A Sliding Mode Control for a Sensorless Tracker: Application on a Photovoltaic System

The photovoltaic sun tracker allows us to increase the energy production. The sun tracker considered in this study has two degrees of freedom (2-DOF) and especially specified by the lack of sensors. In this way, the tracker will have as a set point the sun position at every second during the day for a period of five years. After sunset, the tracker goes back to the initial position (which of sunrise). The sliding mode control (SMC) will be applied to ensure at best the tracking mechanism and, in another hand, the sliding mode observer will replace the velocity sensor which suffers from a lot of measurement disturbances. Experimental measurements show that this autonomic dual axis Sun Tracker increases the power production by over 40%

Synthesis of Minimal Error Control Software

Software implementations of controllers for physical systems are at the core of many embedded systems. The design of controllers uses the theory of dynamical systems to construct a mathematical control law that ensures that the controlled system has certain properties, such as asymptotic convergence to an equilibrium point, while optimizing some performance criteria. However, owing to quantization errors arising from the use of fixed-point arithmetic, the implementation of this control law can only guarantee practical stability: under the actions of the implementation, the trajectories of the controlled system converge to a bounded set around the equilibrium point, and the size of the bounded set is proportional to the error in the implementation. The problem of verifying whether a controller implementation achieves practical stability for a given bounded set has been studied before. In this paper, we change the emphasis from verification to automatic synthesis. Using synthesis, the need for formal verification can be considerably reduced thereby reducing the design time as well as design cost of embedded control software. We give a methodology and a tool to synthesize embedded control software that is Pareto optimal w.r.t. both performance criteria and practical stability regions. Our technique is a combination of static analysis to estimate quantization errors for specific controller implementations and stochastic local search over the space of possible controllers using particle swarm optimization. The effectiveness of our technique is illustrated using examples of various standard control systems: in most examples, we achieve controllers with close LQR-LQG performance but with implementation errors, hence regions of practical stability, several times as small.Comment: 18 pages, 2 figure

Англійська мова для студентів електромеханічних спеціальностей

Навчальний посібник розрахований на студентів напряму підготовки 6.050702 Електромеханіка. Містить уроки, що структуровані за тематичними розділами, граматичний коментар, короткі англо-український і українсько- англійський словники та додатки, які спрямовані на закріплення загальних навичок володіння англійською мовою. Акцентований на ɨсобливості термінології, що застосовується у науково-технічній галузі, зокрема, в електромеханіці та виконання запропонованих завдань, що буде сприяти формуванню навичок перекладу з англійської та української мов, сприйняттю письмової та усної англійської мови, вмінню письмового викладення англійською мовою науково-технічних та інших текстів під час професійної діяльності, спілкуванню з професійних та загальних питань тощо

Analysis and stabilization of chaos in electric-vehicle steering system

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A Practical Fast Acting Control Scheme For Fuzzy Logic-Based Voltage Stabilization Control

This paper presents a simplified control model for stabilizing a load voltage using a switched reactor in parallel with a fixed capacitor of static VAR compensator. Two IGBT’s are used to control the reactance of the switched reactor. A uniform pulse width modulation is used for controlling the two switches. The compensator has a simple control circuit and structure. A complete modeling and numerical simulation for the proposed systems is presented. A high speed Digital Signal Processor is used for implementing proportional-integral (PI) and fuzzy load voltage controllers. Experimental results indicate the superiority of fuzzy logic control over the conventional proportional-integral control method. Simulation results are reported and proved to be in good agreement with the relevant experimental results

