Microprocessor based signal processing techniques for system identification and adaptive control of DC-DC converters

PhD ThesisMany industrial and consumer devices rely on switch mode power converters (SMPCs) to provide a reliable, well regulated, DC power supply. A poorly performing power supply can potentially compromise the characteristic behaviour, efficiency, and operating range of the device. To ensure accurate regulation of the SMPC, optimal control of the power converter output is required. However, SMPC uncertainties such as component variations and load changes will affect the performance of the controller. To compensate for these time varying problems, there is increasing interest in employing real-time adaptive control techniques in SMPC applications. It is important to note that many adaptive controllers constantly tune and adjust their parameters based upon on-line system identification. In the area of system identification and adaptive control, Recursive Least Square (RLS) method provide promising results in terms of fast convergence rate, small prediction error, accurate parametric estimation, and simple adaptive structure. Despite being popular, RLS methods often have limited application in low cost systems, such as SMPCs, due to the computationally heavy calculations demanding significant hardware resources which, in turn, may require a high specification microprocessor to successfully implement. For this reason, this thesis presents research into lower complexity adaptive signal processing and filtering techniques for on-line system identification and control of SMPCs systems. The thesis presents the novel application of a Dichotomous Coordinate Descent (DCD) algorithm for the system identification of a dc-dc buck converter. Two unique applications of the DCD algorithm are proposed; system identification and self-compensation of a dc-dc SMPC. Firstly, specific attention is given to the parameter estimation of dc-dc buck SMPC. It is computationally efficient, and uses an infinite impulse response (IIR) adaptive filter as a plant model. Importantly, the proposed method is able to identify the parameters quickly and accurately; thus offering an efficient hardware solution which is well suited to real-time applications. Secondly, new alternative adaptive schemes that do not depend entirely on estimating the plant parameters is embedded with DCD algorithm. The proposed technique is based on a simple adaptive filter method and uses a one-tap finite impulse response (FIR) prediction error filter (PEF). Experimental and simulation results clearly show the DCD technique can be optimised to achieve comparable performance to classic RLS algorithms. However, it is computationally superior; thus making it an ideal candidate technique for low cost microprocessor based applications.Iraq Ministry of Higher Educatio

Advanced Strategies for Robot Manipulators

Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored

Microprocessor based signal processing techniques for system identification and adaptive control of DC-DC converters

Many industrial and consumer devices rely on switch mode power converters (SMPCs) to provide a reliable, well regulated, DC power supply. A poorly performing power supply can potentially compromise the characteristic behaviour, efficiency, and operating range of the device. To ensure accurate regulation of the SMPC, optimal control of the power converter output is required. However, SMPC uncertainties such as component variations and load changes will affect the performance of the controller. To compensate for these time varying problems, there is increasing interest in employing real-time adaptive control techniques in SMPC applications. It is important to note that many adaptive controllers constantly tune and adjust their parameters based upon on-line system identification. In the area of system identification and adaptive control, Recursive Least Square (RLS) method provide promising results in terms of fast convergence rate, small prediction error, accurate parametric estimation, and simple adaptive structure. Despite being popular, RLS methods often have limited application in low cost systems, such as SMPCs, due to the computationally heavy calculations demanding significant hardware resources which, in turn, may require a high specification microprocessor to successfully implement. For this reason, this thesis presents research into lower complexity adaptive signal processing and filtering techniques for on-line system identification and control of SMPCs systems. The thesis presents the novel application of a Dichotomous Coordinate Descent (DCD) algorithm for the system identification of a dc-dc buck converter. Two unique applications of the DCD algorithm are proposed; system identification and self-compensation of a dc-dc SMPC. Firstly, specific attention is given to the parameter estimation of dc-dc buck SMPC. It is computationally efficient, and uses an infinite impulse response (IIR) adaptive filter as a plant model. Importantly, the proposed method is able to identify the parameters quickly and accurately; thus offering an efficient hardware solution which is well suited to real-time applications. Secondly, new alternative adaptive schemes that do not depend entirely on estimating the plant parameters is embedded with DCD algorithm. The proposed technique is based on a simple adaptive filter method and uses a one-tap finite impulse response (FIR) prediction error filter (PEF). Experimental and simulation results clearly show the DCD technique can be optimised to achieve comparable performance to classic RLS algorithms. However, it is computationally superior; thus making it an ideal candidate technique for low cost microprocessor based applications.EThOS - Electronic Theses Online ServiceIraq Ministry of Higher EducationGBUnited Kingdo

