جستجوی مقالات مرتبط با کلیدواژه « نامساوی ماتریسی خطی » در نشریات گروه « برق »

In this paper, an observer-based controller is introduced for a class of nonlinear fractional order systems. First of all, considering a stable linear fractional order system known as the reference model, the controller is designed so that the closed loop system tracks the states of the reference system. In most systems, some states are unmeasurable or unreachable, so the controller must be designed based on the observer. The observer has been suggested in this research, using the well-known differential mean value theorem approach, converts the nonlinear error dynamics of the observer into linear and parameters-varying, so that the stability analysis is done simply using the Lyapunov quadratic function and linear matrix inequality. The stability analysis of the observer-based controller is also performed using the Lyapunov theorem in the following section. Finally, the efficiency and effectiveness of the proposed controller are shown through the simulation results of two nonlinear fractional order systems.

In this paper, the 2-D modified Fornasini-Marchesini (MFM) state-space model of iterative learning control for uncertain state-delayed systems is introduced. Based on the 2-D model, the problem of designing a robust iterative learning controller is transformed to robust H_inf  stabilization of a 2-D state-delayed system. Dynamic output feedback controller is applied to stabilize the uncertain 2-D model of the process. Linear matrix inequality (LMI) approach is used to find the coefficients of the controller. Finally, two numerical examples are simulated by MATLAB software to compare the proposed method with the classic ILC method and to show the efficiency of the proposed ILC method.

In this paper, the formation tracking of multi-agent systems is discussed. The model considered for each agent is linear with uncertain parameters. The effect of external disturbances is also considered in the model. To achieve predetermined time-varying formation, the required control input is presented. By applying this input, the closed-loop system will take the desired formation. Establishing the appropriate conditions for the realization of time-varying formation, and using the Lyapunov theory and the H_inf index to reduce the disturbance effect, results in some linear matrix inequalities. The designed parameter is then computed by solving those linear matrix inequalities. Finally, a simulation example is presented to illustrate the effectiveness of the designed strategy.

In this paper, a robust model predictive control (MPC) algorithm is designed for nonlinear uncertain systems in presence of the control input constraint. To achieve this goal, first, the additive and polytopic uncertainties are formulated in the nonlinear uncertain system. Then, the control policy is chosen as a state feedback control law in order to minimize a given cost function at each known sample-time. Finally, the robust MPC problem is transformed into another optimization problem subject to some linear matrix inequality (LMI) constraints. The controller gains are determined via the online solution of the proposed minimization problem in real-time. The suggested method is simulated for a second order nonlinear uncertain system. The closed-loop performance is compared to other control techniques. The simulation results show the effectiveness of the proposed algorithm compared to some existing control methods.

In this paper, a dissipativity-based fuzzy integral sliding mode control (FISMC) is proposed for a class of nonlinear systems with matched/unmatched uncertainties and external disturbance and with consideration actuator saturation. The control aim is to design a robust controller such that guarantees the stability of the state variables in the presence of uncertainties and applying the constraint on the control input amplitude, to overcome  the practical limitation. To do this, Takagi-Sugeno fuzzy model is used to represent the nonlinear behavior of a saturated actuator. Ability to appropriate select the maximum control input amplitude is another advantage of the proposed idea .By using Lyapunov theory, stability conditions are derived with a strictly dissipative performance and expressed as linear matrix inequality (LMI) conditions. Finally, simulation results illustrates the effectiveness and priority of the proposed approach.

In this paper, fault-tolerant controller design problem for Lipschitz nonlinear fractional-order systems in presence of sensor fault is considered. By using the descriptor system theory, a correct estimation of the state vector is achieved and then by using it, a stabilizing state feedback controller is designed. By employing some appropriate techniques, parameters design of the observer and controller are stated in terms of linear matrix inequalities, which there exist powerful toolbox for solving them. The proposed observer can estimate the correct value of the sensor fault vector, thus, it can be utilized for the fault detection, isolation, and identification unit.  Besides, the structure of the proposed method is such that the observer and controller design can be performed independently, which facilitate the design process. The effectiveness of the proposed method is shown with numerical simulation results.

This paper deals with the issues of sensor fault detection for a class of Lipschitz uncertain nonlinear system. By definition coordinate transformation matrix for system states and output system, at first the original system divided into two subsystems. the first subsystem includes uncertainties but without any sensor faults and the second subsystem has sensor faults but is free of uncertainties. Then sensor faults in second subsystem are formed as actuator faults. For the aim of fault detection (FD) two sliding mode observer is designed for two subsystems. The necessary matrices and parameters to design observers are obtained by solving linear matrix inequality (LMI) problem.  Stability condition of observer’s dynamics are reviewed based on Lyapunov approach. Finally simulation example is given to illustrate the effectiveness proposed approach.

