Improved sliding mode control for induction motor based on twisting algorithm

Indirect Field-Oriented Control (IFOC) is the part of vector control method which is suitable for industrial application especially for induction motor (IM) drives. It causes IFOC doesn't need flux sensor which changes the construction of 1M like Direct Field-Oriented Control (DFOC). Unfortunately, the robustness and the stability of the system cannot be guaranteed by IFOC. Sliding Mode Control (SMC) is a controller which can guarantee both of them. Ordinary SMC or First-Order SMC (FOSMC) has disadvantages in chattering phenomenon because of discontinuous control input (sign(.) function). This chattering phenomenon also consumes more power to a standstill the robustness. Chattering phenomenon can be reduced by giving thin boundary layer (Boundary - SMC). Boundary - SMC using saturation function proposed to reduced chattering phenomenon and power consumption. This paper discusses FOSMC and Boundary - SMC applied for speed controller (SC) and direct-current regulator in 10HP 1M. The results show power consumption using Boundary - SMC is lower than FOSMC (real power decreases 21.26%, active power decreases 44.27% and apparent power decreases 25.49%). Power factor analyze shows FOSMC in direct-current regulator is the best but consume more real power than the others.

In this paper, a second order-sliding mode (SOSM) control strategy for Field Oriented Control (FOC) of induction motor (IM) is proposed that satisfying requirements of reliable dynamics and steady state performance. The proposed control structure is a state of the development of FOC utilized SOSM in the inner loop that employ the artificial neural network (ANN) to estimate the rotor flux and motor speed. The SOSM controller designed based on model of induction motor (IM) that include torque control loop (inners loop) and speed tracking control (outer loop). Based on the sliding state convergence property, the state variables track the reference values. By analyzing the theory, the desired performance of the proposed control system proven for various situations. The proposed control considerably ameliorates specified disadvantages of the FOC and DTC, such as the sensitivity to motor parameter variations. The simulation results indicate the correctness of the control algorithm under the uncertainty in parameters and load variations. Keywords, (FOC, ANN’s, Uncertainty, estimation, SOSM, Robust)

Induction motor is widely used due to advantages in terms of performance, size, maintenance and efficiency compared to dc motor. Induction motor is either Scalar Controlled or Vector Controlled. Magnitude of voltage and frequency is controlled in scalar control and drive has better performance under steady state. The coupling effect of flux and torque makes the drive sluggish. In vector control of induction motor drive, the flux and torque producing currents are independent of each other, making the transient response of the system better. Direct and indirect vector control is classified depending upon how the magnitude and position of the flux vector are determined. The specifications or parameters of the induction motor have least effect on the performance of the induction motor drive. This is because the measured or estimated flux is processed in the feedback loop for speed control operation of the drive. However, use of flux sensor within the machine which makes the drive uneconomical. In indirect vector control, rotor position signals are used for instantaneous rotor flux magnitude and position estimation. Motor parameters, used for estimation of flux vector vary with frequency, magnetic saturation and temperature. Vector Control is achieved using any of the rotating flux vectors in the induction motor. The vector control must have independent control of flux and torque, rotor flux orientation provides the independent control or natural decoupling. This decoupling leads to improved stability and enhanced dynamic response. Sensitivity is the problem associated with terminal quantities based flux observers. Research has been carried out to overcome the aforementioned problems by implementing accurate estimation of rotor flux. The selection criterion for the control principle depends upon the cost, accuracy, reliability and stability requirements. The uncertainties in the system parameters give way to a more robust and dynamic controller. The sliding mode control is one of the popular control techniques used to handle the uncertain system parameters, model uncertainties and external load variations which exist in induction motor drives. The induction motors used in traction require low speeds only during starting and low speed operations. The power output of the motor can be maximized by limiting the current drawn by the motor to rated value and lowering the reference voltage. The sliding mode control offers a robust tracking of a given speed demand when subjected to disturbances from both input and output side. This gives better performance compared to other techniques. Control Algorithm: Sliding Mode Controlled three-phase induction motor is shown in Fig. 1. It consists of three-phase rectifier converting the ac grid voltage into dc voltage which forms dc bus for PWM Inverter. Sliding mode algorithm is used for controlling the inverter which powers the three-phase induction motor.The aim of the control algorithm is to control the inverter voltage so that the induction motor tracks the desired speed. Two-line voltages and phase currents are sensed into the controller. These line quantities are converted to α-β components by the conventional 3-phase to 2-phase transformation. Synchronous speed is determined using frequency estimator and then estimated mechanical speed is compared with speed reference to give speed error. This error forms the input for the Sliding Mode control. In addition to this, torque current reference and actual torque current component are compared to give the voltage reference Vds. The voltages are converted to Va, Vb and Vc using the reverse transformation. By conventional Sine-triangle comparison, switching pulses are generated. SIMULATION RESULTS: Motor response with Indirect Field Oriented Control is as shown in Fig. 2. It can be observed that the motor accelerates at the starting with maximum torque. As the motor reaches the desired speed, the generated torque becomes minimum. At t = 0.5 sec, load torque is applied on the motor resulting in the transient shown.Fig. 2 - Electromagnetic torque and Speed of the three phase induction motor with IFOC algorithm. For the same transient conditions, motor is controlled using Sliding Mode control algorithm. The results are shwon in Fig. 3. The motor torque and speed response shows lower ripple which which validates the robustness of the algorithm.Fig. 3 - Electromagnetic torque and speed of the three phase induction motor with Sliding Mode Control.

