مجله مهندسی برق و الکترونیک ایران
سال هفدهم شماره 3 (پاییز 1399)

In this paper, a novel offset-free control strategy is proposed for delayed discrete-time linear systems with constraint in control input and state. The proposed strategy is based on the reference input management approach and consists of two control loop: inner and supervisory loop control. At first, an augmented delay-free model is introduced and its controllability is investigated. Then, necessary and sufficient conditions for offset-free control of delayed systems are expressed. According to the augmented model and regardless the constraints presence, a suitable tracking controller is designed to form inner loop. The proposed tracking controller is determined in such a way that the resulted closed-loop model to be applicable to compute the so-called maximal output admissible set (MOAS) as the core of reference governor (RG) strategy. Finally, the supervisory loop is formed by reference governor (RG) block to manage the manipulated reference input for guaranteeing the constraint satisfaction. At the end, a numerical example illustrated to show the efficiency of the proposed strategy.

This study focuses on the state of the art in the field of bio-nanorobotics by describing various molecular level systems and associated design issues. Bio-nano robots are controllable machines at the nano meter or molecular scale that are composed of nano-scale components. With the modern scientific capabilities, it has become possible to attempt the creation of bio-nanorobotic devices and interface them with the macro world for control. There are countless such machines that exist in nature and there is an opportunity to build more of them by mimicking nature. Even if the field of nanorobotics is fundamentally different than that of macro robots due to the differences in scale and material, there are many similarities in design and control techniques that eventually could be projected and applied. A roadmap towards the progression of this field is proposed and some design concept and philosophies are illustrated. There are many applications for bio-nanorobotic systems and its biggest impact would be in the area of medicine. Also, this study utilizes the bio-nanorobots characteristics to design fault-tolerant systems and reliability modeling that are more powerful in terms of implementing robust digital function.

Lyapunov method is an effective method to prove the stability of the equilibrium points of dynamical systems. One of the applications of the Lyapunov analysis, is estimation of the region of attraction. In general, based on the selected Lyapunov function, a portion of the region of attraction is estimated that may not be the best. In this paper, via proposing an algorithm for generating Lyapunov candidate functions in a series from, a method is developed by which, in each step ahead, a modified estimation of the region of attraction is presented. The main idea of generation of the mentioned entries, is constructing a new Lyapunov function using a linear combination of the current Lyapunov function with its derivative. According to each candidate Lyapunov function, a region of attraction is constructed so that in the generation process of the new functions, every estimated region of attraction is subset of the next estimation. Finally, to show the effectiveness of the obtained results, some numerical examples are simulated.

Melanoma is one of the most dangerous types of cancers and every year, many people suffer from this cancer. Hence, quick diagnosis and treatment are significantly important for the physicians. In the recent decade, intelligent methods have attracted considerable attention for diagnosing and treating the melanoma. The main objective of this paper is determining the optimal dosage of the drug for the elimination of the cancer cells while preventing from the side effect of the drug on the normal cells. To this aim, the Q-learning algorithm is employed. In order to select the actions, a Case-Based Reasoning (CBR) policy is used, which is an accelerated heuristic policy. The considered policy has increased the learning speed and reduced the overall time, to reach the optimal policy. The half-life effect of the drug is also considered to obtain the side effect of the drug on the patientchr('39')s body, at each time step. In order to demonstrate Q-learning algorithm performance in cancer cells control and optimal dosage determination purposes, Q-learning is compared with two methods, including fix dosage injection method and Hamiltonian method, which is one of the most important optimal control methods. Finally, it is revealed that the total injected dosage by using Reinforcement Learning method (Q-learning) is significantly reduced within the whole period of time in comparison with employing the optimal control and a fixed dosage injection cases. The number of cancer cells is controlled, as well. It should be noted that by applying the noise and uncertainty to the system parameters and the initial conditions, the proposed method can successfully control the cancer cells.

