• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.56 no.1
• /
• pp.6-12
• /
• 2007

In this paper, an robust adaptive backstepping controller is proposed for the speed control of separately excited DC motor in pure electric vehicles. A general electric drive train of PEV is conceptually rearrange to major subsystems as electric propulsion, energy source, and auxiliary subsystem and the load torque is modeled by considering the aerodynamic, rolling resistance and grading resistance. Armature and field resistance, damping coefficient and load torque are considered as uncertainties and noise generated at applying load torque to motor is also considered. It shows that the backstepping algorithm can be used to solve the problems of nonlinear system very well and robust controller can be designed without the variation of adaptive law. Simulation results are provided to demonstrate the effectiveness of the proposed controller.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.37 no.2
• /
• pp.1-7
• /
• 2000

In this paper, an adaptive control law for nonlinear systems represented by input-output models are proposed under the assumption that unknown system parameters are in a known compact and convex set. Contrary to the previous results, the compact and convex set is not restricted to a ball whose center is at the origin or convex hypercube. It is proven that the proposed parameter update rule produces a sequence of parameters which reside in the set and guarantees that the position, velocity, and acceleration error converges to zero as time goes to infinity. This theoretical result was justified through simulations.

• Journal of Electrical Engineering and Technology
• /
• v.8 no.3
• /
• pp.516-523
• /
• 2013

This paper investigates an improved stator flux linkage observer for sensorless permanent magnet synchronous motor (PMSM) drives using a voltage-based flux linkage model and an adaptive sliding mode variable structure. We propose a new observer design that employs an improved sliding mode reaching law to achieve better estimation accuracy. The design includes two models and two adaptive estimating laws, and we illustrate that the design is stable using the Popov hyper-stability theory. Simulation and experimental results demonstrate that the proposed estimator accurately calculates the speed, the stator flux linkage, and the resistance while overcoming the shortcomings of traditional estimators.

• Proceedings of the KIEE Conference
• /
• 2000.07d
• /
• pp.2729-2731
• /
• 2000

In this paper, we propose an adaptive controller using RBFN(radial basis function network) for robot manipulators. The structure of the proposed controller consists of a RBFN and a fixed gain PD controller. On the basis of the Lyapunov stability theorem, we guarantee the UUB (uniformly ultimately boundedness) for the total system. And the learning law of RBFN is established by the Lyapunov method. Finally, we apply the proposed controller to tracking control for the 2 link SCARA type robot manipulator.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.57 no.5
• /
• pp.845-851
• /
• 2008

A simple RMRAC (Robust Model Reference Adaptive Control) scheme for the PMSM (Permanent Magnent Synchronous Motor) is proposed in the synchronous frame. A current control of PMSM is the most inner loop of electro-mechanical driving systems and it requires a fast and simple control law to play a foundation role in the control hierarchy. In the proposed synchronous current model, the input signal is composed of a calculated voltage by proposed adaptive laws and real system disturbance. The gains of feed-forward and feedback controllers are estimated by the proposed modified Gradient method respectively, where the system disturbances are assumed as filtered current tracking errors. After the estimation of the system disturbances from the tracking errors, the corresponding voltage is fed forward to control input voltage to compensate for the disturbances. The proposed method is robust against high frequency disturbance and has a fast dynamic response. It also shows a good real-time performance due to it's simplicity of control structure. Through the simulations and real experiments, efficiency of the proposed method is verified.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.23 no.11
• /
• pp.9-21
• /
• 2009

This paper proposes the alternative observer and controller design scheme based on T-S fuzzy model. Nonlinear systems are represented by fuzzy models since fuzzy logic systems are universal approximators. In order to estimate the unmeasurable states of a given unknown nonlinear system, T-S fuzzy modeling method is applied to get the dynamics of an observation system. T-S fuzzy system uses the linear combination of the input state variables and the modeling applications of them to various kinds of nonlinear systems can be found. The proposed indirect adaptive fuzzy observer based on T-S fuzzy model can cope with not only unknown states but also unknown parameters. The proposed controller is based on a simple output feedback method. Therefore, it solves the singularity problem, without any additional algorithm, which occurs in the inverse dynamics based on the feedback linearization method. The adaptive fuzzy scheme estimates the parameters and the feedback gain comprising the fuzzy model representing the observation system. In the process of deriving adaptive law, the Lyapunov theory and Lipchitz condition are used. To show the performance of the proposed observer and controller, they are applied to an inverted pendulum on a cart.

