In this paper the regulator and filter algebraic Riccati equations, corresponding to the steady state optimal control and filtering of weakly coupled linear discrete stochastic systems are solved in terms of reduced-order sub problems by using the eigenvector approach. The eigenvector method outperforms iterative methods (fixed point iterations, Newton method) of solutions to reduced-order sub problems in case of higher level of coupling between subsystems. In such cases the iterative methods could fail to produce solutions of the corresponding algebraic Riccati equations.

In this paper a general transformation for block- diagonalization (decoupling) of multi time-scale singularly perturbed linear systems composed of N subsystems is introduced. The resulting decoupling is consistent to subsystem time-scales and produces N completely independent subsystems. The block-diagonalization transformation matrix is obtained by successive solutions of reduced-order nonsquare, nonsymmetric, algebraic Riccati equations. The nonsquare, nonsymmetric, algebraic Riccati equations can be efficiently solved by the Newton iterative method.

In this paper a novel transformation is introduced for block-diagonalization of weakly coupled linear systems composed of N subsystems. The block-diagonalization transformation matrix is obtained by successive solution of reduced-order nonsquare, nonsymmetric, algebraic Riccati equations. The nonsquare, nonsymmetric, algebraic Riccati equations can be efficiently solved by iterative methods.

In this paper the regulator and filter algebraic Riccati equations, corresponding to the steady state optimal control and filtering of weakly coupled linear continuous-time stochastic systems are solved in terms of reduced-order sub problems by using the eigenvector approach. In addition, the optimal global Kalman filter is decomposed into local optimal filters both driven by the system measurements and the system optimal control inputs. The eigenvector method outperforms iterative methods (fixed point iterations, Newton method) of solutions to reduced-order sub problems in case of higher level of coupling between subsystems. In such cases the iterative methods could fail to produce solutions of the corresponding algebraic Riccati equations.

In this paper a two-step design methodology of the near optimal Mamdani type fuzzy logic controller (FLC) has been applied to three types of systems: a linear time invariant system (LTI), a LTI system with time delay and an nonlinear system. In the first step, the tuning/learning procedure of a data base/knowledge base of the FLC is based on the model of the system using genetic algorithms (GA). The achieved solution is the optimal one with regard to the model of the system. In the second step experiments are performed on the real system at the vicinity of the optimum (achieved by GA) and using response surface methodology (RSM) control parameters are readjusted in order to achieve the near optimal solution for the real system. The proposed two step methodology gives a systematic way of the near optimal FLC tuning/learning when confronted with the real system and a very efficient combination of off-line and online part of design procedure.

The validation of a power system clustering algorithm proposed by Glavic et al. on a real-life power system, the Bosnian electric power system, is presented. Voltage collapse is associated with a stress condition of the power system. Theoretical investigations of this complex phenomenon have resulted in many different analyses and solutions. It has been confirmed that the voltage instability problems start locally at the weakest nodes in a reduced area of the network, and the problem spreads to neighbouring nodes, resulting in a cascading phenomenon. Identification of reduced areas in a power system, with coherent behaviour of node voltages, may reduce the computational burden for voltage collapse analysis. A network clustering is proposed to reduce the computational burden associated with the analysis of voltage instability in large power networks. The algorithm is based on a method which was initially developed for coherency based system decomposition into study and external areas based on the linearised dynamic model of power system (satisfactory results have been obtained using the 30-bus New England test system). The algorithm is disturbance-independent, computationally fast and avoids computational burden because it requires only a limited number of basic arithmetic operations on the reduced Jacobian matrix of the load flow model.

