Adaptive Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor via Fuzzy Neural Networks

In this paper, based on fuzzy neural networks, we develop an adaptive sliding mode controller for chaos suppression and tracking control in a chaotic permanent magnet synchronous motor (PMSM) drive system. The proposed controller consists of two parts. The first is an adaptive sliding mode controller which employs a fuzzy neural network to estimate the unknown nonlinear models for constructing the sliding mode controller. The second is a compensational controller which adaptively compensates estimation errors. For stability analysis, the Lyapunov synthesis approach is used to ensure the stability of controlled systems. Finally, simulation results are provided to verify the validity and superiority of the proposed method

The design of quasi-sliding mode control for a permanent magnet synchronous motor with unmatched uncertainties

AbstractIn this study, the concept of a quasi-sliding mode control (QSMC) is introduced for the robust control of a permanent magnet synchronous motor (PMSM) system subjected to unmatched uncertainties, and even with input nonlinearity. On the basis of the new concept of QSMC, continuous control is obtained, to avoid the chattering phenomenon. As expected, the system state can be stabilized and driven into a predictable neighborhood of zero. Also, this approach only uses a single controller to achieve chaos control, which reduces the cost and complexity of implementation. The results of numerical simulations demonstrate the validity of the proposed QSMC design method

Distributed Sliding-Mode Formation Controller Design for Multirobot Dynamic Systems

This paper presents a distributed formation control for multirobot dynamic systems with external disturbances and system uncertainties. First from the Lagrangian analysis, the dynamic model of a wheeled mobile robot can be derived. Then, the robust distributed formation controller is proposed based on sliding-mode control, consensus algorithm, and graph theory. In this study, the robust stability of the closed-loop system is guaranteed by the Lyapunov stability theorem. From the simulation results, the proposed approach provides better formation responses compared to consensus algorithm

An Improved Adaptive Tracking Controller of Permanent Magnet Synchronous Motor

This paper proposes a new adaptive fuzzy neural control to suppress chaos and also to achieve the speed tracking control in a permanent magnet synchronous motor (PMSM) drive system with unknown parameters and uncertainties. The control scheme consists of fuzzy neural and compensatory controllers. The fuzzy neural controller with online parameter tuning is used to estimate the unknown nonlinear models and construct linearization feedback control law, while the compensatory controller is employed to attenuate the estimation error effects of the fuzzy neural network and ensure the robustness of the controlled system. Moreover, due to improvement in controller design, the singularity problem is surely avoided. Finally, numerical simulations are carried out to demonstrate that the proposed control scheme can successfully remove chaotic oscillations and allow the speed to follow the desired trajectory in a chaotic PMSM despite the existence of unknown models and uncertainties

Quasi-Sliding Mode Control of Chaos in Permanent Magnet Synchronous Motor

A quasi-sliding mode control (QSMC) to suppress chaos for a permanent magnet synchronous motor (PMSM) with parameters fall into a certain area is proposed in this paper. Especially, based on the new concept of QSMC, continuous control input is obtained to avoid chattering phenomenon. As expected, the system states can be driven to zero or into a predictable and adjustable bound even when uncertainties are present. Numerical simulations demonstrate the validity of the proposed QSMC design method

Adaptive Memoryless Sliding Mode Control of Uncertain Rössler Systems with Unknown Time Delays

In this paper, by adopting sliding mode control, an adaptive memoryless control scheme has been developed for uncertain Rössler chaotic systems with unknown time delays. Firstly, the proposed adaptive control can force the trajectories of controlled Rössler time-delayed chaotic systems into the specified sliding manifold. Then, the Riemann sum is introduced to analyze the stability of the equivalent dynamics in the sliding manifold. The control performance can be predicted even if the controlled systems have unmatched uncertainties and unknown time delays, which have not been well addressed in the literature. Numerical simulations are included to demonstrate the feasibility of the proposed scheme

Adaptive Memoryless Sliding Mode Control of Uncertain Rössler Systems with Unknown Time Delays

In this paper, by adopting sliding mode control, an adaptive memoryless control scheme has been developed for uncertain Rössler chaotic systems with unknown time delays. Firstly, the proposed adaptive control can force the trajectories of controlled Rössler time-delayed chaotic systems into the specified sliding manifold. Then, the Riemann sum is introduced to analyze the stability of the equivalent dynamics in the sliding manifold. The control performance can be predicted even if the controlled systems have unmatched uncertainties and unknown time delays, which have not been well addressed in the literature. Numerical simulations are included to demonstrate the feasibility of the proposed scheme

Chaos Suppression in Uncertain Generalized Lorenz–Stenflo Systems via a Single Rippling Controller with Input Nonlinearity

In this paper, a robust control design of chaos suppression is considered for generalized four-dimensional (4D) Lorenz–Stenflo systems subjected to matched/mismatched uncertainties and input nonlinearity. It is implemented by using rippling sliding mode control (SMC). A proportional-integral (PI) type scalar switching surface is designed such that the controlled dynamics in the sliding manifold becomes easy to analyze. Furthermore, only by using single rippling SMC even with input nonlinearity can we ensure the existence of the sliding mode for the controlled dynamics and suppress the chaotic behavior in a manner of rippling. Under the proposed control scheme, the chaos behavior in uncertain generalized 4D Lorenz–Stenflo systems subjected to mismatched uncertainties can be robustly suppressed to predictable bounds, which is not addressed in the literature. The numerical simulation results including matched/mismatched uncertainties and nonlinear inputs are presented to verify the robustness and validity of the rippling sliding mode controller