The optimal Bayesian filter for a single target is known to provide the best tracking performance in a cluttered environment. However, its main drawback is the increase in memory size and computation quantity over time. In this paper, the inevitable predicament of the optimal Bayesian filter is resolved in a suboptimal fashion through the use of a receding horizon strategy. As a result, the problems of memory and computational requirements are diminished. As a priori information, the horizon initial state is estimated from the validated measurements on the receding horizon. Consequently, the suboptimal algorithm proposed allows for real time implementation.

In this paper, we investigate a robust $H_{\infty}$ state feedback control technique for continuous time bilinear systems with an additive disturbance input. The nonlinear robust $H_{\infty}$control for bilinear systems requires a solution to the state dependent algebraic Riccati equation (SDARE). We present a new robust $H_{\infty}$control technique based on the successive approximation method for solving the SDARE by converting bilinear systems into time-varying linear systems. The proposed control method guarantees robust stability for closed loop bilinear systems. The proposed algorithm is verified by numerical examples.

This work is a modified version of an earlier work that was based on ellipsoidal type feasible sets. Unlike the earlier work, polyhedral types of invariant and feasible sets are adopted to deal with input constraints. The use of polyhedral sets enables the formulation of on-line algorithm in terms of QP (Quadratic Programming), which can be solved more efficiently than semi-def algorithms. A simple numerical example shows that the proposed method yields larger stabilizable sets with greater bounds on disturbances than is the case in the earlier approach.

Biaffine Matrix Inequality (BMI) is known to provide the most general framework in control synthesis, but problems involving BMI's are very difficult to solve because nonconvex optimization should be solved. In the previous paper, we proposed a new solver for problems involving BMI's using Evolutionary Algorithms (EA). In this paper, we solve several control synthesis examples such as Reduced-order control, Simultaneous stabilization, Multi-objective control, $H_{\infty}$ optimal control, Maxed $H_2$ / $H_{\infty}$control design, and Robust $H_{\infty}$ control. Each of these problems is formulated as the standard BMI form, and solved by the proposed algorithm. The performance in each case is compared with those of conventional methods.

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

A false signal problem of the bleed air leak detection system in a widely used modem airplane is studied in this paper. It is considered to be a problem of stability of the controller unit. An equivalent circuit is extracted from the study. The circuit is considered to be an alternative bridge. A mathematical model is derived to describe the stability of the circuit. The conclusion is drawn that the sending of the false signal is relevant to not only the sensors, but also the detector. If a parameter in the detector is readjusted, then the problem may be avoided.

An $H_2$/$H_{\infty}$ -controller realization is carried out by considering an entropy integral. Using J-spectral factorization, the parametrizations of all $H_{\infty}$ stabilizing controllers are derived. By the relation of a mixed $H_2$/$H_{\infty}$ control problem and a minimum entropy/$H_{\infty}$ control problem, the mixed $H_2$/$H_{\infty}$-controller state-space realization is presented.

This paper presents a residence time distribution (RTD) measurement method using a radioisotope tracer and the estimation method of RTD model parameters to analyze a submerged bio-reactor. The mathematical RTD models have been investigated to represent the flow behavior and the existence of stagnant regions in the reactor. Knowing the parameters of the RTD model is important for understanding the mixing characteristics of a reactor The radioisotope tracer experiment was carried out by injecting a radioisotope tracer as a pulse into the inlet of the reactor and recording the change of its concentration at the outlet of the reactor to obtain the experimental RTD response. The parameter estimation was performed by the Levenberg-Marquardt optimization algorithm. The proposed scheme allowed the parameter estimation of RTD model suggested by Adler-Hovorka with very low deviations. The estimation procedure is shown to lead to accurate estimation of the RTD parameters and to a good agreement between experimental and simulated response.