Modern PID Controller Parameter Design for DC Motor

DC motor is widely applied in industry practices. The speed control is a key procedure of the design, where PID controller is still the main stream. Stability and transient response are two significant design requirements for the DC motor control system. To decrease overshoot and rising time, a lot of tuning methods were invented, which were used in DC motor design. However, most of rules are based on empirical observation and experiment experience. The purpose of this thesis is to theoretically design PID parameter sets which can guarantee both system stability and performance. The thesis starts from theoretical signature PID stabilizing sets design method and finds all PID parameters for DC motor control system. Then the thesis proposes a modified characteristic ratio assignment criterion to optimize previous stabilizing sets. Utilizing the parameter from new sets, better transient response performance can be reached. To demonstrate the method, a PID controller and PD controller design are integrated into the DC motor velocity control and DC motor position control problems separately. Next, the DC motor system is discretized. The digital PI and PID stabilizing sets are designed by using root counting algorithm. To sum up, the thesis gives a completed theoretical procedure to design PID controller parameters for DC motor system. The optimized parameter sets can not only satisfy stability criterion, but also achieve better transient performance

ADAPTIVE DYNAMICAL FEEDBACK REGULATION STRATEGIES FOR LINEARIZABLE UNCERTAIN SYSTEMS

In this paper we address the design of adaptive dynamical feedback strategies of the continuous and discontinuous, types for the output stabilization of nonlinear systems. The class of systems considered corresponds to nonlinear controlled systems exhibiting linear parametric uncertainty. Dynamical feedback controllers, ideally achieving output stabilization via exact linearization, are obtained by means of repeated output differentiation and, either, pole placement, or, sliding mode control techniques. The adaptive versions of the dynamical stabilizing controllers are then obtainable through standard, direct, overparamemzed adaptive control strategies available for linearizable systems. Illustrative examples are provided which deal with the regulation of electromechanical systems

Nonlinear Robust Control of a Series DC Motor Utilizing the Recursive Design Approach

In this thesis, the investigation of asymptotic stability of the series DC motor with unknown load-torque and unknown armature inductance is considered. The control technique of recursive, or backstepping, design is employed. Three cases are considered. In the first case, the system is assumed to be perfectly known. In the second case, the load torque is assumed to be unknown and a proportional-integral controller is developed to compensate for this unknown quantity. In the final case, it is assumed that two system parameters, load torque and armature inductance, are not known exactly, but vary from expected nominal values within a specified range. A robust control is designed to handle this case. The Lyapunov stability criterion is applied in all three cases to prove the stability of the system under the developed control. The results are then verified through the use of computer simulation

Performance Comparison Between Pid And Lqr Controllers For Drone Application

In industries of unmanned aerial vehicle (UAV), the implementation of a motor control system is essential to ensure the system mechanism can be operated efficiently. In addition, DC servo motor systems are widely applied in a variety of fields of UAV. They are used to generate electrical power in power plants and to supply mechanical motive power to operate the UAV and manage numerous industrial operations in industrial settings. In some application of the DC servo motor, when load is applied, or disturbance occur during the operation, the DC servo motor is required to maintain its desired speed to ensure the stability and efficiency of the system. This system can be controlled using PID, Fuzzy, LQR and other more. The PID algorithm becomes a closed loop system when it is added to the motor. The system is developed in MATLAB software, and the PID algorithm is tuned by adjusting the values of proportional gain, Kp, integral gain, Ki, and derivative gain, Kd to get a motor speed and position that is less overshoot, has a longer settling time, and has a longer rise time. To control the Dc servo motor speed and position, the Linear Quadratic Regulator (LQR) controller is introduced. The LQR controller is designed and tuned using MATLAB/Simulink, and it is simulated using a mathematical model of a DC servo motor. A new approach of controlling the motor is the Linear Quadratic Regulator (LQR) controller. The Linear Quadratic Regulator (LQR) is an optimum control theory that focuses on controlling a dynamic system at the lowest possible cost. The purpose of the Linear Quadratic Regulator (LQR) is to minimize the deviation of the motor's speed and position. The input voltage of the motor will be specified by the motor's speed, and the output will be compared to the input. The advantages of using LQR are that it is simple to build and that it improves the accuracy of state variables by estimating them. When contrasted to pole placement, the LQR control has the advantage of specifying a set of performance weighting rather than needing to define where eigenvalues should be positioned, which may be more intuitive