Geun Bum Koo

Decentralized Fuzzy Observer-Based Output-Feedback Control for Nonlinear Large-Scale Systems: An LMI Approach

This paper presents a decentralized fuzzy control problem for asymptotic stabilization of a class of nonlinear large-scale systems that use an observer-based output-feedback scheme. A Takagi-Sugeno (T-S) fuzzy model is adopted for the nonlinear large-scale system, which has unknown interconnection terms, and a fuzzy controller is separately considered for measurable and nonmeasurable premise variable cases. Sufficient conditions are derived for both asymptotic stabilization and optimization of a maximum bound of interconnection and are formulated in terms of linear matrix inequalities. Finally, numerical examples are provided to verify the effectiveness of the proposed techniques.

Decentralized sampled-data H ∞ fuzzy filter for nonlinear large-scale systems

This paper presents a decentralized sampled-data H ∞ fuzzy filter design method for nonlinear large-scale systems which are represented by a Takagi-Sugeno (T-S) fuzzy model. Based on the T-S fuzzy model, the error system between the nonlinear large-scale system and the filter is obtained. The discretization process of the error system is accomplished with the exact discrete-time approach to eliminate the exact-approximate mismatch. By using the discrete-time Lyapunov sense, the sufficient condition of the asymptotic stability for the error system is given and a prescribed level of the H ∞ norm is ensured to guarantee the H ∞ fuzzy filter performance. Finally, numerical examples are given to show the effectiveness of the proposed methods.

Intelligent Digital Redesign for Nonlinear Interconnected Systems using Decentralized Fuzzy Control

In this paper, a novel intelligent digital redesign (IDR) technique is proposed for the nonlinear interconnected systems which can be represented by a Takagi-Sugeno (T-S) fuzzy model. The IDR technique is to convert a pre-designed analog controller into an equivalent digital one. To develop this method, the discretized models of the analog and digital closed-loop system with the decentralized controller are presented, respectively. Using these discretized models, the digital decentralized control gain is obtained to minimize the norm between the state variables of the analog and digital closed-loop systems and stabilize the digital closed-loop system. Its sufficient conditions are derived in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to verify the effectiveness of the proposed technique. (13). Also, the robust IDR problem was studied for non- linear systems with parametric uncertainties (14). However, these papers dealt with the local state-matching without concerning the global one. The global approaches of the IDR problem were presented in (15-17), but they did not consider the interconnection of the nonlinear systems. The decentralized digital controllers for the interconnected systems were proposed in (18-20). But there were no studies about the IDR problem for the nonlinear inter- connected systems. In this paper, we propose a novel IDR method for the nonlinear interconnected systems which can be modelled by T-S fuzzy systems. The discretized models of the analog and digital closed-loop system with the decentralized con- troller are presented, respectively. Based on these discre- tized models, sufficient conditions are achieved for both the stability of the digital closed-loop system and the state- matching condition between analog and digital closed-loop systems. Its constructive conditions are presented in terms of linear matrix inequalities (LMIs). Finally, it shows the validity of the proposed ideas, techniques and procedures, through simple example. This paper is organized as follows: Section 2 describes the discretized models of the T-S fuzzy interconnected systems. The stability and state-matching conditions are proposed with the LMI form in Section 3. In Section 4, simulation example is provided to demonstrate the design procedures. Finally, the conclusions are given in Section 5.

Observer-based sampled-data controller of linear system for the wave energy converter

In this paper, an observer-based sampled-data controller of linear system is proposed for the wave energy converter. Based on the sampled-data observer, the controller is design. In the closed-loop system with controller, it obtains the norm inequality between the continuous-time state variable and the discrete-time one. Using the norm inequality, sufficient condition is derived for the asymptotic stability of the closed-loop system and formulated in terms of linear matrix inequality. Finally, the wave energy converter simulation is provided to verify the effectiveness of the proposed technique.