Experimental comparison of classical pid and model-free control: position control of a shape memory alloy active spring

WOSInternational audienceShape memory alloys (sma) are more and more integrated in engineering applications. These materials with their shape memory effect permit to simplify mechanisms and to reduce the size of actuators. sma parts can easily be activated by Joule effect but their modelling and consequently their control remains difficult, it is principally due to their hysteretic thermomechanical behaviour. Most of successful control strategy applied to sma actuator are not often suitable for industrial applications: they are particularly heavy and use the Preisach model or neural networks to model the hysteretic behaviour of these material; this kind of models are difficult to identify and to use in real time. That is why this paper deals with an application of the new framework of model-free control (mfc) to a sma spring based actuator. This control strategy is based on new results on fast derivatives estimation of noisy sig- nals, its main advantages are: its simplicity and its robustness. Experimental results and comparisons with pi control are exposed that demonstrate the efficiency of this new control strategy. Key words: Nonlinear control, Model-free control, Shape memory alloy, Derivative estimation, Nonphysical modelling

Dynamic modelling and control of a flexible manoeuvring system.

In this research a twin rotor multi-input multi-output system (TRMS), which is a laboratory platform with 2 degrees of freedom (DOF) is considered. Although, the TRMS does not fly, it has a striking similarity with a helicopter, such as system nonlinearities and cross-coupled modes. Therefore, the TRMS can be perceived as an unconventional and complex "air vehicle" that poses formidable challenges in modelling, control design and analysis, and implementation. These issues constitute the scope of this research. Linear and nonlinear models for the vertical movement of the TRMS are obtained via system identification techniques using black-box modelling. The approach yields input-output models without a priori defined model structure or specific parameter settings reflecting any physical attributes of the system. Firstly, linear parametric models, characterising the TRMS in its hovering operation mode, are obtained using the potential of recursive least squares (RLS) estimation and genetic algorithms (GAs). Further, a nonlinear model using multi-layer perceptron (MLP) neural networks (NNs) is obtained. Such a high fidelity nonlinear model is often required for nonlinear system simulation studies and is commonly employed in the aerospace industry. Both time and frequency domain analyses are utilised to investigate and develop confidence in the models obtained. The frequency domain verification method is a useful tool in the validation of extracted parametric models. It allows high-fidelity verification of dynamic characteristics over a frequency range of interest. The resulting models are utilized in designing controllers for low frequency vibration suppression, development of suitable feedback control laws for set-point tracking, and design of augmented feedforward and feedback control schemes for both vibration suppression and set-point tracking performance. The modelling approaches presented here are shown to be suitable for modelling complex new generation air vehicles, whose flight mechanics are not well understood. Modelling of the TRMS revealed the presence of resonance modes, which are responsible for inducing unwanted vibrations in the system. Command shaping 11 control strategies are developed to reduce motion and uneven mass induced vibrations, produced by the main rotor during the vertical movement around the lateral axis of the TRMS rig. 2-impulse, 3-impulse and 4-impulse sequence input shapers and Iow-pass and band-stop digital filters are developed to shape the command signals such that the resonance modes are not overly excited. The effectiveness of this concept is then demonstrated in both simulation and real-time experimental environments in terms of level of vibration reduction using power spectral density profiles of the system response. Combinations of intelligent and conventional techniques are commonly used the control of complex dynamic systems. Such hybrid schemes have proved to be efficient and can overcome the deficiencies of conventional and intelligent controllers alone. The current study is confined to the development of two forms of hybrid control schemes that combine fuzzy control and conventional PID compensator for input tracking performance. The two hybrid control strategies comprising conventional PO control plus PlO compensator and PO-type fuzzy control plus PlO compensator are developed and implemented for set-point tracking control of the vertical movement of the TRMS rig. It is observed that the hybrid control schemes are superior to other feedback control strategies namely, PlO compensator, pure PO-type and PI-type fuzzy controllers in terms of time domain system behaviour. This research also witnesses investigations into the development of an augmented feedforward and feedback control scheme (AFFCS) for the control of rigid body motion and vibration suppression of the TRMS. The main goal of this framework is to satisfy performance objectives in terms of robust command tracking, fast system response and minimum residual vibration. The developed control strategies have been designed and implemented within both simulation and real-time environments of the TRMS rig. The employed control strategies are shown to demonstrate acceptable performances. The obtained results show that much improved tracking is achieved on positive and negative cycles of the reference signal, as compared to that without any control action. The system performance with the feedback controller is significantly improved when the feedforward control component is added. This leads to the conclusion that augmenting feedback control with feedforward method can lead to more practical and accurate control of flexible systems such as the TRMS

