One of the issues of reliable performance in the power grid is the existence of electromechanical oscillations between interconnected generators. The number of generators participating in each electromechanical oscillation mode and the frequency oscillation depends on the structure and function of the power grid. In this paper, to improve the transient nature of the network and damping electromechanical fluctuations, a decentralized robust adaptive control method based on dynamic programming has been used to design a stabilizing power system and a complementary static var compensator (SVC) Controller. By applying a single line to ground fault in the network, the robustness of the designed control systems is demonstrated. Also, the simulation results of the method used in this paper are compared with Controllers whose parameters are adjusted using the PSO algorithm. The simulation results show the superiority of the decentralized robust adaptive control method based on dynamic programming for the stabilizing design of the power system and the complementary SVC Controller. The performance of the control method is tested using the IEEE 16-machine, 68-bus, 5-area is verified with time domain simulation.

In this study, a robust H_2/H_∞ multi-objective state-feedback Controller and tracking design are presented for a mobile two-wheeled inverted pendulum (MTWIP). The proposed control has to track the desired angular velocity while keeping the mobile two-wheeled inverted pendulum balanced. First, error of output states are added to the dynamic of system for better tracking control. And uncertainties of parameters are defined by affine parameters. Next, Takagi-Sugeno (T-S) fuzzy model is used for estimating the uncertainty of nonlinear model parameters. Robust H_2/H_∞ Controller is designed and analyzed for each local linear subsystem of mobile two-wheeled inverted pendulum by using a linear matrix inequalities method. To sum up, in order to calculate the whole dynamic of system from each local linear subsystem, weight average defuzzifer method is used and the total Controller is designed and analyzed according to parallel distribute compensation. The simulation indicate that the proposed scheme has high accuracy, robustness, good tracking, fast transient responses and lower control effort for a mobile two-wheeled inverted pendulum despite the uncertainties and external disturbance.

In this paper, a new comparative approach was proposed for reliable Controller design. Scientists and engineers are often confronted with the analysis, design, and synthesis of real-life problems. The first step in such studies is the development of a 'mathematical model' which can be considered as a substitute for the real problem. The mathematical model is used here as a plant. Fractional integrals and derivatives have found wide application in the control of dynamical systems when the controlled system and the Controller are described by a set of fractional order differential equations. Here the stability and robustness of fractional order system is checked at the different level and it is found that the stability region is large in the complex plane. This large stability region provides the more flexibility for system implementation in the control engineering. Generally, an analytically or experimentally approaches are used for designing the Controller. If a fractional order Controller design approach used for a given plant then the controlled parameter gives the better result.

Haptic technology has enormous applications in several fields from medical, military, and in our day-to-day life’ s products including video games, smartphones, and smart cities. The Haptic Interface Controller (HIC), a key circuitry for interaction between the user and the virtual world, has two main control issues: stability and transparency. These two issues are complementary to each other i. e. emphasis on one will degrade the other and vice-versa. To address this, intelligent control techniques including Genetic Algorithm (GA), Feed-Forward Neural Network (FFNN), and Fuzzy Logic Control (FLC) have been used in design of the HIC. To ensure the performance in real-time, in system parametric uncertainty and delay have been added while designing the HIC so that a balance could be maintained between the two issues.

Since the car is working in different climatic conditions, any Changes in temperature of the climatic conditions can affect various parts of the car. So it is necessary to optimize the energy consumption & keep the passengers in a suit & comfort state considering this kind of changes. The automotive air conditioning system is one of the largest ancillary loads in the passenger cars, with considerable effects on the vehicle fuel consumption. While most of the studies have represented linearized equations & simplified steady state matrixes, In the simulations of this paper, Nonlinear dynamic equations with using a model predictive Controller and advanced control theory is designed for automotive air conditioning system. At the same time, a genetic algorithm technic which is a method of generation evaluation, is finding out the best answer in the whole problem space using a fitness function to select optimized inputs for sending to the plant. Also in this system a compressor capacity & fan flow rate has considered as two inputs to achieve the main objectives like minimizing the steady state error, system fast response, reduction of noise effects, minimizing of energy consumption and comforter of passengers.

