Myoung-Sam Ko

Control of induction motors via feedback linearization with input-output decoupling

In induction motor control, power efficiency is an important factor to be considered. We attempt to achieve both high dynamic performance and maximum power efficiency by means of linear decoupling of rotor speed (or motor torque) and rotor flux. The induction motor with our controller possesses the input-output dynamic characteristics of a linear system such that the rotor speed (or motor torque) and the rotor flux are decoupled. The rotor speed responses are not affected by abrupt changes in the rotor flux and vice versa. The rotor flux need not be measured but is estimated by the well known flux simulator. The effect of large variation in the rotor resistance on the control performances is minimized by employing a parameter adaptation method. To illuminate the significance of our work, we present simulation and experimental results as well as mathematical performance analyses. In particular, our experimental work demonstrates that recently developed nonlinear feedback control theories are of practical use.

Control of induction motors for both high dynamic performance and high power efficiency

The authors attempt to control induction motors with maximum power efficiency as well as high dynamic performance by means of decoupling of motor speed (or motor torque) and rotor flux. For maximum power efficiency, the squared rotor flux is adjusted according to a minimum power search algorithm until the measured power input reaches the minimum. Since the motor speed is dynamically decoupled from the rotor flux, this can be done successfully without any degradation of motor speed responses. The controller depends on rotor resistance but not on stator resistance. However, the performance of the control scheme is robust with respect to variations in rotor resistance because an identification algorithm for rotor resistance is employed. The identification algorithm for rotor resistance has some advantages over the previous methods. To demonstrate the practical significance of the results, some experimental results are presented. >

Performance analysis of PNG laws for randomly maneuvering targets

The performance of the conventional proportional navigation guidance (PNG) law for a randomly maneuvering target is considered. By means of the Lyapunov method, it is proved that an ideal missile guided by the conventional PNG law can always intercept a target which maneuvers with time-varying normal acceleration provided that the navigation constant is sufficiently large. The authors also propose a modified PNG (MPNG) law which seems to improve the performance of the conventional PNG law at the final phase of pursuit. Simulation results demonstrate that the MPNG law demands less missile acceleration at the final phase of pursuit than the conventional PNG law. >

A resolver-to-digital conversion method for fast tracking

A resolver-to-digital (R/D) conversion method in which a bang-bang type phase comparator is used for fast tracking is proposed. The low-pass filter needed to reject carrier signal and noise is eliminated from the R/D conversion loop. Instead, two prefilters outside the R/D conversion loop take the role of the low-pass filter, resulting in a fast and accurate tracking R/D converter. Some simulation and experimental results and a mathematical performance analysis are presented to demonstrate the superior tracking performance. >

An efficient estimation algorithm for the model parameters of robotic manipulators

An approach to identification of the model parameters of robotic manipulators is proposed in which neither prior knowledge of the geometric parameters nor restrictive robot motions are required. The number of the model parameters to be identified is minimized through a regrouping procedure for the Lagrangian functions of robotic manipulators. The identification model for the model parameters is formulated in an upper block triangular form. Based on these results, a computationally efficient estimation algorithm for the model parameters is obtained. To illustrate the practical use of the method, a four-degree-of-freedom SCARA robot is examined. >

An application of force ellipsoid to the optimal load distribution for two cooperating robots

The optimal load distribution for two cooperating robots is studied, and a solution approach utilizing a force ellipsoid is presented. The load distribution problem is formulated as a nonlinear optimization problem with a quadratic cost function. The effort exerted by the robots to follow the specified motion is defined to be the joint torque norm square, and the optimal solution minimizing the effort is obtained using the concept of the force ellipsoid and nonlinear optimization theory. Despite the presence of the joint torque constraints, the optimal solution is obtained almost as a closed form which requires small computation time. The proposed solution approach is illustrated and the internal force effect is studied using a numerical example.<<ETX>>

Speed and efficiency control of induction motors via asymptotic decoupling

An attempt is made to control induction motors with high power efficiency as well as high dynamic performance by utilizing recently developed nonlinear feedback control techniques. The controller consists of three subcontrollers: a saturation current controller, a decoupling controller, and a well-known flux simulator. The decoupling controller decouples rotor speed (or motor torque) and rotor flux linearly. The controller does not need the transformation between a d-q synchronously rotating frame and an x-y stator-fixed frame. It is computationally quite simple and does not depend on the rotor resistance, which varies widely with the machine temperature. Performance analysis and simulation results are presented.<<ETX>>

Time-optimal motion of a cooperating multiple robot system along a prescribed path

This paper deals with a systematic analysis of the time-optimal motion of a multiple robot system carrying an object along a prescribed path. In our approach, the time-optimal motion planning problem is formulated in a concise form by employing a parameter describing the movement along the path and a vector representing the internal force. Various constraints governing the motion yield the so-called admissible region in the phase plane of the parameter. The orthogonal projection technique and the theory of multiple objective optimization make it possible to construct the admissible region, while taking into account the load distribution problem that is coupled with the motion. Furthermore, our approach provides a way of detailed investigation for the admissible region that is not simply connected. The resulting velocity profile of the path parameter and the internal force at every instant determine the optimal actuator torques for each robot. Computer simulation results reinforce the physical interpretation on the characteristics of the optimal actuator torques.

A new approach to feedback linearizing control of variable reluctance motors for direct-drive applications

In this paper, we consider feedback-linearizing control of VR (variable reluctance) motors which have been increasingly used in high performance direct-drive applications. We characterize all torque controllers that can make the generated torque of a VR motor linear to torque command but without torque ripple. The torque controllers maximize the range of torque commands which are admissible under the physical limitation in stator currents. The whole class of all such torque controllers is parameterized in the explicit form which contains a function to be chosen freely. This free function can be used to achieve other control objectives as well as linear dynamic characteristics. As the example for optimal choice of the free function, we actually determine an optimal free function for minimal rate of change in current commands. To illuminate further the practical use of our torque controllers, we present some experimental results for the case of a commercially available VR motor.<<ETX>>

An iterative learning approach to error compensation of position sensors for servo motors

In this paper, we present an iterative learning method of compensating for position sensor errors. Unlike the previously known compensation algorithms, the method presented does need a special perfect position sensor or a priori information about error sources. To the best of our knowledge, any iterative learning approach has not been taken for sensor error compensation. Furthermore, our iterative learning algorithm does not have the drawbacks of the existing iterative learning control theories. To be more specific, our algorithm learns a uncertain function itself rather than its special time-trajectory and does not require the derivatives of measurement signals. Moreover, it does not require the learning system to start with the same initial condition for all iterations. To illuminate the generality and practical use of our algorithm, we give a rigorous proof for its convergence and some experimental results.<<ETX>>