Sliding mode control (SMC) of induction motor is a new concept in the current scenario, as it seeks to improve torque control accuracy and power steering efficiency through the use of pulse width modulation schemes. Furthermore, artificial neural network‐based sliding mode control is applied to a squirrel cage induction motor, which is used in the steering control of automobiles. The artificial neural network (ANN)‐based SMC is more popular due to its robustness and good stability in external parameter variation. Additionally, an SMC and an ANN‐based SMC are employed to compute the torque and flux, improving performance for power steering applications. The performance of the designed model is validated through MATLAB/Simulink and experimental models with different controllers under various operating conditions. The controller has been embedded into a TMS320F28335 controller, and performances have been evaluated. The performance analyses of induction motor using different controllers are performed. The transient performances of induction motor such as delay time, rise time, settling time, and steady‐state error are investigated. The proposed work is analysed by using a mathematical model and implemented in a test‐bench model for validation.

This paper presents the use of a higher order sliding mode scheme for sensorless control of induction motors (IM). The sliding mode approach is used both for control and for generating the rotor speed and the rotor flux estimates necessary for the controller, based only on the measurements of stator voltages and currents. The second order sliding mode control (SOSMC) is based on a super-twisting algorithm. Then, the proposed control scheme and the observer are tested on an experimental setup confirming their convergence properties. The used observer converges in a finite time and permits to give a good estimate of flux and speed even in presence of rotor and stator resistance variations.

This paper proposes a new flux and speed sliding mode controller for sensorless induction motor. A combined controller and observer design allows, under parametric uncertainties, to achieve the tracking of rotor flux and mechanical speed for induction motor without the need of flux, speed and load torque measurements. Based on Lyapunov theory, firstly the stability of the proposed controller is given. Secondly, the stability of the proposed observer is proved and then the proof of the whole closed-loop system stability "controller+observer" is given under induction motor parametric uncertainties. Furthermore, due to the complexity of observation at low frequencies, the proposed controller using an observer is tested and validated on reference trajectories of a sensorless induction motor control benchmark. This benchmark is applied on an experimental set-up located at IRCCyN (Nantes, France). The applicability of the designed controller is emphasized by the robustness experiment tests with respect to parameters variation of induction motor.

This paper presents design and practical implementation of robust super twisting type Second Order Sliding Mode (SOSM) controller for an under actuated 2-DOF flexible link manipulator system. The flexible link system resembles to a robotic arm having two flexible links governed by two DC motors. The mathematical model of the system is derived using Euler-Lagrange formula. A goal of Super Twisting (ST) controller is to regulate the position of both arms simultaneously having coupling effect. The STA of SOSMC attenuate the chattering effect that predominates in the classical sliding mode controller. The efficacy of the controller is checked in both simulation as well experimentally. The simulation is carried out using MATLAB R2016. The experiment is carried out on a laboratory setup using QUARC software (for hardware interfacing) and MATLAB R2016. The simulation as well experimental results are compared with conventional LQR controller and classical f-order sliding mode controller. The results endows that the 2-order SMC controller outperforms the other controllers.