Control Theory in Engineering

The subject matter of this book ranges from new control design methods to control theory applications in electrical and mechanical engineering and computers. The book covers certain aspects of control theory, including new methodologies, techniques, and applications. It promotes control theory in practical applications of these engineering domains and shows the way to disseminate researchers’ contributions in the field. This project presents applications that improve the properties and performance of control systems in analysis and design using a higher technical level of scientific attainment. The authors have included worked examples and case studies resulting from their research in the field. Readers will benefit from new solutions and answers to questions related to the emerging realm of control theory in engineering applications and its implementation

Modeling, identification and control of cart-pole system

To understand any physical world system, a proper mathematical model is required. With the help of mathematical model, the system can be studied and controlled. There are different ways to develop a mathematical model such as first principle method and system identification method. First principle method is generally used when there is sufficient knowledge of the physical world system but system identification is used when there is no knowledge of the system. System identification is widely used to develop mathematical model of complex, non-linear systems. Cart-pole system is a benchmark problem in control system where the control objective is to balance the inverted pendulum mounted on the cart to a vertical position. This complete system is nonlinear in nature and the mathematical model can’t be efficiently calculated using first principle modeling. So system identification method is used to develop the mathematical model of the said system. This thesis finds out the linearized mathematical model of the said cart-pole system using parametric system identification procedure. Parametric system identification procedure consists of experiment design, model structure selection, parameter estimation and model validation. This thesis also designs linear and non-linear controller for the said system. For linearized model of cart-pole system some of the linear controllers designed are LQR, LQR-Pole-placement-PID, Fuzzy-PID, LQG and H-infinity. For the nonlinear model of cart-pole system, the control techniques discussed are the partial feedback linearization and classical feedback linearization control

Input shaping-based control schemes for a three dimensional gantry crane

The motion induced sway of oscillatory systems such as gantry cranes may decrease the efficiency of production lines. In this thesis, modelling and development of input shaping-based control schemes for a three dimensional (3D) lab-scaled gantry crane are proposed. Several input shaping schemes are investigated in open and closed-loop systems. The controller performances are investigated in terms of trolley position and sway responses of the 3D crane. Firstly, a new distributed Delay Zero Vibration (DZV) shaper is implemented and compared with Zero Vibration (ZV) shaper and Zero Vibration Derivative (ZVD) shaper. Simulation and experimental results show that all the shapers are able to reduce payload sway significantly while maintaining desired position response specifications. Robustness tests with ±20% error in natural frequency show that DZV shaper exhibits asymmetric robustness behaviour as compared to ZV and ZVD shapers. Secondly, as analytical technique could only provide good performance for linear systems, meta-heuristic based input shaper is proposed to reduce sway of a gantry crane which is a nonlinear system. The results show that designing meta-heuristic-based input shapers provides 30% to 50% improvement as compared to the analytical-based shapers. Subsequently, a particle swarm optimization based optimal performance control scheme is developed in closed-loop system. Simulation and experimental results demonstrate that the controller gives zero overshoot with 60% and 20% improvements in settling time and integrated absolute error value of position response respectively, as compared to a specific designed PID-PID anti swing controller for the lab-scaled gantry crane. It is found that crane control with changing cable length is still a problem to be solved. An adaptive input shaping control scheme that can adapt to variation of cable’s length is developed. Simulation with real crane dimensions and experimental results verify that the controller provides 50% reduction in payload sway for different operational commands with hoisting as compared to the average travel length approach