The education system in this paper is used to demonstrate and educate the effects of electromagnetic induction. Placing an aluminum ring over the core and switching on AC source causes the ring to jump in the air due to induced currents in the ring producing a magnetic field opposed to that produced in the core. To control the position of the ring by only the current, it is to require nonlinear control algorithm and control board that is composed of photo sensors, decode circuit, computer communication, and power electronics circuit. This paper provides the development for education system in detail and the effects of dynamic neural networks for nonlinear control with on line is studied.

A tool which can be used for the analysis of a robot soccer game is presented. The tool enables automatic filtering and selection of game sequences which are suitable for the analysis of the game. Fuzzy logic is used since the data gathered by a camera is highly noisy. The data used in the paper was recorded during the game Germany - Slovenia in Hagen, on November 11, 2001. The dynamic parameters of our robots are estimated using the least squares technique. Meandering parameters are estimated and an attempt is made to identify the strategy of the opposing team with the method of introspection.

We designed a family of completely autonomous mobile robots with local intelligence. Each robot has a number of on-board sensors, including vision, and does not rely on global positioning systems The on-board embedded controller is sufficient to analyze several low-resolution color images per second. This enables our robots to perform several complex tasks such as navigation, map generation, or providing intelligent group behavior. Not being limited to playing the game of soccer and being completely autonomous, we are also looking at a number of other interesting scenarios. The robots can communicate with each other, e.g. for exchanging positions, information about objects or just the local states they are currently in (e.g. sharing their current objectives with other robots in the group). We are particularly interested in the differences between a behavior-based approach versus a traditional control algorithm at this still very low level of action.

This paper presents a cooperation strategy between a human operator and autonomous robots for an interactive robot soccer game, The interactive robot soccer game has been developed to allow humans to join into the game dynamically and reinforce entertainment characteristics. In order to make these games more interesting, a cooperation strategy between humans and autonomous robots on a team is very important. Strategies can be pre-programmed or learned by robots themselves with learning or evolving algorithms. Since the robot soccer system is hard to model and its environment changes dynamically, it is very difficult to pre-program cooperation strategies between robot agents. Q-learning - one of the most representative reinforcement learning methods - is shown to be effective for solving problems dynamically without explicit knowledge of the system. Therefore, in our research, a Q-learning based learning method has been utilized. Prior to utilizing Q-teaming, state variables describing the game situation and actions' sets of robots have been defined. After the learning process, the human operator could play the game more easily. To evaluate the usefulness of the proposed strategy, some simulations and games have been carried out.

In this paper, a new model for mobile soccer robot, a type of linear system, is presented. A controller, consisting of two loops the one of which is the inner state feedback loop designed for stability and plant be well conditioned and the outer loop is a well-known PI controller designed for tracking the reference input, is suggested. Because the plant, the soccer robot, is parameter dependent, it requires the controller to be insensitive to the parameter variation. To achieve this objective, the pole-sensitivity as a pole-variation with respect to the parameter variation is defined and design algorithms for state-feedback controllers are suggested, consisting of two matrices one of which is for general pole-placement and other for parameter insensitive. This paper shows that the PI controller is equivalent to the state feedback and the cost function for reference tracking is equivalent to the LQ cost. By using these properties, we suggest a tuning procedure for the PI controller. We that the control algorithm in this paper, based on the linear system theory, is well work by simulation, and the LPD system modeling and control are more easy treatment for soccer robot.

A biped robot should be designed to be an effective mechanical structure and have smaller hardware system if it is to be a stand-alone structure. This paper shows the design methodology of a biped robot controller using FPGA(Field Programmable Gate Array). A hardware system consists of DSP(Digital Signal Processor) as the main CPU, and FPGA as the motor controller. By using FPGA, more flexible hardware system has been achieved, and more compact and simple controller has been designed.

In this study, we investigated the modeling and design of an Intelligent Agent System (IAS). To achieve this goal, we introduced several kinds of agents that exhibit intelligent features. These are the main agent, management agent, watcher agent, report agent and application agent. We applied the intelligent agent concept to two different application fields, i.e. the intelligent agent system for pattern classification and the intelligent agent system for bank asset management modeling.