Investigations on Direct Torque and Flux Control of Speed Sensorless Induction Motor Drive

The Induction motors (IM) are used worldwide as the workhorse in most of the industrial applications due to their simplicity, high performance, robustness and capability of operating in hazardous as well as extreme environmental conditions. However, the speed control of IM is complex as compared to the DC motor due to the presence of coupling between torque and flux producing components. The speed of the IM can be controlled using scalar control and vector control techniques. The most commonly used technique for speed control of IM is scalar control method. In this method, only the magnitude and frequency of the stator voltage or current is regulated. This method is easy to implement, but suffers from the poor dynamic response. Therefore, the vector control or field oriented control (FOC) is used for IM drives to achieve improved dynamic performance. In this method, the IM is operated like a fully compensated and separately excited DC motor. However, it requires more coordinate transformations, current controllers and modulation schemes. In order to get quick dynamic performance, direct torque and flux controlled (DTFC) IM drive is used. The DTFC is achieved by direct and independent control of flux linkages and electromagnetic torque through the selection of optimal inverter switching which gives fast torque and flux response without the use of current controllers, more coordinate transformations and modulation schemes. Many industries have marked various forms of IM drives using DTFC since 1980. The linear fixed-gain proportional-integral (PI) based speed controller is used in DTFC of an IM drive (IMD) under various operating modes. However, The PI controller (PIC) requires proper and accurate gain values to get high performance. The PIC gain values are tuned for a specific operating point and which may not be able to perform satisfactorily when the load torque and operating point changes. Therefore, the PIC is replaced by Type-1 fuzzy logic controller (T1FLC) to improve the dynamic performance over a wide speed range and also load torque disturbance rejections. The T1FLC is simple, easy to implement and effectively deals with the nonlinear control system without requiring complex mathematical equations using simple logical rules, which are decided by the expert. In order to further improve the controller performance, the T1FLC is replaced by Type-2 fuzzy logic controller (T2FLC). The T2FLC effectively handles the large footprint of uncertainties compared to the T1FLC due to the availability of three-dimensional control with type-reduction technique (i.e. Type-2 fuzzy sets and Type-2 reducer set) in the defuzzification process, whereas the T1FLC consists only a Type-1 fuzzy sets and single membership function. The training data for T1FLC and T2FLC is selected based on the PIC scheme

Fuzzy-logic-based control, filtering, and fault detection for networked systems: A Survey

This paper is concerned with the overview of the recent progress in fuzzy-logic-based filtering, control, and fault detection problems. First, the network technologies are introduced, the networked control systems are categorized from the aspects of fieldbuses and industrial Ethernets, the necessity of utilizing the fuzzy logic is justified, and the network-induced phenomena are discussed. Then, the fuzzy logic control strategies are reviewed in great detail. Special attention is given to the thorough examination on the latest results for fuzzy PID control, fuzzy adaptive control, and fuzzy tracking control problems. Furthermore, recent advances on the fuzzy-logic-based filtering and fault detection problems are reviewed. Finally, conclusions are given and some possible future research directions are pointed out, for example, topics on two-dimensional networked systems, wireless networked control systems, Quality-of-Service (QoS) of networked systems, and fuzzy access control in open networked systems.This work was supported in part by the National Natural Science Foundation of China under Grants 61329301, 61374039, 61473163, and 61374127, the Hujiang Foundation of China under Grants C14002 andD15009, the Engineering and Physical Sciences Research Council (EPSRC) of the UK, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany

A survey of fuzzy control for stabilized platforms

This paper focusses on the application of fuzzy control techniques (fuzzy type-1 and type-2) and their hybrid forms (Hybrid adaptive fuzzy controller and fuzzy-PID controller) in the area of stabilized platforms. It represents an attempt to cover the basic principles and concepts of fuzzy control in stabilization and position control, with an outline of a number of recent applications used in advanced control of stabilized platform. Overall, in this survey we will make some comparisons with the classical control techniques such us PID control to demonstrate the advantages and disadvantages of the application of fuzzy control techniques

New methods for the estimation of Takagi-Sugeno model based extended Kalman filter and its applications to optimal control for nonlinear systems

This paper describes new approaches to improve the local and global approximation (matching) and modeling capability of Takagi–Sugeno (T-S) fuzzy model. The main aim is obtaining high function approximation accuracy and fast convergence. The main problem encountered is that T-S identification method cannot be applied when the membership functions are overlapped by pairs. This restricts the application of the T-S method because this type of membership function has been widely used during the last 2 decades in the stability, controller design of fuzzy systems and is popular in industrial control applications. The approach developed here can be considered as a generalized version of T-S identification method with optimized performance in approximating nonlinear functions. We propose a noniterative method through weighting of parameters approach and an iterative algorithm by applying the extended Kalman filter, based on the same idea of parameters’ weighting. We show that the Kalman filter is an effective tool in the identification of T-S fuzzy model. A fuzzy controller based linear quadratic regulator is proposed in order to show the effectiveness of the estimation method developed here in control applications. An illustrative example of an inverted pendulum is chosen to evaluate the robustness and remarkable performance of the proposed method locally and globally in comparison with the original T-S model. Simulation results indicate the potential, simplicity, and generality of the algorithm. An illustrative example is chosen to evaluate the robustness. In this paper, we prove that these algorithms converge very fast, thereby making them very practical to use

Fuzzy logic control for energy saving in autonomous electric vehicles

Limited battery capacity and excessive battery dimensions have been two major limiting factors in the rapid advancement of electric vehicles. An alternative to increasing battery capacities is to use better: intelligent control techniques which save energy on-board while preserving the performance that will extend the range with the same or even smaller battery capacity and dimensions. In this paper, we present a Type-2 Fuzzy Logic Controller (Type-2 FLC) as the speed controller, acting as the Driver Model Controller (DMC) in Autonomous Electric Vehicles (AEV). The DMC is implemented using realtime control hardware and tested on a scaled down version of a back to back connected brushless DC motor setup where the actual vehicle dynamics are modelled with a Hardware-In-the-Loop (HIL) system. Using the minimization of the Integral Absolute Error (IAE) has been the control design criteria and the performance is compared against Type-1 Fuzzy Logic and Proportional Integral Derivative DMCs. Particle swarm optimization is used in the control design. Comparisons on energy consumption and maximum power demand have been carried out using HIL system for NEDC and ARTEMIS drive cycles. Experimental results show that Type-2 FLC saves energy by a substantial amount while simultaneously achieving the best IAE of the control strategies tested

A novel technique for load frequency control of multi-area power systems

In this paper, an adaptive type-2 fuzzy controller is proposed to control the load frequency of a two-area power system based on descending gradient training and error back-propagation. The dynamics of the system are completely uncertain. The multilayer perceptron (MLP) artificial neural network structure is used to extract Jacobian and estimate the system model, and then, the estimated model is applied to the controller, online. A proportional–derivative (PD) controller is added to the type-2 fuzzy controller, which increases the stability and robustness of the system against disturbances. The adaptation, being real-time and independency of the system parameters are new features of the proposed controller. Carrying out simulations on New England 39-bus power system, the performance of the proposed controller is compared with the conventional PI, PID and internal model control based on PID (IMC-PID) controllers. Simulation results indicate that our proposed controller method outperforms the conventional controllers in terms of transient response and stability