This paper presents a new robust adaptive inverse control approach for a force-reflecting teleportation system with varying time delay. In this approach, using the Smith predictor idea, an impedance Controller and an adaptive inverse Controller are designed, respectively, for the master and slave robots such that the stability and performance of the closed-loop system are achieved in the presence of communication channels varying time delay. Also, based on robust control theory, two sufficient conditions for the stability of overall system are derived. The time domain desired specifications are contained in the design problem using the standard characteristic polynomials. Also, the proposed approach is compared with the sliding mode control. The simulation results show the proposed approach successfully compensates the position drift although time delay is randomly varying.

In this paper, Teaching-Learning-Based Optimization (TLBO) algorithm is employed for controlling the speed of induction motors using fuzzy sliding mode Controller. The proposed control scheme formulates the design of the Controller as an optimization problem. First, a sliding mode speed Controller with an integral switching surface is designed, in which the acceleration information for speed control is not required. In this case, the upper bound of the lumped uncertainties including the parameter uncertainties and the load disturbance must be available. The importance of this parameter on the system performance is illustrated. Then, the fuzzy sliding mode speed Controller is designed to estimate the upper bound of the lumped uncertainties. Finally, TLBO algorithm is adopted to determine the optimal upper bound of the uncertainty. Simulation results are given to demonstrate the superiority of the proposed Controller in comparison with the proportional-integrator, traditional sliding mode Controller, fuzzy sliding mode Controller and adaptive fuzzy sliding mode Controller.

The body freedom flutter phenomenon is one of the aeroelastic instabilities that occurs due to the coupling of the aeroelastic bending mode of the wing with the short-period mode in the flight dynamics of the aircraft. By using the aeroservoelastic model and applying closed loop control, this phenomenon can be suppressed in the operating conditions of the aircraft and the velocity of this event can be increased. The simplest model aircraft capable of displaying this instability includes the flexible wing and the planar flight dynamics model. For this purpose, the wing structure is modeled using the Euler-Bernoulli beam and, the theory of minimum variable state is used to model unstable aerodynamics to make the conditions suitable for modeling the system in state space. In the control section, the elevator is used as the control surface and LQR theory with Kalman filter is used to body freedom flutter suppression. Finally, the effect of adding a closed loop control to increase the body freedom flutter velocity and the limitations of this work are studied.

Background and Objectives: Regulation of protein expression in cellular level are so challenging. In cellular scale, biochemical processes are intrinsically noisy and many convenient Controllers aren’ t physically implementable. Methods: In this paper, we consider standard Lyapunov function and by using Ito formula and stochastic analysis, we derive sufficient conditions for noise to state stability presented in the form of matrix inequalities. In the next step, by defining appropriate change of variables, matrix inequalities are transformed to Linear matrix inequalities which can be used to synthesize Controller with the desired structure. Results: This paper deals with the design of implementable Controller for stochastic gene regulatory networks with multiplicative and additive noises. In particular, we consider structural limitations that are present in real cellular systems and design the decentralized feedback that guarantees noise to state stability. Since the proposed conditions for Controller design are in the form of linear matrix inequalities, Controller gains can be derived efficiently through solving presented LMIs numerically. It is noteworthy that Because of its simple structure, the proposed Controller can be implemented universally in many cells. Moreover, we consider a synthetic gene regulatory networks and investigate the effectiveness of the proposed Controller by simulations. Conclusion: Our results provide a new method for designing Decentralized Controller in gene regulatory networks with intrinsic and extrinsic noises. the proposed Controller can be easily implemented in cellular environment.

In this paper, we try to use modeling based on singular perturbation theory, in order to control satellite attitude during the wide rolling angle maneuvering through nonlinear H∞ control strategy. Differential equations describing dynamics of the satellite are presented first, and by choosing the appropriate dynamic model for actuators and based on the standard singular perturbation model, the closed-loop system is created. Next, this model is put into the appropriate form to solve H∞ problem. Then, after solving the HJI equation, the control law is determined. Simulation results for a nominal satellite control based on our approach are finally presented.