Due to the prevalence of pulse encoders for system state information, an all-digital proportional-integral-derivative (ADPID) is proposed as an alternative to traditional analog and digital PID controllers. The basic concept of an ADPID stems from the use of pulse-width-modulation (PWM) control signals for continuous-time dynamical systems, in that the controllers proportional, integral and derivative actions are converted into pulses by means of standard up-down digital counters and other digital logic devices. An ADPID eliminates the need for analog-digital and digital-analog conversion, which can be costly and may introduce error and delay into the system. In the proposed ADPID, the unaltered output from a pulse encoder attached to the systems output can be interpreted directly. After defining a pulse train to represent the desired output of the encoder, an error signal is formed then processed by the ADPID. The resulting ADPID output or control signal is in PWM format, and can be fed directly into the target system without digital-to-analog conversion. In addition to proposing an architecture for the ADPID, rules are presented to enable control engineers to design ADPIDs for a variety of applications.

Electro-Hydraulic (EH) systems are commonly used in the industry for applications that require high power-weight ratios and large driving forces. The EH system studied in this master thesis have recently been upgraded with new hardware components and as a part of this upgrade a new controller was requested. The system consists of a controller that computes a control signal for an electric motor. The motor drives a gear pump that generates a flow of hydraulic fluid. The flow is then directed to a cylinder. The movements of a piston in the cylinder is affected by the flow and the piston position can be measured. The measured piston position is then fed back to the controller and the control loop is complete. The system was previously controlled using a Proportional-Integral-Derivative (PID) controller and the purpose of this thesis is to compare the old controller with alternative control strategies suitable for this application. The evaluation of the controllers is based on both software and hardware simulations and results in a recommendation for final implementation of the best suited controller. The control strategies chosen for investigation are: a retuned PID controller, a PID controller with feed forward from reference, a PID based cascade controller, a Linear Quadratic (LQ) controller, and a Model Predictive Controller (MPC). To synthesize the controllers an approximate model of the system is formed and implemented in the software environment Matlab Simulink. The model is tuned to fit recorded data and provides a decent estimation of the actual system. The proposed control strategies are then simulated and evaluated in Simulink with the model posing as the real system. These simulations resulted in the elimination of the cascade controller as a possible candidate since it proved unstable for large steps in the reference signal. The remaining four controllers were all selected for simulation on the real hardware system. Unfortunately the MPC was never successfully implemented on the hardware due to some unknown compatibility error and hence eliminated as a possible candidate. The three remaining control strategies, PID, PID with feed forward from reference and the LQ controller, were all successfully implemented and simulated on hardware. The results from the hardware simulations compared to simulations made with the old controller, as well as the results from the software simulations, were then evaluated. Depending on the purpose one of two control strategies is recommended for this application. The LQ controller achieved the best overall performance and is presented as the control strategy best suited for this application.

The purpose of this thesis is to investigate different control strategies on a differential drive vehicle. The vehicle should be able to drive in turns at high speed and slowly when it should park next to a charger. In both these cases, good precision in both orientation and distance to the path is important. A PID and an LQ controller have been implemented for this purpose. The two controllers were first implemented in a simulation environment. After implementing the controllers on the system itself, tests to evaluate the controllers were made to imitate real-life situations. This includes tests regarding driving with different speeds in different turns, tests with load distributions, and tests with stopping accuracy. The existing controller on the system was also tested and compared to the new controllers. After evaluating the controllers, it was stated that the existing controller was the most robust. It was not affected much by the load distribution compared to the new controllers. However, the LQ controller was slightly better in most cases, even though it was highly affected by the load distribution. The PID controller performed best regarding stopping accuracy but was the least robust controller by the three. Since the existing controller has a similar performance as the LQ controller but is more robust, the existing controller was chosen as the best one.

The digitization of classical control systems presents a number of challenges and opportunities with respect to the miniaturization, distribution, reliability verification and obsolescence of both the controller and the underlying system under control. A method for the design of proportional-integral-derivative (PID) compensators realized in the form of all-digital components is presented. All-digital refers to a system implementation that is realizable with a wide range of digital logic components including discrete digital logic elements and programmable logic devices (PLDs) such as field-programmable gate arrays. The proportional, integral and derivative components of the classical PID control law were re-envisioned in terms of frequency of occurrences or counts for adaptation to combinatorial and sequential digital logic. Modification of the control scheme around this newly formed representation of system error enables the development of a PID-like FPGA-based or PLD-based controller. Details of the design of an all-digital PID-like controller including abstract, causal block diagrams and a MATLAB® and Simulink® based implementation are presented. The compensator was simulated in a velocity tracking DC motor control application and was found to perform comparably to that of a classical PID based control. Methods for assessing the resultant stability of an all-digital PID compensated system under control are discussed.