This paper proposes a new computed torque strategy to control the robotic manipulators using polytopic linear parameter varying (LPV) modeling method. An LPV representation of the robot is generated by identification of its dynamic in different workspace points about an arbitrary full-path trajectory. Identification of the models is done by using the least square of errors  algorithm. By utilization of parameter set mapping based on parameter component analysis (PCA) a reduced polytopic LPV model is obtained that reduces the complexity of the implementation. In the conventional computed torque control, it is necessary to know the dynamical model of the robot while in the proposed method an estimation of the required torque is computed based on the obtained LPV model. The control gain matrix is derived by solving a set of linear matrix inequalities (LMIs) that takes the time derivative of the Lyapunov function to the sufficiently small value. Sufficient conditions are provided to ensure the asymptotic stability of the closed loop LPV system against the torque estimation error. The proposed scheme is applied to trajectory tracking control of a six degree of freedom (DOF) PUMA 560 robot manipulator.

In this paper, a new autopilot  is designed using backstepping adaptive second-order sliding mode.Autopilot designed is a good result  against uncertainty and perturbations. It also ensures convergence limited time, Reduce chattering, increased control accuracy and the need to derive the sliding surface of the other features of this method of control. One feature of this method is that comparative gains that have poor information uncertainty and uncertainty is implemented. Autopilot designed both the exterior and interior have been included for each section, Designed for each section in a similar way. The simulation results show that the reference signal on autopilot designed with adaptive backstepping second-order- sliding mode using super Twisting  algorithm compared to backstepping, sliding mode first order, adaptive backstepping first-order sliding mode tracking is faster and stronger.

The Multi-agent systems has shown their usefulness as an efficient approach for modeling, analyzing as well as implementing complex, dynamic and distributed applications such as robotic teams, distributed control, resource management, traffic control, land use planning, crisis management, forest fire control and to name but a few. The main challenge in multi-agent systems is to find the suitable behavior for each agent that maximize the average utility rate of the whole system. Moreover, it is sometimes necessary that they learn new behaviors online, such that the performance of the whole system gradually improves. Thus, a learning mechanism is necessary so that agents gradually find the global optimal solution on their own. In this context, reinforcement learning as a promising approach for training agents could be useful such that the agent never sees examples of correct behavior but instead receives positive or negative rewards for the actions it tries. Thus, it allows agent to automatically determine the ideal behavior within a specific context, in order to maximize its performance. Each time the agent performs an action in its environment, a trainer may provide a reward to indicate the desirability of the resulting state and the agent tries to learn a control policy which is a mapping from states to actions that maximizes the expected sum of the received rewards. Continuous reinforcement learning algorithms which use generalization, the ability of a system to perform accurately on unseen data, perform properly in real-world problems. In practical point of view, there is a natural metric on the state space such that close states exhibit similar behavior so that the agents are able to deal with states never exactly experienced before and they can learn efficiently by generalizing from previously (similar, close) experienced states. The success of continuous reinforcement learning algorithms on real-world problems hinges on effective function approximator which maps states to values via a parameterized function. Among the many function approximator schemes proposed, tile coding which forms a piecewise-constant approximation of the value function and strikes an empirical balance between representational power and computational cost is applied in this research. The focus of this paper is to combine multi-agent systems with continuous state reinforcement learning by using tile coding. The proposed approached was validated using traffic signal control, in which traffic lights located at intersections can be seen as autonomous agents that learn while interacting with the environment. There are some challenging issues in traffic signal control such as high number of agents, nonstationarity of the multi-agent learning problem, the curse of dimensionality and continuity in state space which makes it as a suitable testbed. The reinforcement learning controller is benchmarked against optimized pretimed control. The results indicate that reinforcement learning agent achieves 21% less stop time compared to optimized pretimed control.

In last decade a lot of algorithms and novelties has been developed to reduce work’s stresses on pilots and increase safety of flight. The major records from aviation technology is design and implementation Helmets, cost efficiently and reliably. In this case we developed a domestic Helmet to monitor pilot’s vital function with advanced electrical instrumentation, fusion the radar’s data and angle of pilot’s head with PC-Stick and new mechanism for high-speed serial

For the balk acoustic wave (BAW) gyroscopes it is desired to maximize the matching frequency of the drive and the sense modes, since this improves the quality factor of the device. In this paper, the effect of perforation shape formed during the fabrication release steps of a BAW gyroscope upon its sense / drive mode matching frequency is examined.The Gyroscope is assumed to be a closed loop built employing SOI technology. In particular, we have considered the effects of two categories of perforation shapes. If the proof mass holes are cone-shaped with interior obtuse angle, mismatch frequency between modes becomes smaller but if cones are made with interior acute angle, mismatch frequency increases.

