جستجوی مقالات مرتبط با کلیدواژه « کنترل بهینه » در نشریات گروه « برق »

Time-delay systems have been very much considered in the last few decades. Many of these time-delay systems appear in different systems and branches of science such as engineering, chemistry, physics, disease models. The presence of delay makes the analysis and control of such systems much more complicated. In fact, the application of Pontryagin’s maximum principle to the optimal control problems with time-delay results in boundary value problem involving both delay and advance terms. In this paper, we consider a time-delay optimal control problems. The first section, using the Pontryagin's maximum principle for optimal control problems with time delay, the necessary optimality conditions for this problem, are obtained. Then a new algorithm is proposed to solve this problem numerically. This algorithm is based on an approximation for derivatives and linear interpolation for delayed arguments. Finally, the resulting equations becomes a linear programming problem that can be solved numerically. The efficiency of the proposed method is evaluated by solving several numerical examples.

Bellman's optimality principle states that designing an optimal controller for continuous-time bilinear systems with known system dynamics has a high computational complexity. As a result, controller design typically uses approximation techniques that depend on system dynamics knowledge. This problem will become more challenging when the system dynamics are unknown. Identifying the bilinear system dynamics through identification techniques is the first step toward overcoming this. It is well known that the identification methods give the designer a linear model to use in the controller design, based on the input and output data of the system. This paper proposes a new iterative method to design an optimal controller for a bilinear system whose dynamics are unknown, using an online adaptive policy iteration. In the proposed iterative method, instead of knowing the dynamics of the bilinear system, the optimal controller is designed by using the online input information and measurement of states. Also, by applying noise as an input for the system in a certain time interval, the need to measure the states for the next iterations is eliminated. The convergence of the adaptive iterative process to the optimal controller has been presented and proved in a theorem.

In this study, the possibility of energy recovery from oil pipelines as a source of clean energy is discussed. At present, a significant amount of energy is lost in oil pressure reducing stations. The energy regeneration scheme proposed in this study investigates the possibility of converting the currently wasted energy into the electrical energy by using a Francis turbine coupled with a synchronous generator system. The dynamic hydraulic model of the existing pipeline system starting from the compressor stations to the pressure regulating stations is obtained. Also the state space equations of the energy regeneration system including the control valves, the turbine and the synchronous generator connected to a local load are modeled. The propositional-integral-derivative (PID) and linear quadratic regulator (LQR) are designed to control the flow, pressure, speed and voltage in the energy regeneration system. The results of two controllers are then compared under different conditions. The obtained model for Razan-Shazand transmission line crude oil are simulated and the results are interpreted. The results show the possibility of a significant energy regeneration and the appropriate performance of the designed system with the proposed controllers.

The process of empowering muscles in order to make them to a normal and common value is an expensive and prolonged work. There are some commercial exercise machines used for  this purpose called rehabilitation systems. However, these machines have limited use because of some reasons. In this paper, an algorithm and an improved rule are presented for controlling a rehabilitation system of lower limbs which is implemented on a 3-Degree Of Freedom  planar robot. Estimation and optimization of control parameters will be done by artificial neural networks and genetic algorithms, respectively (intelligent strategy). MATLAB Simulink is used for simulations. It shows that proposed algorithm in comparation with other similar methods is a low-cost method and needs to less energy and force with high accuracy.

This paper is concerned with the problem of output feedback fuzzy mixed H2/H_inf tracking control design for nonlinear systems. A general and modified class of output-feedback controller structure is assumed. Using parallel distributed compensation, a fuzzy controller is proposed which not only satisfies an H_inf tracking control constraint, but also optimally minimizes a H2control performance measure. The proposed method leads to a trade-off between the tracking performance and the amount of control input effort. The problem formulation and the method of finding the optimal fuzzy tracking controller parameters involve a single step linear matrix inequalities form. On a benchmark example, the proposed method is applied on the inverted-pendulum system and is compared with traditional H_inf-only results from diffrent perspectives.

This paper is concerned with the problem of improvement of fuzzy H_infinity tracking controller for nonlinear systems modeled by T-S fuzzy scheme. The fuzzy tracking controller not only stabilizes the closed-loop system, but also results in the H_infinity tracking error norm to all the bounded external signals to be less than some given value. A new tracking control law is proposed for each linear local subsystem of T-S fuzzy model. A Linear Matrix Inequalities (LMIs) approach is proposed to find all the parameters of the control laws. The proposed approach results in a noticeable improved tracking performance with respect to the existing approaches. An investigation of the tracking performance of the proposed approach on the inverted pendulum system, in comparison with the other approaches, shows the improvement.

The process of empowering the muscles in order to make them to a normal and common value is an expensive and prolonged work, in common available methods. Hence, it is clearly necessary that Mechatronic technologies should be used in this area. In this paper, an algorithm and an improved rule are presented for controlling a rehabilitation system of lower limbs which is implemented on a 3-Degree Of Freedom (DOF) planar robot. Estimation and optimization of control parameters will be done by artificial neural networks and genetic algorithms, respectively (intelligent strategy). Thereafter, the results of simulations are presented by defining a physiotherapy standard mode on a desired trajectory. MATLAB/SIMULINK is used for simulations. Finally, a comparative discussion between this strategy and common methods is devised.

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.

Lateral vehicle’s control with constant longitudinal velocity using adaptive dynamic programming, backstepping and zero-sum games theory is investigated in this paper. The nonlinear dynamics is considered and the steering torque is chosen to be the control input instead of the steering angle. At first, a subsystem is created by augmenting the lateral vehicle’s dynamics with lane keeping ones considering the steering angle as the control input and the road curvature as a disturbance. Utilizing adaptive dynamic programming, neural networks and zero-sum games theory, the optimal control law is obtained and then, the results exerted on the second subsystem which is the dynamics of the steering angle and a control law is captured for which using the backstepping control method. Finally, performance of the proposed algorithm is demonstrated by applying it on a typical vehicle model.

Rehabilitation and assistive systems such as rotary series elastic actuators (RSEA) should provide the desired torque precisely. In this paper, to improve the life quality of those who suffer from weak knees, the control problem of a rotary series elastic actuator (RSEA) has been studied in order to generate soft human walking motion. These actuators produce the require torque, but the nonlinear resistive and inertia loads inherent in the actuators, set challenges to generate the desired torque accurately. The nonlinear resistive factors and uncertainties in plant dynamics which make the precise torque control difficult should be considered. In this paper, a robust controller based on an optimized control approach is designed to enhance control performance and provide the robustness for modeling uncertainties. The simulation is used to compare the output results of the proposed algorithm with the conventional methods such as sliding mode and adaptive-sliding mode controllers.

In the dynamic manipulation of objects, the aim is to throw an object by a robot to the desired target even outside the reachable workspace. In this paper, the concept of the throw-able workspace or dynamic manipulation workspace is defined as a set of points which the robot is able to throw the object at them. Thus, in order to obtain the maximum dynamic manipulation workspace which means the farthest points that object can be manipulated, it is necessary to solve the optimal throwing problem. To this end, the optimal throwing problem is defined as the optimal control problem solved using the indirect solution method based on the fundamental theorem of the calculus of variations. By applying the throwing equation as a moving boundary condition, the derived optimality conditions construct a two-point boundary value problem which its solution results in the optimal throwing. Finally, an algorithm is presented to calculate the maximum dynamic manipulation workspace. Then, simulation results are presented for a single link robot in order to evaluate the defined concept as well as the effectiveness of the proposed method for problem-solving.