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

Nonlinear Time-Frequency Control of Permanent Magnet Electrical Machines

Permanent magnet (PM) electrical machines have been widely adopted in industrial applications due to their advantages such as easy to control, compact in size, low in power loss, and fast in response, to name only a few. Contemporary control methods specifically designed for the control of PM electrical machines only focus on controlling their time-domain behaviors while completely ignored their frequency-domain characteristics. Hence, when a PM electrical machine is highly nonlinear, none of them performs well. To make up for the drawback and hence improve the performance of PM electrical machines under high nonlinearity, the novel nonlinear time-frequency control concept is adopted to develop viable nonlinear control schemes for PM electrical machines. In this research, three nonlinear time-frequency control schemes are developed for the speed and position control of PM brushed DC motors, speed and position control of PM synchronous motors, and chaos suppression of PM synchronous motors, respectively. The most significant feature of the demonstrated control schemes are their ability in generating a proper control effort that controls the system response in both the time and frequency domains. Simulation and experiment results have verified the effectiveness and superiority of the presented control schemes. The nonlinear time-frequency control scheme is therefore believed to be suitable for PM electrical machine control and is expected to have a positive impact on the broader application of PM electrical machines

CHAOS SYNCHRONIZATION USING SUPER-TWISTING SLIDING MODE CONTROL APPLIED ON CHUA’S CIRCUIT

Chua’s circuit is the classic chaotic system and the most widely used in serval areas due to its potential for secure communication. However, developing an accurate chaos control strategy is one of the most challenging works for Chua’s circuit. This study proposes a new application of super twisting algorithm (STC) based on sliding mode control (SMC) to eliminate or synchronize the chaos behavior in the circuit. Therefore, the proposed control strategy is robust against uncertainty and effectively regulates the system with a good regulation tracking task. Using the Lyapunov stability, the property of asymptotical stability is verified. The whole of the system including the (control strategy, and Chua’s circuit) is implemented under a suitable test setup based on dSpace1104 to validate the effectiveness of our proposed control scheme. The experimental results show that the proposed control method can effectively eliminate or synchronize the chaos in the Chua's circuit

Adaptive Robust Backstepping Control of Permanent Magnet Synchronous Motor Chaotic System with Fully Unknown Parameters and External Disturbances

The chaotic behavior of permanent magnet synchronous motor is directly related to the parameters of chaotic system. The parameters of permanent magnet synchronous motor chaotic system are frequently unknown. Hence, chaotic control of permanent magnet synchronous motor with unknown parameters is of great significance. In order to make the subject more general and feasible, an adaptive robust backstepping control algorithm is proposed to address the issues of fully unknown parameters estimation and external disturbances inhibition on the basis of associating backstepping control with adaptive control. Firstly, the mathematical model of permanent magnet synchronous motor chaotic system with fully unknown parameters is constructed, and the external disturbances are introduced into the model. Secondly, an adaptive robust backstepping control technology is employed to design controller. In contrast with traditional backstepping control, the proposed controller is more concise in structure and avoids many restricted problems. The stability of the control approach is proved by Lyapunov stability theory. Finally, the effectiveness and correctness of the presented algorithm are verified through multiple simulation experiments, and the results show that the proposed scheme enables making permanent magnet synchronous motor operate away from chaotic state rapidly and ensures the tracking errors to converge to a small neighborhood within the origin rapidly under the full parameters uncertainties and external disturbances

Design of single neuron super-twisting sliding mode controller for permanent magnet synchronous servo motor

Aiming at the control system of permanent magnet synchronous servo motor which is easily affected by external disturbance and parameter uncertainty, a single neuron sliding mode combining single neuron adaptive algorithm and super-twisting sliding mode (STSM) control is proposed. The STSM control is used to overcome the chattering problem in the traditional sliding mode control, and the proportional control and the STSM control are combined to enhance the robustness of the control system. In order to improve the dynamic performance of the system and enhance the anti-disturbance ability of the system, the single neuron adaptive control adopted can adjust the relevant parameters of the designed sliding mode controller online. The simulation and experimental results show that the designed improved sliding mode controller can effectively suppress the chattering of the control system, realize the fast following and no overshoot of the control system, and enhance the robustness of the system

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

Nonlinear Time-Frequency Control of Permanent Magnet Electrical Machines

Permanent magnet (PM) electrical machines have been widely adopted in industrial applications due to their advantages such as easy to control, compact in size, low in power loss, and fast in response, to name only a few. Contemporary control methods specifically designed for the control of PM electrical machines only focus on controlling their time-domain behaviors while completely ignored their frequency-domain characteristics. Hence, when a PM electrical machine is highly nonlinear, none of them performs well. To make up for the drawback and hence improve the performance of PM electrical machines under high nonlinearity, the novel nonlinear time-frequency control concept is adopted to develop viable nonlinear control schemes for PM electrical machines. In this research, three nonlinear time-frequency control schemes are developed for the speed and position control of PM brushed DC motors, speed and position control of PM synchronous motors, and chaos suppression of PM synchronous motors, respectively. The most significant feature of the demonstrated control schemes are their ability in generating a proper control effort that controls the system response in both the time and frequency domains. Simulation and experiment results have verified the effectiveness and superiority of the presented control schemes. The nonlinear time-frequency control scheme is therefore believed to be suitable for PM electrical machine control and is expected to have a positive impact on the broader application of PM electrical machines

Short-memory discrete fractional difference equation wind turbine model and its inferential control of a chaotic permanent magnet synchronous transformer in time-scale analysis

The aerodynamics analysis has grown in relevance for wind energy projects; this mechanism is focused on elucidating aerodynamic characteristics to maximize accuracy and practicability via the modelling of chaos in a wind turbine system's permanent magnet synchronous generator using short-memory methodologies. Fractional derivatives have memory impacts and are widely used in numerous practical contexts. Even so, they also require a significant amount of storage capacity and have inefficient operations. We suggested a novel approach to investigating the fractional-order operator's Lyapunov candidate that would do away with the challenging task of determining the indication of the Lyapunov first derivative. Next, a short-memory fractional modelling strategy is presented, followed by short-memory fractional derivatives. Meanwhile, we demonstrate the dynamics of chaotic systems using the Lyapunov function. Predictor-corrector methods are used to provide analytical results. It is suggested to use system dynamics to reduce chaotic behaviour and stabilize operation; the benefit of such a framework is that it can only be used for one state of the hybrid power system. The key variables and characteristics, i.e., the modulation index, pitch angle, drag coefficients, power coefficient, air density, rotor angular speed and short-memory fractional differential equations are also evaluated via numerical simulations to enhance signal strength