Journal of Control
Year:2022 | Volume:15 | Issue:4|Papers Count:7

In this paper, first, using a test set on a turbocharged diesel engine in the engine test room, a nonlinear model is obtained and it is linearized around several operation points. Then, for the linear models, a linear quadratic integral Gaussian controller (LQIG) with variable parameters as function of engine speed is designed and simulated. In the proposed method, by controlling the wastegate, the boost produced by the turbocharger can be controlled at all engine speeds (especially the lower and middle speeds) with high performance response, thereby improving the output power and engine torque in all speeds. The closed loop control system is simulated and evaluated in the presence of noise and model uncertainty. The simulation results show the better performance and higher robustness in comparison with PID and LQR controllers.

In this paper, a model-free reinforcement learning-based controller is designed to extract a treatment protocol because the design of a model-based controller is complex due to the highly nonlinear dynamics of cancer. The Q-learning algorithm is used to develop an optimal controller for cancer chemotherapy drug dosing. In the Q-learning algorithm, each entry of the Q-table is updated using data from states, action, and reward. The action is the chemo-drug dose. The proposed controller is implemented on a four states mathematical model including immune cells, tumor cells, healthy cells, and chemo-drug concentration in the bloodstream. Three different treatment strategies are proposed for three young, old, and pregnant patients considering his/her age. Chemotherapy is used in all cases. In the older patient, immunotherapy is also used for modifying the dynamics of cancer by reinforcing his/her weak immune system. A Mamdani fuzzy inference system is designed to limit the maximum chemo-drug dose by regarding the age of the patients. Simulation results show the effectiveness of the proposed treatment strategy. It is also shown that immunotherapy is necessary for finite duration cancer treatment in patients with a weak immune system. The used strategy is a model-free method which is the main advantage of this method.

In this paper, the design of a distributed adaptive controller for a class of unknown non-affine MIMO strict-feedback multi agent systems with time delay has been performed under a directed graph. The controller design is based on dynamic surface control method. In the design process, radial basis function neural networks (RBFNNs) were employed to approximate the unknown nonlinear functions. Stability analysis was performed using the Lyapunov-Krasovskii function and it was proved that all the signals of the closed-loop system are semi-globally uniformly bounded. Finally, the simulation results also confirmed the performance of the proposed control method.

Power decoupling of pulsating grid side power from constant source side power is one of the paramount issues in single phase-phase grid-connected renewable systems. The principal aim of such systems is the decrease of the capacitance of the decoupled capacitor. However, this causes some problems such as an increase in the total harmonic distortion (THD) of injected current to the grid and bus voltage fluctuations. By considering the aforementioned explanation, utilizing control strategies is critical to modify system performance. The voltage controller is responsible for adjusting the bus voltage, suppressing the second-order harmonic, and providing a trade-off between system cost and total harmonic distortion as well as bus voltage fluctuations. This paper presents a digital notch filter in order to improve the system transient response and remove the harmonic ripples. The relations between system cost, THD, and bus voltage fluctuations have been described completely. Moreover, the mathematical expressions of the proposed digital notch filter have been provided. The analyzed system is 250 W PV panel connected to a 220 V RMS, 50 HZ grid. The THD of injected current to the grid is approximately 0. 6%.

In this paper, an adaptive fault tolerant nonlinear control is proposed for attitude tracking problem of satellite with three magnetorquers and one reaction wheel in the presence of inertia uncertainties, external disturbances, and actuator faults. Firstly, sliding surface variable is chosen based on avoiding the singularity of control signal and guaranteeing the convergence of attitude tracking error to zero in a finite-time. Subsequently, modified non-singular fast terminal sliding mode is designed as fault tolerant control approach. Then, the control gain of reaching law is adaptively designed to improve the performance of proposed controller. The adaptive control gain is designed independent of the upper and lower bounds of the actuator effectiveness factors. Stability proof is performed by Lyapunov function candidate to show that attitude tracking errors and angular velocities are converged to zero. To evaluate the performance of proposed method, simulation results are compared with their non-adaptive version. Outcomes show better performance of the proposed controller in tracking the desired attitude, a significant reduction in convergence time, and reduction in the chattering of control torque.

The wind is one of the most important and affecting phenomena and is known as one of the significant clean resources of energy. Apart from other atmospheric parameters, the wind has complex behavior and intermittent characteristics. Local phenomena can be accompanied by the wind, which is strong, non-predicted, and damaging. Weather radars are capable of detecting and displaying storm-related turbulence as well as precipitation in a relatively wide area. This capability can improve the quality of the wind forecast. In this paper, a method is presented and implemented to forecast the probability of strong wind in the next five hours based on the Hidden Markov Model (HMM). The method is expanded to find out the forecast of wind turbine output power and reliability as well. Achieved results show that about 67% of strong winds are correctly forecasted

Fault detection has always been important in aviation systems to prevent many accidents. This process is possible in different ways. In this paper, we first identify the longitudinal axis plane model using neural network approach. Then based on the obtained model and using fuzzy logic, the aircraft status sensor fault detection unit was designed. The simulation results show that the fault detection system is able to work well, with additional alarms averaging 1 alert per 4-hour flight and miss alert rates averaging 1 alert per 2 hours. The results are confirmed by the experts from the UAV system.