Adaptive fuzzy dynamic surface control for the chaotic permanent magnet synchronous motor using Nussbaum gain.

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

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.

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.

A composite learning dynamic surface control is proposed for a class of multi-machine power systems with uncertainties and external disturbances by using fuzzy logic systems (FLSs) and disturbance observer (DOB). The main characteristics of the proposed strategy are as follows: (1) The approximation ability of FLSs for nonlinear model of multi-machine power systems is enhanced considerably by using the composite learning method and providing additional correction information for the FLSs. These findings differ considerably from previous designs that focus directly on the system's tracking performance. (2) The filtering errors caused by the utilizations of the first-order low-pass filters in dynamic surface control (DSC) are compensated effectively by designing the compensating signals in the control law design process. (3) The compound disturbances including the FLSs' approximation error and external disturbances are estimated and mitigated by constructing DOB. Finally, the proposed control algorithm is verified on the StarSim Hardware-in-loop experimental platform, and the experimental results validate the effectiveness of the proposed control strategy in suppressing disturbances and enhancing the robustness of the controller.

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.

Composite control based on FNTSMC and adaptive neural network for PMSM system

In this paper, a nonlinear state observer control strategy is developed for projective self-synchronization of the fractional-order chaotic attractors of a permanent magnet synchronous motor (PMSM) system. The mathematical model of PMSM system in a smooth fractional-order form is derived by using the fractional derivative theory. A state observer control design can achieve the full-state projective synchronization of the fractional-order PMSM (FO-PMSM) system without the limitation of partial-linearity. Global stability and asymptotic synchronization between the outputs of drive system and response system can be obtained. Simulation results are provided to demonstrate the effectiveness of the approach.

The fuzzy controller is a popular choice for permanent magnet synchronous motor (PMSM) control systems because of its advantages, such as straightforward design, and no reliance on the precise mathematical model of the motor. But the existing pure PI-like fuzzy control strategy still has some disadvantages, such as poor adaptive ability and large overshooting. This work redevelops the structure and rules of the adaptive fuzzy controller, and proposes and proves an improved adaptive PI-like fuzzy control algorithm for the PMSM system. Firstly, a parallel dual fuzzy controller structure is constructed to facilitate the adaptive adjustment of the “PI-like fuzzy controller”. Secondly, the error acceleration parameter rv(k), which contains the PMSM speed information, is set and normalized to accurately identify the dynamic response stages of the PMSM system. Lastly, an adaptive fuzzy rule table is designed based on the dynamic response waveform of the PMSM system, and the control characterization is analyzed. The simulation and experimental results of the PMSM system show that the improved adaptive PI-like fuzzy controller has a broad dynamic adjustment range, the PMSM can rapidly and smoothly reach the given speed during the startup stage with small overshooting, the speed drop is low when the load is abruptly added, the PMSM system can quickly return to the steady state with a strong adaptive ability, and its dynamic performance indicators surpass those of the PID controller and traditional PI-like fuzzy controller.

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.

A new instantaneous torque-control strategy is presented for high-performance control of a permanent magnet (PM) synchronous motor. In order to deal with the torque pulsating problem of a PM synchronous motor in a low-speed region, new torque estimation and control techniques are proposed. The linkage flux of a PM synchronous motor is estimated using a model reference adaptive system technique, and the developed torque is instantaneously controlled by the proposed torque controller combining a variable structure control (VSC) with a space-vector pulse-width modulation (PWM). The proposed control provides the advantage of reducing the torque pulsation caused by the nonsinusoidal flux distribution. This control strategy is applied to the high-torque PM synchronous motor drive system for direct-drive applications and implemented by using a software of the digital signal processor (DSP) TMS320C30. The simulations and experiments are carried out for this system, and the results demonstrate the effectiveness of the proposed control.

This work deals with the development of double closed loop control based on explicit model predictive control (MPC) for permanent magnet synchronous motor (PMSM) system with constraints. The aim of the proposed controller is to solve the constrained optimization problem for fast-sampling time PMSM system and improve the robustness of the system. The recreated mathematical model of PMSM is introduced at first. Then the proposed explicit MPC controller is designed based on the reformed model. The explicit controller is employed to control the mechanical speed and the q-axis current, and the proportional-integral (PI) control is employed to control d-axis current. The simulation results demonstrate that the proposed double closed loop control method is effective when the working condition of PMSM changes and the performance of the speed and current is satisfactory.

Anti-disturbance speed control of low-speed high-torque PMSM based on second-order non-singular terminal sliding mode load observer

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.