This article presents an original motion control strategy for robot manipulators based on the coupling of the inverse dynamics method with the so-called second-order sliding mode control approach. Using this method, in principle, all the coupling non-linearities in the dynamical model of the manipulator are compensated, transforming the multi-input non-linear system into a linear and decoupled one. Actually, since the inverse dynamics relies on an identified model, some residual uncertain terms remain and perturb the linear and decoupled system. This motivates the use of a robust control design approach to complete the control scheme. In this article the sliding mode control methodology is adopted. Sliding mode control has many appreciable features, such as design simplicity and robustness versus a wide class of uncertainties and disturbances. Yet conventional sliding mode control seems inappropriate to be applied in robotics since it can generate the so-called chattering effect, which can be destructive for the controlled robot. In this article, this problem is suitably circumvented by designing a second-order sliding mode controller capable of generating a continuous control law making the proposed sliding mode controller actually applicable to industrial robots. To build the inverse dynamics part of the proposed controller, a suitable dynamical model of the system has been formulated, and its parameters have been accurately identified relying on a practical MIMO identification procedure recently devised. The proposed inverse dynamics-based second-order sliding mode controller has been experimentally tested on a COMAU SMART3-S2 industrial manipulator, demonstrating the tracking properties and the good performances of the controlled system.

This paper presents the cascaded PI-continuous second order sliding mode control for induction motor in the presence of operational constraints. The inner-loop Sliding Mode Control (SMC) is designed to control the current dynamics of the motor while the outer-loop control is the PI control of speed. The main advantages of the proposed method are that the PI control provides reference to inner-loop SMC with constraints according to the system requirements in terms of maximum current and speed limits. Moreover the inner-loop dynamics of the motor being more non-linear, SMC design has more importance in terms of robustness and disturbance rejection capability. The proposed cascade PI with SMC is chattering-free control action with fixed-time convergence. The performance of the developed controller is validated and compared by carrying out real-time experimental studies. Experimental results demonstrate remarkable robust tracking performance of the controller in terms of transient response speed and steady-state accuracy.

In this paper, a sensorless control scheme is presented for induction motors with core loss. First, a controller is designed using a high order sliding mode twisting algorithm, to track a desired rotor velocity signal and an optimal rotor flux modulus, minimizing the power loss in copper and core. Then, a super-twisting (ST) sliding mode observer for stator current is designed and the rotor flux is calculated, by means of the equivalent control method. Two methods for the rotor velocity estimation are then proposed. The first consists of a further super-twisting sliding mode observer for rotor fluxes, with the purpose of retrieving the back-electromotive force components by means of the equivalent control method. These components are functions of the rotor velocity which, hence, can be easily determined. The second method is based on a generalization of the phase-locked loop methodology. Finally, a simple Luenberger observer is designed, filtering the rotor velocity estimate and giving also an estimate of the load torque. The performance of the motor is verified by means of numeric simulations and experimental tests, where good tracking results are obtained.

Passivity-based second-order sliding mode control via the homogeneous Lyapunov approach for mechanical port-Hamiltonian systems

The Doubly Fed Induction Generator (DFIG) in wind system is linked to the power-grid, which is vulnerable to significant grid failures. Because of the DFIG’s sensitivity to disturbances in the grid, designing of the controller is considered. In this article, the Feed-Forward Neuro-Second Order Sliding Mode (FFN-SOSM) controller and the Second Order Sliding Mode (SOSM) controller are compared for the Low Voltage Ride Through (LVRT) enhancement under voltage sag situation. Convergence in the sliding surface control is assisted by the use of higher-order switching functions in the FFN-SOSM. Importantly, controller benefits are such that reduction in chattering effect and minimized settling time for the system’s parameters as a result in the implementation of the FFN-SOSM method. With the assistance of MATLAB/SIMULINK, a comparison is made between the performances of the FFN-SOSM controller and those of SOSM controller, which is described in the existed research. The results of the simulation indicate that the "FFN-SOSM controller improved the LVRT capability of the DFIG-Wind Turbine (WT) system" when it is functioning under dynamic conditions.

Design of sliding mode controller for induction motor drive

Practical second order sliding modes in single-loop networked control of nonlinear systems

Vector control has been widely used in induction motor speed settings due to its high performance and ability to be separately controlled as DC motors. Indirect Field Oriented Control (IFOC) is a vector control method applicable to induction motors due to the ease of design and implementation. However, its performance decreases due to changes in motor parameter variations and load disturbances, which led to the use of Sliding Mode Control (SMC), capable of resisting and stabilizing parameter changes and load disturbances. In the First Order Sliding Mode Control (FOSMC), a dangerous chattering phenomenon occurs and results in the stability of the control system. Therefore, this research designed a Boundary-SMC controller to control the speed of a 3-phase induction based on IFOC with a change in load disturbance to reduce the chattering phenomenon at low speeds, where the largest percentage occurs. Furthermore, this research used Lyapunov's stability theory to ensure stability with the performance of these controls monitored and analyzed in LabView. The results showed that FOSMC has better resilience and stability than PID, with a steady error rate of 0.001% and a chattering percentage of 0.12%. With Boundary-SMC, the chattering percentage and stiffness are reduced to 0.005% and 0.284%, respectively.