In this paper, modeling, analyzing and controlling nonlinear systems using Polytopic linear models is considered. First, the output tracking problem is investigated for the state of the system as compared to the affine input, and then the problem is solved for the non-affine state. In the state of determining the parameters of each region to increase the problem solving speed we determine the weighted function in a specific manner that prevents interference between the regions and by solving an linear inequality matrix of controller design , in contrast to the past, it is not necessary to solve a bilinear matrix inequality and only by solving a linear one, the controller will be designed. To stability and design of the controller, a method is used to ensure both the stability of the approximate model (polytopic) and the stability of the main model (nonlinear). Finally, the results are taken and the methods proposed are used to  design of an elastic missile  system.

Takagi and Sugeno have proposed a fuzzy model for a continuous system in which disturbances are also considered as a time-dependent variable function in the consequent part of the fuzzy model rules. To eliminate the disturbances effect on the output of a system with a Takagi and Sugeno fuzzy model, researchers have proposed a linear matrix inequality-based minimization problem in which system parameters are directly present in the problem constraints. In electromechanical systems, the existence of parametric uncertainties such as uncertainties in the moment of inertia matrices and inaccuracy of actuators parameters cannot be neglected in any way. In this paper, at first, by solving an optimization problem, the upper bound of the uncertainties in the system parameters is determined, and the fuzzy model with disturbances is extracted in the presence of uncertainties. In the following, in order to eliminate disturbances in the presence of parametric uncertainties using a stable fuzzy controller based on the parallel distributed compensation, a minimization theorem based on the new obtained fuzzy model is proposed and its validity is proven. Finally, to evaluate the performance of the proposed controller, a boat-mounted camera stabilizer is used as a case study. The simulations results well demonstrate the favorable efficiency of the proposed controller in eliminating disturbances by taking into account the parametric uncertainties.

In electric vehicle’s nonlinear dynamic equations, some parameters has uncertainty such as the coefficient of rolling resistance, drag coefficient, armature resistance and field winding resistance. Design of a controller that is robust in the presence of these parametric uncertainties and also in presence of external disturbances, and on the other hand simultaneously satisfies the optimality criteria, is a challenging issue. In practical applications, in addition to the above problem, the computational load of the control input should also be considered and provide a rational interaction between the controller's desirable performance and the calculations volume. In the present paper, a robust optimal stable fuzzy controller based on the parallel distributed compensation is designed, using Takagi-Sugeno fuzzy model of electric vehicle. The stabilizer feedback gains of fuzzy model, the upper bound of the uncertainties, the upper bound of the disturbances effect, and the upper bound of the cost function are obtained completely offline, through the solving of a minimization problem based on the linear matrix inequality. Therefore, the calculation volume of the control input is extremely low. This allows the practical implementation of the proposed controller. The favorable performance of the proposed controller is demonstrated in five-step simulations.

This paper proposes a sliding mode observer (SMO) for actuator fault reconstruction of nonlinear systems subjected to external disturbance. In the proposed approach, first, the nonlinear system is modelled by a Takagi-Sugeno fuzzy model with immeasurable premise variables. Then, SMO is used to estimate the states and fault. Finally, by using a non-quadratic Lyapunov function (NQLF), the stability of the error system is proved. By considering  performance criteria, the effect of the exogenous disturbance on the state estimations is minimized which provides effective fault estimation. Furthermore, the states and fault estimations asymptotically converge to their actual values for the non-perturbed systems. In the stability analysis and the observer gains design, some change of coordinates are proposed which the transformation matrix of one of them is obtained by solving linear matrix inequalities (LMIs). The proposed approach has some superiority over the existing methods. First, employing the NQLF leads to more relaxed results and better estimation performance. Second, using SMO for fault reconstruction makes the proposed approach insensitive to the uncertainties and unknown inputs and besides detecting the fault, its shape and size are determined. Third, since the premise variables are assumed to be unmeasurable, the presented approach is applicable for a wide class of nonlinear systems. Finally, a continuous stirred tank reactor (CSTR) process is considered and numerical simulation is carried out to illustrate the effectiveness and the accuracy of the proposed approach comparison with the recently published methods.

This paper investigates asynchronous controller design problem for a class of continuous-time Markov jump linear systems. The mentioed asynchronous phenomenon is a case in which the system and the controller Markov chainsare not matched, however they are relevant according to certain probabilities. This phenomenon describes a realistic and practical situation which arises as a result of inaccurate observation of the system’s Markov chain. The proposed design scheme considers the closed-loop system as a unified Markov jump linear system and utulizes the multiple Lyapunov function approach. By this approach, firstly, the stabilizability of the closed-loop system is ensured and then the asynchronous state-feedback controller is synthesizesed. The designed controller is formulated in terms of linear matrix inequalities; which are easy to check. A numerical example illustrates the usefulness of the developed method.

