The Modares Journal of Electrical Engineering
Volume:16 Issue: 1, 2016

In this paper, tracking control synthesis problem for nonlinear polynomial discrete-time systems are studied. Proposed controller drives the plant such that the state vector of the plant follows those of a stable reference model. The objective is to design a controller such that the energy gains from the exogenous signals that are the reference signal and the state vector of the reference model, to the tracking error to be less or equal to prescribe thresholds. The main difficulty in the problem of designing tracking nonlinear discrete-time control law for the polynomial discrete time systems is that in general this problem may not be formulated as a convex problem. With proper selection of Lyapunov function and based on Lyapunov theory and by using sum of square approach, sufficient conditions for existence of controller are presented in terms of a feasibility SOS programming problem that can be solved using numerical solvers such as SOSTOOLS. Finally, the performance of proposed approach will be shown using the simulation of several examples.

Air conditioning system (A/C) of the car imposes an additional load on the engine, increasing fuel consumption and losses. Therefore, any improvement in its performance has a direct impact on vehicle performance and fuel consumption. The automotive A/C system is a Multi Input- Multi Output (MIMO) plant and There are constraints on its variables So the method of Model Predictive Control (MPC) as an effective method. So far the MPC method is implemented largely for this system. In this paper a predictive control method based on orthonormal functions is provided for automotive air conditioning system. System's model has been changed with an embedded integrator, inputs and outputs changes are highly penalized in cost function and Laguerre orthonormal basis functions are added in MPC's structure and it will be shown that in the proposed control method compared to the conventional MPC method, the automotive air conditioning system performance has been improved and because of reduced computational load the runtime of simulations implementation has been reduced.

In this paper, the stability problem of nonlinear first order hyperbolic partial differential equations (PDE) systems is investigated. Based on Lyapunov stability theorem, the sufficient conditions to guarantee the stability of Takagi-Sugeno (TS) fuzzy hyperbolic PDE model are achieved in terms of spatially varying linear matrix inequalities (SVLMI). To investigate the exponentially stabilization of nonlinear first order hyperbolic PDE systems, a fuzzy Lyapunov function is considered. Then, some new space varying slack matrices are introduced to conduct the stability analysis. The proposed stability conditions are more relaxed than the newly published one. Furthermore, the problem of applying some constraints on control input is studied through this paper. Hence, the performance of the controller is improved in the proposed approach. Finally, in order to evaluate the validity of the proposed approach, a practical application of nonisothermal plug flow reactor (PFR) is considered.

High blood glucose levels in the body named diabetes can increase damage in kidneys, eyes, heart and etc. In this investigation, a novel TS fuzzy static output feedback control structure is proposed to regulate the blood glucose level in the pre-defined desired values for type 1 diabetes using exogenous intra-venous insulin delivery rate. To this end, a nonlinear delay differential equation framework is considered to model the blood glucose/insulin endocrine metabolic regulatory system. The governing equations of the blood glucose/insulin model are approximated by a TS fuzzy model and then the proposed static output feedback controller is designed for this TS model.

One of the most important cases in the power distribution system is to prepare a good quality of the electrical power for consumers, in general. A number of different methods to reach the better quality of the electricity are investigated in academic and industrial environments. In the distribution networks, active filters have the merit to prevent the harmonies, existed in the electrical voltage and corresponding current signals. In a word, the research proposed here is to guarantee the better quality of three-phase active power through fuzzy-based approach that is being used in the designing process of the active filter in controlling procedures. The results of the simulation programs confirm the desirable performance of the proposed control approach, obviously.

This paper presents a new scheme based on state estimation to diagnosis an actuator or plant fault in a class of nonlinear systems that represent the nonlinear dynamic model of gas turbine engine. An optimal nonlinear observer is designed for the nonlinear system. By utilizing Lyapunov's direct method, the observer is proved to be optimal with respect to a performance function, including the magnitude of the observer gain and the convergence time. The observer gain is obtained by using approximation of Hamilton -Jacobi -Bellman (HJB) equation. The approximation is determined via an online trained neural network (NN). Using the proposed observer, the system states and the fault signal can be estimated and diagnosed, respectively. The proposed approach is implemented for state estimation and fault detection of a gas turbine model subject to compressor mass flow fault. The simulation results illustrate that the proposed fault detection scheme is a promising tool for the gas turbine diagnostics.