposed scheme.