• 제어로봇시스템학회:학술대회논문집
• /
• 2005.06a
• /
• pp.999-1004
• /
• 2005

An algorithm for a hybrid controller consists of a sliding mode control part and a fuzzy logic part which ar purposely for nonlinear systems. The sliding mode part of the solution is based on "eigenvalue/vector"-type controller is used as the backstepping approach for tracking errors. The fuzzy logic part is a Mamdani fuzzy model. This is designed by applying sliding mode control (SMC) method to the dynamic model. The main objective is to keep the update dynamics in a stable region by used SMC. After that the plant behavior is presented to train procedure of adaptive neuro-fuzzy inference systems (ANFIS). ANFIS architecture is determined and the relevant formulation for the approach is given. Using the error (e) and rate of error (de), occur due to the difference between the desired output value (yd) and the actual output value (y) of the system. A dynamic adaptation law is proposed and proved the particularly chosen form of the adaptation strategy. Subsequently VSC creates a sliding mode in the plant behavior while the parameters of the controller are also in a sliding mode (stable trainer). This study considers the ANFIS structure with first order Sugeno model containing nine rules. Bell shaped membership functions with product inference rule are used at the fuzzification level. Finally the Mamdani fuzzy logic which is depends on adaptive neuro-fuzzy inference systems structure designed. At the transferable stage from ANFIS to Mamdani fuzzy model is adjusted for the membership function of the input value (e, de) and the actual output value (y) of the system could be changed to trapezoidal and triangular functions through tuning the parameters of the membership functions and rules base. These help adjust the contributions of both fuzzy control and variable structure control to the entire control value. The application example, control of a mass-damper system is considered. The simulation has been done using MATLAB. Three cases of the controller will be considered: for backstepping sliding-mode controller, for hybrid controller, and for adaptive backstepping sliding-mode controller. A numerical example is simulated to verify the performances of the proposed control strategy, and the simulation results show that the controller designed is more effective than the adaptive backstepping sliding mode controller.

• Journal of Advanced Navigation Technology
• /
• v.13 no.5
• /
• pp.756-762
• /
• 2009

As nonlinear friction, stick-slip friction in hydraulic actuators are a problem for accuracy and repeatability. Therefore friction compensation has been approached through various control algorithms. A Adaptive discrete time sliding mode tracking controller has been applied in order to compensate the nonlinear friction characteristics in a hydraulic Actuator. Based on the diophantine equation, a new discrete time sliding function is defined and utilized for the control law which includes a friction and modeling error. Robustness is increased by using both a projection algorithm and a sliding function-based nonlinear feedforward. From the results of simulation and experiment good tracking performance is achieved.

• Journal of Auto-vehicle Safety Association
• /
• v.14 no.2
• /
• pp.26-38
• /
• 2022

This paper proposes multiple RLS and actuator performance index-based adaptive actuator fault-tolerant control and detection algorithms for longitudinal autonomous driving. The proposed algorithm computes the desired acceleration using feedback law for longitudinal autonomous driving. When actuator fault or performance degradation exists, it is designed that the desired acceleration is adjusted with the calculated feedback gains based on multiple RLS and gradient descent method for fault-tolerant control. In order to define the performance index, the error between the desired and actual accelerations is used. The window-based weighted error standard deviation is computed with the design parameters. Fault level decision algorithm that can represent three fault levels such as normal, warning, emergency levels is proposed in this study. Performance evaluation under various driving scenarios with actuator fault was conducted based on co-simulation of Matlab/Simulink and commercial software (CarMaker).

• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.36S no.10
• /
• pp.51-57
• /
• 1999

In this paper, we deal with the problem of controlling an unknown nonlinear dynamical system, using wavelet network. Accurate control of the nonlinear systems depends critically on the accuracy and efficiency of the function approximator used to approximate the function. Thus, we use wavelet network which shows high capability of approximating the functions and includes the free-selection of basis functions for the control of the nonlinear system. We find the dilation and translation that are wavelet network parameters by analyzing the time-frequency characteristics of the controller's input to construct an initial adaptive wavelet network controller. Then, weights is adjusted by the adaptive law based on the Lyapunov stability theory. We apply this direct adaptive wavelet network controller to control the inverted pendulum system which is an nonlinear system.