Magdi S. Mahmoud

State estimation with asynchronous multi-rate multi-smart sensors

In asynchronous multi-sensor systems, data missing and delays are likely to occur when performing state estimation at different rates. In this paper, an algorithm to process both the delayed and missing measurements is developed, under the assumption that the data arrives between two consecutive sampling times. Local estimation is then implemented using a fusion algorithm. This study extends the currently available state estimation for asynchronous multi-rate multi-sensor that only considers random measurements missing with the ability to remove delay effect. Simulation experiments are performed to illustrate the effectiveness of the developed theory.

New Predictive Control Scheme for Networked Control Systems

This paper is concerned with the design of networked control systems with random network-induced delay and data dropout. It presents a new control scheme, which is termed networked predictive control with optimal estimation. Based on Multirate Kalman Filtering, the measured data which are out of sequence or delayed can be used to improve the precision of estimation. The control prediction generator provides a set of future control predictions to make the closed-loop system achieve the desired control performance and the compensator removes the effects of the network transmission with time delay and data dropout. Simulation results are presented to illustrate the effectiveness of the control strategy via comparing with control schemes without any compensation for the network.

Robust Quantized Approach to Fuzzy Networked Control Systems

This paper investigates the problem of robust H∞ control for uncertain discrete-time Takagi-Sugeno (T-S) fuzzy networked control systems (NCSs) with state quantization. A new model of network-based control with simultaneous consideration of network induced delays and packet dropouts is proposed. Using fuzzy Lyapunov-Krasovskii functional, we derive a less conservative delay-dependent stability condition for the closed NCSs. Robust H∞ fuzzy controller is developed for the asymptotic stabilization of the NCSs and expressed in linear matrix inequality-based conditions. Numerical simulation examples show the feasibility applications of the developed technique.

Reliable decentralized control of interconnected discrete delay systems

Abstract In this paper, we study the problem of designing decentralized reliable state-feedback controllers under a class of actuator failures for a class of linear interconnected discrete-time systems having subsystem and coupling time delays. The failures take into consideration possible outages or partial failures in every single actuator. A decentralized stabilizing reliable feedback controller is derived at the subsystem level to give the closed-loop subsystem delay-dependent robust internal stability with a γ -level l 2 -gain. The results developed are tested on a representative water quality control example.

Improved digital tracking controller design for pilot-scale unmanned helicopter

Abstract In this paper, methods for improved design of digital tracking controller for a pilot-scale unmanned helicopter are considered. By discretizing the linearized helicopter model, the linear quadratic with integral (LQI) capability is investigated and applied in order to develop an efficient tracking system including a state-feedback plus integral action. The helicopter velocities are used to formulate a prescribed position reference tracking trajectory. When both process and measurement noises are present, a Kalman filter (KF) is combined with the LQI to form a linear quadratic Gaussian with integral (LQGI) tracking system. Simulation studies illuminate both the capability of the controller design and the accuracy of the estimator. Next, H2, H ∞ and mixed H 2 / H ∞ controls are designed and the results between methods are produced and compared.

Stability of Discrete Recurrent Neural Networks with Interval Delays: Global Results

A global exponential stability method for a class of discrete time recurrent neural networks with interval time-varying delays and norm-bounded time-varying parameter uncertainties is developed in this paper. The method is derived based on a new Lyapunov-Krasovskii functional to exhibit the delay-range-dependent dynamics and to compensate for the enlarged time-span. In addition, it eliminates the need for over bounding and utilizes smaller number of LMI decision variables. Effective solutions to the global stability problem are provided in terms of feasibility-testing of parameterized linear matrix inequalities LMIs. Numerical examples are presented to demonstrate the potential of the developed technique.

Model identification and analysis of small-power wind turbines

In this paper, we present complete results for model identification methods and analysis of a small-power wind turbine in the prospect of designing efficient controllers for obtaining maximum electrical power output and devising the fault detection and diagnosis schemes. The system has been identified using three different model structures: ARX, ARMAX and state-space models. The techniques used for their estimation are least-squares, prediction-error and subspace-based N4SID methods, respectively. Identification and validations are performed on actual measurements of a wind turbine installed at West Michigan University (WMU). It is concluded that the identified ARX model gives the best results in terms of minimum value of Akaike’s information criterion (AIC) and maximum percentage of fitness when validation tests are performed.

Fuzzy Networked Control Systems

This chapter provides fuzzy feedback design methods for classes of networked control systems (NCS). The chapter is divided into three parts: n n1. n nThe fuzzy control designs using state feedback and observer-based feedback for linear systems connected over a common digital communication network are addressed. The network conditions including network-induced delays, data packet dropouts, and limited communication capacity due to signal quantization are taken into consideration. n n n n n2. n nThe problem of robust ({mathscr {H}}_infty ) control for uncertain discrete-time fuzzy NCS with state quantization is studied with simultaneous consideration of network-induced delays and packet dropouts. Delay-dependent closed-loop stability condition are established. Robust ({mathscr {H}}_infty ) fuzzy controller is developed to guarantee the asymptotic stabilization of the NCS. n n n n n3. n nIncorporating random measurement, actuation delays, quantization and random packet dropout into the feedback fuzzy control design for NCS is treated in the third part. The main design focus is on the observer-based output feedback fuzzy controller design. Sufficient conditions for exponential stability of the resulting closed-loop fuzzy system re established.

Fuzzy Stabilization Approaches

This chapter presents, in the first section, a generalized approach to state feedback stabilization of interconnected fuzzy systems. A convex optimization algorithm provides a decentralized solution to the problem of asymptotic stability with strict dissipativity. It is established that the new methodology can reproduce earlier results on passivity, positive realness, and disturbance attenuation. The next section discusses the stabilization of uncertain T–S fuzzy systems with bounded and time-varying input delay using state and observer-based feedback schemes. Extension to T–S fuzzy input delay systems is presented and a separate design principle is developed. Following which a class of fuzzy large-scale nonlinear discrete systems with local quantizers is examined. This fuzzy system has unknown-but-bounded couplings and delays. Of interest are the analysis and design related to decentralized fuzzy feedback structure with ({mathscr {H}}_infty ) measure. It is established that the resulting closed-loop fuzzy system exhibits delay-dependent asymptotic stability with disturbance attenuation level. Using quantized output measurements, a local procedure is constructed for tuning the quantizer parameters to achieve similar asymptotic stability and guaranteed performance. The final section investigates new criteria of the robust ({mathscr {H}}_infty ) stability of uncertain stochastic fuzzy mixed delay systems with nonlinear noise disturbances by employing an improved free-weighting matrix approach. Both the parameter uncertainties and Brownian motion stochastic disturbances are considered. In terms of a stochastic fuzzy Lyapunov functional, a sufficient criterion is proposed to investigate dynamical behaviors of the system in the mean square sense with an ({mathscr {H}}_infty ) performance index.

Basics of Fuzzy Control

This chapter investigates the problem of fuzzy control design methods. We keep in mind the simple statement, ‘Fuzzy logic’ means approximate reasoning, information granulation, computing with words, and so on. First we provide a tutorial introduction to the ingredients of fuzzy logic-based systems. We show the properties and features of classical versus fuzzy sets and demonstrate the process of fuzzification by several examples. Next we consider fuzzy rule-based systems and pay particular attention to comparative analysis with conventional approaches. After illustrating the different methods of defuzzification, we shed lights on fuzzy inference systems of Mamdani, Tsukamoto, and Sugeno. Finally, we show how to deal with a variety of basic fuzzy control systems.