This paper presents the analysis and design of an asymmetrical directional coupler (ADC) with arbitrary source and load impedances. The microstrip-coupled-line structure is analyzed and its impedance matrix is first obtained. Its transmission matrix is then determined. The transmission matrix of multi-section ADC is then obtained using its properties. Finally, the total transmission matrix is converted to the impedance matrix and then its scattering matrix is derived. The method of least squares (MLS) is applied for the design of ADC. An error function is constructed based on the scattering parameters of ADC, which is a function of its geometrical dimensions. The minimization of the error function determines its dimensions (namely widths and lengths of line sections and their spacings) by writing a software in MATLAB using the Genetic Algorithm (GA) and Conjugate Gradient Algorithm (CGM). The HFSS Full Wave Software is applied to finalize the optimum design of the ADC configuration. An example of the ADC is designed, fabricated (on the substrate ROGERS 4003) and measured as the proof of concept. Its coupling coefficient is 6 dB, frequency band is 5.2-6.2 GHz, terminal impedances are 50, 50, 75 and 100 Ohms.

In this paper, a new dynamical model is suggested for the Brush-Less DC (BLDC) thruster motors, by an observer-based robust adaptive fuzzy controller. The proposed control method utilizes an accurate thrust model which is more efficient than the other approach. The suggested scheme is very simple, accurate and robust, so that, a control method of thrust, torque, and speed of BLDC motors is available. Based on the adaptive fuzzy algorithm an observer-based estimator is presented that applies feedback error function as the input of the fuzzy system to estimate and adaptively compensate the external disturbance and unknown uncertainties of the system under control. Although the proposed controller scheme requires the uncertainties to be bounded, it does not require these bounds to be known. An H∞ robust controller is employed to attenuate the residual error to the desired level and recompense both the fuzzy approximation errors and observer errors. The proposed method guarantees the stability of the closed-loop system based on the Strictly Positive Real (SPR) condition and Lyapunov theory. Finally, in simulation studies, to demonstrate the usefulness and effectiveness of the proposed technique, a BLDC motor system is employed.

Classification of ECG arrhythmia along with medical knowledge can lead to proper decision-making on the patientchr('39')s condition. Also, classification of arrhythmia types is one of the challenging issues due to the need for detailed analysis of the extracted feature from ECG signal. Therefore, addressing this field using signal processing techniques can be very important. In this paper, various types of morphological features are used to determine the type of ECG arrhythmia. Sparse structured principal component analysis and sparse non-negative matrix factorization algorithms are used to learn the over-complete models based on the characteristics of each data category. Also, the wavelet packet transform coefficients are calculated in different decomposition level to learn over-complete dictionaries. The results of this categorization are compared with the results of the classification based on the neural network, support vector machine another methods presented in this processing field. The simulation results show that the proposed method based on the selected combinational features and learning the over-complete dictionaries can be able to classify the types of ECG arrhythmia precisely.

Increase in chip temperature causes more power consumption in multi core processors and decreases the CPU lifetime. The optimization of online task assignment to processing cores is an efficient approach to control chip-wide temperature distribution. However, task assignment faces some uncertainties in the system (including: stochastic task arrival, random task pairings, and time-varying thermal profile variations). In this paper, an online task to core assignment approach is presented which uses Semi-Markov Decision Process to prevent performance reduction and considers randomness and uncertainties in system. As the transitional properties are not accessible and due to high dimension of system state components, the proposed approach uses function approximation to approximate action values in any system state. The simulation results show 6 centigrade decrease in system average peak temperature and 66 milliseconds decreases in task service time.

An exact 2D analytical method for magnetic field and electromagnetic torque calculation in high speed spoke type interior permanent magnet Machines considering slotting effects, magnetization orientation and winding layout has been proposed in this paper. The analytical method is based on the resolution of Laplace and Poisson equations as well as Maxwell equation in polar coordinate by using sub-domain method and applying hyperbolic functions. The proposed method is applied on the performance computation of a prototype spoke type permanent magnet machine, i.e., a 3-phase 18S-8P motor. The analytical results are validated through FEA method and experimental tests.