In this paper, we propose a new method for observer-based controller design of nonlinear systems which are represented by Takagi-Sugeno (T-S) fuzzy systems. Two practical restrictions have been considered to cover a more general problem. First, we suppose that the premise variables of the T-S model are unmeasurable, which permits one to utilize the proposed method in more practical systems. Second, actuator saturation is considered as a physical limitation and the controller is designed subject to this restriction. Sufficient conditions for the existence of such a controller are derived in terms of ­linear matrix inequalities (LMIs). The effectiveness of developed technique is shown through numerical example

 In this paper, we consider the problem of observer-based controller design for nonlinear systems which can be represented by Takagi-Sugeno (T-S) fuzzy systems. Two practical restrictions have been considered to cover a more general problem. First, we suppose that the premise variables of the T-S model are unmeasurable, which permits one to utilize the proposed method in more practical systems. Second, actuator saturation is considered as a physical limitation and the controller is designed subject to this restriction. Sufficient conditions for the existence of such a controller are derived in terms of linear matrix inequalities (LMIs). The effectiveness of developed technique is shown through a numerical example.

In this paper, the uncertain H fault tolerant control approach is investigated for interconnected nonlinear systems with time-varying delays in interconnections based on observers. The time varying delay interactions are considered in both the state and the observation output. Faults invoked in this paper are actuator ones which are modeled as both the loss of effectiveness and lock in place. The considered actuator failure can cover most failures that may occur in actuators of the systems. On the basis of fault estimation information, an observer-based memoryless fault-tolerant controller is designed to guarantee the stability of the closedloop time delay system in terms of linear matrix inequalities (LMIs) via the Lyapunov–Krasovskii approach. Two machine subsystems are considered as numerical examples which consisted of highly nonlinear interconnections. These subsystems are employed to verify the validity and the effectiveness of the obtained results in the case of actuator failures

Radome causes refraction of the incoming rardar wave in radar-guided interceptors, thus having a destabilizing effect on the guidance loop, especially at high altitudes. Therefore, a compensator is required to maintain the stability of the guidance loop and causes minimum miss distance in the presence of radome error. From the control perspective, Radome causes an unwanted feedback that is not similar to the conventional feedback loops, in which output must follow a desired control signal. In this paper, the desired closed-loop response is determined first, then a novel approach is proposed to shape the frequency response of the feedforward path so that the stability and performance requirements are satisfied . Frequency response is shaped by linear matrix inequality (LMI) tools and v-gap metric is used to select the best frequency response. Simulation results show that the designed compensator drastically decreases the miss distance, while the stability is guaranteed.

This paper presents a novel delay-dependent consensus algorithm within the linear matrix inequality (LMI) framework to solve consensus problem of linear multi-agent systems with time-varying communication and input delays using fixed-order dynamic output feedback controller. The proposed scheme is decentralized in the sense that each agent updates its state according to the output information of itself and its neighbors. To guarantee consensus in this method, first based on graph theory and by proper system transformations, the consensus problem is converted to the stability problem of an equivalent state-delayed linear system. Then, by considering a suitable Lyapunov-Krasovskii function and applying special conditions on symmetric positive definite matrices, new delay-dependent consensus criteria in LMI form and the unknown controller coefficients are obtained for the system under fixed interconnection topology which can be easily solved by various effective optimization algorithms. As a main feature of the proposed approach, the order of decentralized controllers can be chosen arbitrarily according to the system conditions and limitations. Finally, a numerical example is presented to show the applicability and effectiveness of the proposed method.

This paper presents AQM design to improve the performance of networked control systems. An analytical model of the transmission control protocol (TCP) network is considered and a PID-type state feedback controller is developed to regulate the queue length. This leads to keeping network induced delay and its variation to be small enough to improve the overall performance of the NCSs. The model is assumed to possess structured uncertainties due to the stochastic nature of the network. By augmenting the TCP model with the plant equations، the closed loop system is transformed to a state-dependent delay differential equation (SDDDE). Applying the Lyapunov-Krasovskii method، a sufficient condition for the stability of this class of systems is obtained in terms of linear matrix inequality (LMI). PID controller parameters will be determined by solving these LMIs. Simulation results are presented and it has been shown that maximum allowable bound of time delay will increase in the proposed method.

In this paper, a robust model predictive control (RMPC) is proposed based on model reference adaptive system (MRAS). In this algorithm, using the MRAS a combinational RMPC controller, which is called CRMPC, for three degree freedom satellite is designed such that the effect of moment of inertia uncertainty and external disturbance is compensated on the stability and performance of closed loop system. Control law is a state feedback which its gain is obtained by solving a convex optimization problem subject to several linear matrix inequalities (LMIs). To avoid the actuators saturation a linear matrix inequality is incorporated as LMI in the mentioned optimization problem. The advantages of this algorithm are needless to exact information from system’s model, robustness against model uncertainties and external disturbance. The proposed algorithm is implemented on the satellite attitude control system and the results of them are compared to generalized incremental model predictive control (GIPC) algorithm. The results show that the suggestive controller is more robust than the GIPC method.Keywords: Robust model predictive control; Linear matrix inequality; Model reference adaptive system; Satellite attitude control system.