Journal of Control
Year:2021 | Volume:15 | Issue:2|Papers Count:12

Parkinson's disease is the most common neurological disorder after Alzheimer's, which is caused by a decrease in the brain's ability to produce dopamine. There are several ways to reduce the effects of this disease so far, but unfortunately these have many side effects. For this reason, many attempts have been made to find non-invasive alternative methods, which are generally ineffective due to the cost, system weight, and so on. In this paper, the active method was used to reduce the transmitted vibration to the device. In the proposed scheme, the hand vibration was first identified using an ANFIS filter and then a reverse motion was applied by two electric motors and the designed mechanism, using a predictive controller. The simulation results showed the system performance in following voluntary movements and eliminating spontaneous vibration, which stems from Parkinson's disease.

In this paper, the design of consensus control law in leader-follower multi-agent systems will be studied with agents with TS fuzzy dynamic model. In this study, assuming an excitation-event mechanism for updating the control signal of each agent, while reducing the number of control signal updates, the closed-loop system stability will be guaranteed. Moreover, in the proposed event triggered control strategy, the Zeno-behaviour is avoided. Each agent only sends data to other agents at the time of its trigger instants, and its control input is updated only at the same time. The design of the control law and the excitation-event mechanism will lead to the solution of a linear matrix inequality. The innovation of the study is in selecting a fuzzy model for agents that can extend the solution of the consensus problem in multi-agent systems to more nonlinear systems. Finally, the effectiveness of the proposed method is shown through a numerical example.

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 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, the control problem of a finite-time target tracking for an underactuated autonomous submarine is considered in three-dimensional space in the presence of unknown disturbances caused by waves and ocean currents via Dynamic Surface Control (DSC) method. At first, computational complexities of the backstepping method are greatly reduced by employing the DSC technique. Next, by designing a finite-time controller, it can be demonstrated that system errors converge to a small region containing the origin in a finite time. An adaptive robust controller is employed to compensate for unknown vehicle parameters and uncertain nonlinearities. The stability of the proposed controller is demonstrated by an analysis based on Lyapunov theory. Finally, the tracking performance of the proposed control scheme is simulated by MATLAB software and its effectiveness is shown as well.

In this paper, a two-input two-output experimental TRMS model helicopter is designed and built to investigate and test different identification and control algorithms. First, the steps required (software and hardware) to design, build, and operate this two-degree-of-freedom system are stated. Then, laboratory system is identified and modeled using physical laws governing the system and their analysis, as well as with the determination method and some classical identification methods to determine the parameters, and a highly nonlinear dynamic with interaction effect for the system is achieved. In addition, the two-input two-output system is transformed into two decoupled singleinput single-output subsystems and each PID controller is individually designed for a subsystem. Also, considering the interaction effect for the two-input two-output system, a PID controller is designed and implemented using Sequential loop closing (SLC) method. For validation, the practical results of the implementation are compared with the simulation results in Matlab software. The results show the validity of the extracted model as well as the effective performance of the controller.

In this paper, a tracking decentralized Adaptive Integral Terminal Sliding Mode control (DAITSMC) technique is proposed for a class of linear interconnected mechanical systems with unknown linear interconnections between subsystems and in the presence of disturbance is considered. In this way, the interconnected system is divided into several subsystems. Then an integral terminal sliding surface is considered for each subsystem. The proposed approach increases the speed of input tracking in a finite time as well as the disturbance attenuation. The effect of unknown linear interconnections between subsystems is considered as uncertainty that is estimated by adaptive rules. The stability of the closed-loop system is guaranteed by a Lyapunov function and selecting the appropriate design parameters. The developed method is applied to two interconnected mechanical systems; the simulation results show that the proposed method (DAITSMC) is efficient for interconnected systems in the presence of disturbance. Comparison of simulation results with several control methods shows that the proposed method (DAITSMC) is efficient for linear interconnected systems in the presence of disturbance and the convergence error becomes zero faster.

