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.

Research subject: Energy is an integral part of human life and from the beginning of history, mankind is looking for a way to control it to provide the basic needs. One of the available energy sources is fossil energy that has been considered due to the huge amount of energy. Microbial fuel cell is a bioreactor which converts the chemical energy stored in chemical bonds of the organic compounds to electrical energy through the catalytic reactions. Research approach: In this paper, two types of classical PI and MPC controller are used to investigate the voltage control of a two chamber microbial fuel cell using the model which has been presented by Esfandyari et al. [1, 2]. Considering the features of the proposed model, it can be used to optimize and control the MFC in continuous and batch modes. For this purpose, a classical PI controller based on internal model and MPC controller was designed and implemented. Based on the designed controllers, the adjustment of substrate input flow rate was implemented considering the distribution such as substrate input concentration or the uncertainty of the process model parameter such as Ks and rmax. Main results: For to control the output voltage, step change of 5 units was made in setpoint (inlet flowrate to anode chamber). The values of absolute integral error, rise time and overshoot for the classical PI controller were 3. 75, 13. 159, and 0. 091, respectively, while these values were 0. 035, 11. 908, and 0. 142, respectively for the MPC controller. Considering the achieved data, it can be concluded MPC controller performed better than the classic PI controller.

Artificial lifting utilizing electrical submersible pump is widely used to increase oil production from wells. A suitable control system is required in order to increase efficiency of ESP system and to avoid damage to pump and to increase safety of ESP production. Automatic control of ESP unit is not a trivial task due to the high number of parameters and variable involved. Many methods have been proposed for control of ESP lifted wells. Most methods rely on a linear model of ESP lifted well and controller designed based on this linear approximation. Linear model and controllers can fail if the process undergoes large changes in operating conditions or huge disturbances are introduced to process. Other methods solve dynamic equations governing the ESP lifted well operation. Although these methods are highly accurate; however, they are computationally expensive, and they cannot be implemented on conventional control systems. In this paper, a nonlinear dynamic model is developed for ESP lifted well. The model is then utilized inside a Nonlinear Model predictive Control (NMPC) System. The developed model and controller performance is then tested and assessed under various scenarios. The developed controller performance shows proper reference tracking and disturbance rejection properties while the process constraint are completely satisfied.

Designing the suitable autopilot for the quadrotors is very important in how the flight vehicle moves and follows the specified reference path. One of the suitable controllers for autopilot design is the predictive controller, which is the most well-known Generalized predictive controller method. In the autopilot design, constraints on inputs as well as communication delays must be taken into account, and if these two issues are not addressed in the controller design, the autopilot will not function properly and may even lead to instability. In this paper, a generalized predictive controller with consideration for delayed input data for a quadrotor autopilot is presented. Also, in order to determine the predictive control parameters, the Metaheuristic method, particle swarm optimization, has been used and these parameters have been optimally adjusted. In adjusting the controller parameters, the objective function is used based on the performance indicators such as settling time, peak time, overshoot, and steady-state error. By adjusting the weights of this function, the controller performance indicators can be determined. Also, the simulation results show that the controller performance based on the defined objective function is much improved compared to the cost function based on the integral of the error.

The control of fluidized-bed operations processes is still one of the major areas of research due to the complexity of the process and the inherent nonlinearity and varying dynamics involved in its operation. There are varieties of problems in chemical engineering that can be formulated as NonLinear Programming (NLPs).The quality of the developed solution significantly affects the performance of such system. controller design involves tuning the process controllers and implementing them to achieve certain performance of controlled variables by using Sequential Quadratic Programming (SQP) method to tackle the constrained high NLPs problem for modified mathematical model for gas phase olefin polymerization in fluidized-bed catalytic reactor.The objective of this work is to present a comparative study; PID control is compared to an advanced neural network based MPC decentralized controller and also, see the effect of SQP on the performance of controlled variables. The two control approached were evaluated for set point tracking and load rejection properties giving acceptable results.

In this paper, design and implementation of model predictive controller for turbofan engine fuel control are proposed. The satisfaction of operational and structural limits of the engine is a controller design challenge. The control system must ensure that the engine operates without any limit violation at all times, i. e. without over-speed of the shaft, compressor stall, combustion chamber blow out and turbine over-temperature. In this regard, a controller is required which can take into account these constraints while obtaining an optimal control input. Therefore, the model predictive control for turbofan engine fuel control using a linear model of the engine at one operating point is designed. The simulation results show that while model predictive control generate optimal control signal, all the constraints are perfectly satisfied. After assuring the valid performance of the controller in computer simulations, the fuel control algorithm is implemented on a hardware framework. For this purpose, the controller algorithm is implemented on a microcontroller and hardware-in-the-loop test is performed. The results of the hardware-in-the-loop simulation indicate the correct implementation of the model predictive controller on the hardware framework.

In this paper a new sliding mode controller based on predictive control is used for the first order system, which is a good model for the industrial process. In this method a developed predictive control is used to optimize the sliding mode control including sliding surface and switching function coefficient at every moment. A new smooth function is used to reduce the chattering problems. Simulation results show the high effectiveness of the proposed controller.

Magnetic levitation systems (Maglev) are widely used in various industries. Open loop Maglev system is highly nonlinear and unstable. Therefore, designing a simple, but effective controller for such a system is a challenging problem. In this paper, a fractional order predictive Functional controller (FPFC) is proposed for control of Magnetic Levitation system based on its linearized unstable model. At first, the unstable plant is decomposed into two stable models. Then, using these two stable models and employing fractional order cost function, the PFC controller is designed. Because of more degrees of freedom and its flexibility of fractional order calculus, the proposed fractional PFC would improve the performance of closed loop system, noticeably. Robust stability of closed-loop system has been also studied considering the uncertainties and model mismatches via small gain theorem. Simulation results show good performance of the proposed controller in nominal and perturbed conditions. Based on provided simulations, via the proposed controller, overshoot has been omitted and performance indices have been improved more than 50% with respect to first and second order of freedom integer/fractional order PID controllers, designed for this system, in the literature.

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.