Decentralized Sampled-Data Fuzzy Observer Design for Nonlinear Interconnected Systems

This paper presents the decentralized sampled-data fuzzy observer design techniques for nonlinear interconnected systems, which are assumed to be composed of fuzzy subsystems and unknown interconnections. To design the decentralized fuzzy observer, the estimation error dynamics is obtained, and the performance function is defined. Based on the estimation error dynamics and the performance function, the continuous-time decentralized fuzzy observer is first proposed to minimize the interconnection bound to attenuation degree ratio. In addition, the decentralized sampled-data fuzzy observers are designed by using the approximate discretization and the exact discrete-time design approaches, respectively. Each proposed observer design technique is formulated into the optimal problems with the linear matrix inequalities. Finally, several simulation examples show the validity and superiority of the proposed observer design techniques by comparing with the conventional techniques.

Decentralised control for large-scale sampled-data systems: digital redesign approach

In this paper, digital redesign (DR) techniques are presented for a decentralised controller of large-scale sampled-data systems. To improve the performance of the previous DR technique, the state-matching error is defined and directly minimised by using the state-matching error cost function. Also, the discretisation error of the interconnection term is eliminated through an exact discrete-time design approach. Sufficient conditions of the proposed DR techniques are obtained in the Lyapunov sense and are converted into optimal problems with linear matrix inequalities. Finally, two numerical examples are provided to demonstrate the performance improvement of the proposed techniques.

Sampled-data H fuzzy filtering for nonlinear systems with missing measurements

In this paper, a sampled-data H fuzzy filtering problem is considered for nonlinear systems with missing measurements. The nonlinear sampled-data system and missing measurements are assumed to be represented by a TakagiSugeno (TS) fuzzy system and an independent, identically distributed Bernoulli random process, respectively. Based on the fuzzy system, the H fuzzy filtering problem is formulated to design the sampled-data fuzzy filter. By using the exponential mean-square stability definition, the stability condition with an H performance is guaranteed for the fuzzy system with the sampled-data fuzzy filter, and its sufficient condition is converted into the linear matrix inequality (LMI) format. Finally, an example is provided to verify the effectiveness of the proposed fuzzy filtering technique.

Robust decentralized control for fuzzy large-scale systems using dynamic output-feedback

This paper presents a robust decentralized controller design problem for guaranteed cost stabilization of a class of fuzzy large-scale systems using dynamic output-feedback scheme. A Takagi-Sugeno fuzzy model is adopted for the large-scale system, which has norm-bounded uncertainties and unknown interconnection satisfying the quadratic inequality. Sufficient conditions are derived for both robust asymptotic stabilization and minimization of a given cost function and formulated in terms of linear matrix inequalities. Finally, a numerical example is provided to verify the effectiveness of the proposed technique.

Digital controller design for fuzzy systems with packet loss: Intelligent digital redesign approach

In this paper, a novel digital controller is proposed for the nonlinear systems with packet loss using the intelligent digital redesign (IDR). For the fuzzy controller, the nonlinear system is represented by a Takagi-Sugeno (T-S) fuzzy model. The IDR technique is to convert a pre-designed analog controller into an equivalent digital controller. For this technique, the discretized models of the analog and digital closed-loop system are presented, respectively. The digital control gain is obtained to minimize the norm error between the state of the analog and digital closed-loop systems and stabilize the digital closed-loop system. Its sufficient conditions are derived in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to verify the effectiveness of the proposed technique.

An improved digital redesign for sampled-data fuzzy control systems

This paper proposes an improved digital redesign (DR) technique for sampled-data fuzzy controllers in nonlinear systems, based on a TakagiSugeno (TS) fuzzy model. To improve the performance of the DR technique, two methodologies are used: a state-matching error cost function, and a continuous-time fuzzy Lyapunov function. Using these two methodologies, a novel DR technique is proposed to guarantee both the stability and state-matching conditions of the sampled-data fuzzy control system. Further, the proposed DR technique is represented as an optimal problem using the linear matrix inequality (LMI) format. Finally, some simulation examples are provided to verify the effectiveness of the proposed technique in comparison with previous techniques.