Optimal Task Scheduling is one of the most important challenges for achieving high performance in distributed environments such as cloud computing. The primary purpose of task scheduling is to allocate tasks to resources so that some of the system performance metrics will be optimized such as runtime or parallelism. Task scheduling is an NP-complete problem, so heuristic or metha-heuristic algorithms are used to solve it. Because cloud providers offer computing resources based on the pay-as-you-go model, the scheduling algorithm affects the users cost of the cloud. In this paper, a new cloud task scheduling algorithm based on particle swarm optimization as a metha-heuristic method is proposed that assigns users tasks to free resources in cloud computing environments. To enhance the convergence rate of the particle swarm optimization method, the intelligent water drops algorithm is applied. The results of this algorithm on random graphs showed a significant improvement in the performance of the proposed method compared to other task scheduling algorithms.

Resonant converter due to implementation of zero voltage switching (ZVS) or zero current switching (ZCS), are very interested. Although using these techniques, increases the efficiency and also decreases the generated EMI noise, obtaining the small-signal model of these converters is very complicated. The state-space variables mostly change as a sinusoid curve, so the average of these variables is equal to zero. Therefore the traditional method such as state-space averaging is not applicable in order to obtaining the state space equation of the converter. In this article, the state space equation is obtained by using the extended describing functions. To verify the obtained equation, the Middlebrook method is suggested. By means of this method, the bode-plot of the open-loop transfer function could be obtained based on the existing hardware. So an isolated series resonant converter is implemented and the required signals are measured in order to draw the bodeplot of the open-loop transfer function based on Middlebrook method. Verification is performed by comparing the experimental results with simulation results.

The use of the input low-pass filter causes instability in DC-DC converters. These instabilities occur because of the interaction between the filter and the converter. In this paper, a control method based on Lyapunov is proposed. This control method uses the exact system model without linearization. All state variables are controlled using an instantaneous model. A new state variable is defined to reduce the static output voltage error. No condition is necessary to implement the control strategy. The simulation and experimental results are presented to validate the proposed control method.

In this paper, generalized model predictive spread control methodis developed to consider the intermediate constraints on system states and system inputs. Because of using the orthogonal basis functions and thus reducing the computational burden, this new method which is named constrained generalized model predictive spread control can be used in online implementation of a finite-time constrained optimal control problem. For demonstrating the performance of the proposed technique, in this paper an interceptor midcourse guidance problem is formulated to reach a desired point in space. Several constraints are considered such as: hard and soft intermediate and terminal constraints on system states and different constraints on input acceleration command to the interceptor in different time intervals of the trajectory. It is shown that in all the above situations, the proposed method could produce the optimal guidance commands such that all the interceptor trajectory constraints are satisfied.

Several researches have shown that emotion is a mental process and relates to the human’ s brain. The emotion has impacts on important procedures, such as memory, concentration, thinking and decision-making. As a result, investigating the mechanism and performance of the emotion have attracted the cognitive science researchers’ attentions. In addition to clinical applications on quick detection, diagnosis and treatment of psychological disorders, investigating the emotion through biological signal processing can play an important role in human-computer communication-based sciences. This will result in progressive improvements in this field. Due to the fact that number of channels and features extracted out of the EEG signal are usually high, selecting relevant channels, with the aim of obtaining effective features, can have a prominent role in the efficiency of these methods. On the other hand, these features should result in the appropriate efficiency when encounter new subjects. In this paper, a multi-task approach is represented for emotion-related channel selection and proper subject-independent feature selection purposes. Moreover, to demonstrate the efficiency of the proposed method, DREAMER and DEAP datasets are used. Also, considering two emotional dimensions, including arousal and valance, some experiments are performed to show the desired efficiency of the proposed method for channel selection and subject-independent feature selection. Experimental results show that the proposed method has better efficiency in comparison with used methods.

The scientific modeling of cellular growth and proliferation can significantly help to physicians to offer an appropriate treatment. In this paper the modeling of the body's immune system function for a patient with melanoma cancer has been studied. The main advantage of this model is considering more variables to describe the dynamics of the melanoma patient’ s body, which will make our model more realistic. For estimating the coefficients of the mathematical model, a multiobjective optimization method, "Non-Dominated Sorting Genetic Algorithm", is used. One of the major advantages of such method is considering multiple objectives and simultaneously constraining them. Simulation results reveal that our mathematical model can successfully simulate the function of the body defense system of the melanoma patient. In order to analyze the sensitivity and determine the correlation of the mathematical model output to changing in some parameters, Partial Rank Correlation Coefficient (PRCC) method is employed. Finally, it is demonstrated that Interleukin-2 (IL-2) generation rate change has the highest PRCC value and changing this parameter will have a high impact on the model outputs.