High blood pressure, also called hypertension is one of the most common worldwide diseases afflicting humans and is a major risk factor for stroke, myocardial infarction, vascular disease, and chronic kidney disease. If blood pressure is controlled and oscillations in the hemodynamic variables are reduced, patients experience fewer complications after surgery. In clinical practice, this is usually achieved using manual drug delivery. Given that different patients have different sensitivity and reaction time to drugs, determining manually the right drug infusion rates may be difficult. This is a problem where automatic drug delivery can provide a solution, especially if it is designed to adapt to variations in the patient’s conditions. This research work presents an investigation into the development of abnormal blood pressure (hypertension) controllers for postoperative patients. Control of the drugs infusion rates is used to simultaneously regulate the hemodynamic variables such as the Mean Arterial Pressure (MAP) and the Cardiac Output (CO) at the desired level. The implementation of optimal control system is very essential to improve the quality of patient care and also to reduce the workload of healthcare staff and costs. Many researchers have conducted studies earlier on modelling and/or control of abnormal blood pressure for postoperative patients. However, there are still many concerns about smooth transition of blood pressure without any side effect. The blood pressure is classified in two categories: high blood pressure (Hypertension) and low blood pressure (Hypotension). The hypertension often occurred after cardiac surgery, and the hypotension occurred during cardiac surgery. To achieve the optimal control solution for these abnormal blood pressures, many methods are proposed, one of the common methods is infusing the drug related to blood pressure to maintain it at the desired level. There are several kinds of vasodilating drugs such as Sodium Nitroprusside (SNP), Dopamine (DPM), Nitro-glycerine (NTG), and so on, which can be used to treat postoperative patients, also used for hypertensive emergencies to keep the blood pressure at safety level. A comparative performance of two types of algorithms has been presented in chapter four. These include the Internal Model Control (IMC), and Proportional-Integral-Derivative (PID) controller. The resulting controllers are implemented, tested and verified for three sensitivity patient response. SNP is used for all three patients’ situation in order to reduce the pressure smoothly and maintain it at the desire level. A Genetic Algorithms (GAs) optimization technique has been implemented to optimise the controllers’ parameters. A set of experiments are presented to demonstrate the merits and capabilities of the control algorithms. The simulation results in chapter four have demonstrated that the performance criteria are satisfied with the IMC, and PID controllers. On the other hand, the settling time for the PID control of all three patients’ response is shorter than the settling time with IMC controller. Using multiple interacting drugs to control both the MAP and CO of patients with different sensitivity to drugs is a challenging task. A Multivariable Model Reference Adaptive Control (MMRAC) algorithm is developed using a two-input, two-output patient model. Because of the difference in patient’s sensitivity to the drug, and in order to cover the wide ranges of patients, Model Reference Adaptive Control (MRAC) has been implemented to obtain the optimal infusion rates of DPM and SNP. This is developed in chapters five and six. Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters, the simulation results have demonstrated that this algorithm cannot cover the wide range of patient’s sensitivity to drugs, due to that shortcoming, a PID controller using a Neural Network that tunes the controller parameters was designed and implemented. The parameters of the PID controller were optimised offline using Matlab genetic algorithm. The proposed Neuro-PID controller has been tested and validated to demonstrate its merits and capabilities compared to the existing approaches to cover wide range of patients.