In this paper a new control scheme for a robot manipulator based on visual information is proposed. The control system determines the position and orientation of the robot gripper in order to achieve desired grasping relation between the gripper and a 3D object. The proposed control scheme consists of two distinct stages: (1) Learning stage, in this stage the robot system reconstructs a 3D geometrical model of a presented unknown object within a class of objects (polyhedra), by integrating information from an image sequence obtained from a camera mounted on the robot manipulator (eye-in-hand). This model is represented by a set of 3D line segments and denoted as a reference model. The robot is also taught desired grasping relation by manual guidance. (2) Execution stage, in this stage the robot system reconstructs a 3D model of the arbitrarily placed 3D object. This model is denoted as an observed model. Then, the necessary position and orientation of its gripper is determined based on estimated 3D displacement between the reference and observed models. Further, the basic algorithm is extended to handle multiple objects manipulation and recognition. The performance of the proposed algorithms has been tested on the real robot system and the experimental results are presented.

Enabling robot manipulators to manipulate and/or recognise arbitrarily placed 3D objects under sensory control is one of the key issues in robotics. Such robot sensors should be capable of providing 3D information about objects in order to accomplish the above mentioned tasks. Such robot sensors should also provide the means for multisensor or multimeasurement integration. Finally, such 3D information should be efficiently used for performing desired tasks. This work develops a novel computational frame wo rk for solving some of these problems. A vision (camera) sensor is used in conjunction with a robot manipulator, in the frame-work of active vision to estimate 3D structure (3D geometrical model) of a class of objects. Such information is used for the visual robot control, in the frame-work of model based vision. One part o f this dissertation is devoted to the system calibration. The camera and eye/hand calibration is presented. Several contributions are introduced in this part, intended to improve existing calibration procedures. This results in more efficient and accurate calibrations. Experimental results are presented. Second part of this work is devoted to the methods of image processing and image representation. Methods for extracting and representing necessary image features comprising vision based measurements are given. Third part of this dissertation is devoted to the 3D geometrical model reconstruction of a class o f objects (polyhedral objects). A new technique for 3D model reconstruction from an image sequence is introduced. This algorithm estimates a 3D model of an object in terms of 3D straight-line segments (wire-frame model) by integrating pertinent information over an image sequence. The image sequence is obtained from a moving camera mounted on a robot arm. Experimental results are presented. Fourth part of this dissertation is devoted to the robot visual control. A new visual control strategy is introduced. In particular, the necessary homogeneous transformation matrix for the robot gripper in order to grasp an arbitrarily placed 3D object is estimated. This problem is posed as a problem of 3D displacement (motion) estimation between the reference model of an object and the actual model of the object. Further, the basic algorithm is extended to handle multiple object manipulation and recognition. Experimental results are presented.

Using known camera motion to estimate 3D structure of a scene from image sequences is an important task in robot manipulation and navigation. This paper presents a new way to integrate 3D structure of a scene, by tracking and fusing 2D line segment measurements over image sequences. The system is based on a cyclic process. The model structure undergoes a cycle of prediction, matching and updating. The process of tracking, matching and updating is based on Kalman filtering framework. In this work no constraint on camera motion is used and segment tracking is based on estimated structure instead of on traditional features tracking based on image motion heuristics. This approach provides for reliability, accuracy, and computational advantages. Experimental results from a camera mounted on a robot arm are presented to illustrate reliability and accuracy of the approach to integrate 3D structure of a scene.

This paper addresses an approach to the problem of determining the 3D location of points of an object in the environment of a moving camera mounted on a robot arm, based on a monocular image sequence obtained by the camera. These points can be either endpoints of the line segments or other feature points. The robot arm's velocity and position are assumed to be known via the robot arm controller. The motion model of the camera incorporates the robot arm dynamics. The resulting model is a linear time-varying one. This model overcomes the common assumption of a constant velocity camera motion between consecutive image frames. The motion of the 3D points in the camera reference frame is maintained by tracking between frames. This is done recursively using the extended Kalman filter (EKF). The 3D motion stereo equations which are derived serve as the measurement model for the corresponding EKF without the need to solve them explicitly. The resulting measurement equations are linear time-varying ones with multiplicative noise. The 3D location of points of the selected object are then updated recursively using the EKF in conjunction with different views of the object. These models are particularly suitable for the EKF implementation. Correspondence between two 2D image points in consecutive frames of the same 3D scene point is constrained by statistical distance produced by the EKF. Simulation results are presented to illustrate the approach.