Tae-Yong Kuc

An iterative learning control theory for a class of nonlinear dynamic systems

Abstract An iterative learning control scheme is presented for a class of nonlinear dynamic systems which includes holonomic systems as its subset. The control scheme is composed of two types of control methodology: a linear feedback mechanism and a feedforward learning strategy. At each iteration, the linear feedback provides stability of the system and keeps its state errors within uniform bounds. The iterative learning rule, on the other hand, tracks the entire span of a reference input over a sequence of iterations. The proposed learning control scheme takes into account the dominant system dynamics in its update algorithm in the form of scaled feedback errors. In contrast to many other learning control techniques, the proposed learning algorithm neither uses derivative terms of feedback errors nor assumes external input perturbations as a prerequisite. The convergence proof of the proposed learning scheme is given under minor conditions on the system parameters.

Adaptive learning control of uncertain robotic systems

An adaptive learning control scheme is presented for uncertain robotic systems that is capable of tracking the entire profile of the reference input. The control scheme consists of three control blocks: a linear feedback, a feedforward error compensation and a learning strategy. At each iteration, the linear feedback with the feedforward error compensation provides stability of the system and keeps its state errors within uniform bounds. The learning strategy, on the other hand, estimates the desired control input and uncertain system parameters, which are used to track the entire span of a reference input over a sequence of iterations. In contrast with many other learning control techniques, the proposed learning algorithm neither uses derivative terms of feedback errors nor assumes any perturbations on the learning control input as a prerequisite. The parameter estimator neither uses any joint acceleration terms nor uses any inversion of the estimated inertia matrix, which makes its implementation pract...

Brief An adaptive PID learning control of robot manipulators

An adaptive PID learning controller which consists of an adaptive PID feedback control scheme and a feedforward input learning scheme is proposed for learning of periodic robot motion. In the learning controller, the adaptive PID feedback controller takes part in stabilizing the transient response of robot dynamics while the feedforward learning controller plays a role in computing the desired actuator torques for feedforward nonlinear dynamics compensation in steady state. It is shown that all the error signals in the learning control system are bounded and the robot motion trajectory converges to the desired one asymptotically. In addition, the proposed adaptive PID learning controller is compared with the fixed PID learning controller in terms of the stability condition of PID gain bound, the performance of tracking, and the convergence rate of learning system.

Genetic algorithm based path planning and dynamic obstacle avoidance of mobile robots

A simple path planning scheme is proposed for navigation of mobile robots while avoiding obstacles. In generating the goal directed dynamic path, the path planning scheme uses a genetic search algorithm whose coding technique speeds up the execution of genetic search for fast path generation. The fitness value of the generated paths is evaluated in terms of the safety from the obstructing dynamic objects and the distance to the goal position by the genetic algorithm. The execution time of genetic search is further accelerated by projecting the two dimensional data to one dimensional ones to reduce the size of search space.

An adaptive PID learning control of DC motors

With only the classical PID controller applied to control of a DC motor, a good (target) performance characteristic of the controller can be obtained, if all the model parameters of DC motor and operating conditions such as external load torque, disturbance, etc, are exactly known. However, in case when some of system parameters or operating conditions are uncertain or unknown, the fixed PID controller does not guarantee the good performance which is assumed with precisely known system parameters and operating conditions. In view of this and robustness enhancement of DC motor control system, we propose an adaptive PID learning controller which consists of a set of learning rules for PID gain tuning and learning of an auxiliary input. The proposed PID learning controller is shown to drive the state of uncertain DC motor system with unknown system parameters and external load torque to the desired one globally asymptotically. Computer simulation and experimental results are given to demonstrate the effectiveness of the proposed PID learning controller, thereby showing its superiority to the conventional fixed PID controller.

Brief Lower matrix bounds for the continuous algebraic Riccati and Lyapunov matrix equations

In this paper, we propose lower matrix bounds for the continuous algebraic Riccati and Lyapunov matrix equations. We give comparisons between the parallel estimates. Finally, we give examples showing that our bounds can be better than the previous results for some cases.

Stability guaranteed auto-tuning algorithm of a time-delay controller using a modified Nussbaum function

Time-delay control (TDC) has been widely used to control various systems thanks to its simplicity and robustness. The control distribution matrix of TDC is assumed to be constant and tuned heuristically. However, the constant TDC control gain could degrade the system control performance and even cause the closed-loop system to become unstable when the system parameters are substantially changing, resulting in the violation of the stability criterion of TDC. We propose an algorithm for automatic tuning of the TDC gain in order to guarantee stability by using a modified Nussbaum function. Thus, the closed-loop system controlled by TDC with the modified Nussbaum function is proven to be semi-globally uniformly ultimately bounded. The auto-tuned gain satisfies the stability criterion of TDC and the proposed method can widen the range of implementable dynamic systems. Simulations are conducted to verify the simplicity, robustness, and guaranteed stability by auto-tuning of the proposed method.

Remote-controlled Home Robot Server with Zigbee Sensor Network

Recently, interest of general public toward ubiquitous home network has immensely increased and wireless PAN (personal area network) has attracted strong attentions as short-distance networking solution. Convenience of wireless PAN technology has attracted more attentions over traditional wired home network devices such as Ethernet, PLC and HomePNA since it requires no cabling work. In the future, home server will be combined with robot to provide functionalities identical to current home service robot as well as to implement more effective and spontaneous server. In this paper, embedded board has proposed as a home server for an efficient control of internal information and conditions of house from remote location and virtual home robot server to be implemented with Zigbee sensor network

An embodiment of stereo vision system for mobile robot for real-time measuring distance and object tracking

All these days, the active stereo vision system which can detect the certain object and measure the distance from the object through the cameras image have been studied. In the stereo vision system, it is the key point to search the certain object through the image and to track the object by controlling the motor which supports the camera based on the searched object information. Also, as the human being can estimate the distance from the object by using two eyes, the distance from the certain object can be estimated by using two cameras in the stereo vision system. When the certain object is located on the corner of image plane in the stereo vision system where the existing camera is fixed, an error occurs due to a lenss distortion, so reliability goes down. However, the cross stereo vision system always places the object in the middle of image and places the object in the middle of low distortion lens, so it can increase reliability of information gained from the system as its advantage. This paper is intended to implement the stereo vision system which can track the object and can measure the distance from the object in real time by applying the trigonometric measurement method and the robot kinematics to the cross-visual stereo vision system which is fabricate to be applied to the mobile robot and by distributing the controllers load, and to evaluate the performance by applying the mobile robot under an indoor lighting environment.

Brief Wiener-Hopf design of the optimal decoupling control system with state-space formulas

The optimal decoupling controller minimizing a given quadratic cost function is derived for the generalized plant model with two-degree-of-freedom controller configuration. A minimal set of assumptions for the existence of the optimal controller is presented in both the frequency and the state-space domains. The optimal controller formula is described in the frequency domain and the corresponding state-space formula is derived for computational efficiency.