High blood pressure, also called hypertension is one of the most common worldwide diseases afflicting humans and is a major risk factor for stroke, myocardial infarction, vascular disease, and chronic kidney disease. If blood pressure is controlled and oscillations in the hemodynamic variables are reduced, patients experience fewer complications after surgery. In clinical practice, this is usually achieved using manual drug delivery. Given that different patients have different sensitivity and reaction time to drugs, determining manually the right drug infusion rates may be difficult. This is a problem where automatic drug delivery can provide a solution, especially if it is designed to adapt to variations in the patient’s conditions. This research work presents an investigation into the development of abnormal blood pressure (hypertension) controllers for postoperative patients. Control of the drugs infusion rates is used to simultaneously regulate the hemodynamic variables such as the Mean Arterial Pressure (MAP) and the Cardiac Output (CO) at the desired level. The implementation of optimal control system is very essential to improve the quality of patient care and also to reduce the workload of healthcare staff and costs. Many researchers have conducted studies earlier on modelling and/or control of abnormal blood pressure for postoperative patients. However, there are still many concerns about smooth transition of blood pressure without any side effect. The blood pressure is classified in two categories: high blood pressure (Hypertension) and low blood pressure (Hypotension). The hypertension often occurred after cardiac surgery, and the hypotension occurred during cardiac surgery. To achieve the optimal control solution for these abnormal blood pressures, many methods are proposed, one of the common methods is infusing the drug related to blood pressure to maintain it at the desired level. There are several kinds of vasodilating drugs such as Sodium Nitroprusside (SNP), Dopamine (DPM), Nitro-glycerine (NTG), and so on, which can be used to treat postoperative patients, also used for hypertensive emergencies to keep the blood pressure at safety level. A comparative performance of two types of algorithms has been presented in chapter four. These include the Internal Model Control (IMC), and Proportional-Integral-Derivative (PID) controller. The resulting controllers are implemented, tested and verified for three sensitivity patient response. SNP is used for all three patients’ situation in order to reduce the pressure smoothly and maintain it at the desire level. A Genetic Algorithms (GAs) optimization technique has been implemented to optimise the controllers’ parameters. A set of experiments are presented to demonstrate the merits and capabilities of the control algorithms. The simulation results in chapter four have demonstrated that the performance criteria are satisfied with the IMC, and PID controllers. On the other hand, the settling time for the PID control of all three patients’ response is shorter than the settling time with IMC controller. Using multiple interacting drugs to control both the MAP and CO of patients with different sensitivity to drugs is a challenging task. A Multivariable Model Reference Adaptive Control (MMRAC) algorithm is developed using a two-input, two-output patient model. Because of the difference in patient’s sensitivity to the drug, and in order to cover the wide ranges of patients, Model Reference Adaptive Control (MRAC) has been implemented to obtain the optimal infusion rates of DPM and SNP. This is developed in chapters five and six. Computer simulations were carried out to investigate the performance of this controller. The results show that the proposed adaptive scheme is robust with respect to disturbances and variations in model parameters, the simulation results have demonstrated that this algorithm cannot cover the wide range of patient’s sensitivity to drugs, due to that shortcoming, a PID controller using a Neural Network that tunes the controller parameters was designed and implemented. The parameters of the PID controller were optimised offline using Matlab genetic algorithm. The proposed Neuro-PID controller has been tested and validated to demonstrate its merits and capabilities compared to the existing approaches to cover wide range of patients. / Libyan Ministry of Higher Education scholarship

No description available.

Management and supply of water in an urban water distribution system is a complex process, which include various complexities like pressure variations across the network depending on topography, demand variations depending on customers’ requirement and unaccounted water etc. Applying automatic control methods to water distribution systems is a way to improve the management of water distribution. There have been some attempts in recent years to develop optimal control algorithms to assist in the operation of complex water distribution systems. The difficulties involved by these hydraulic systems such as non-linearity, and diurnal demand patterns make the choice of a suitable automatic control method a challenge. For this purpose, this study intends to investigate the applicability of different controllers which would be able to meet the targets as quickly as possible and without creating undue transients. As a first step towards application of different controllers, PD and PID linear controllers have been designed for pump control and valve control in water distribution systems. Then a Dynamic Inversion based nonlinear controller has been designed by considering the non-linearities in the system. Here, different cases considering the effects of initial conditions used, linearization methods used, time step used for integration and selection of gains etc., have been studied before arriving at best controller. These controllers have been designed for both the flow control problems and level control problems. It is found that Dynamic Inversion-based nonlinear controller outperforms other controllers. It is well known that the performance of controllers is much dependent on the tuning of the gains (parameters). Thus in this study various alternative techniques such as Ziegler--Nichols rules (ZNPID), Genetic algorithms (GAPID) and fuzzy algorithms (FZPID) have been studied and a comparative study has been made Although with all the three gain tuning methods, required states have reached their target values, but the responses vary much in reaching to final targets. The self-tuned FZPID controller outperforms other two controllers, especially with regard to overshoots and the time taken to tune the gains for each problem. Further, an optimal DI controller is developed for the over determined case with more controls and less targets. Energy loss is considered as an objective function and normal DI controller equations are considered as constraints. Hence, an attempt is made to reduce the energy minimization in water distribution system by formulating an optimal control problem using optimal Dynamic Inversion concept. Finally, leakage reduction model is developed based on excessive pressure minimization problem by locating valves optimally as well as by setting valves optimally. For this purpose, optimization problem is solved using Pattern search algorithms and hydraulic analysis is carried out using EPANET program.

The PID controller is regarded as a dependable and reliable controller for process industry systems. Many researchers have devoted time and attention to PID controller tuning and they all agree that PID controllers are very important for control systems. A PID equation is very sensitive; its parameters must always be varied following the specific application to increase performance, such as by increasing the system’s responsiveness. PID controllers still have many problems despite their importance for control systems in industries. The problem of big overshoot on the conventional gain tuning is one of the serious problems. Researchers use the PSO algorithm to try and overcome those problems. The tuning of the MIMO PID controller based on the PSO algorithm shows many disadvantages such as high-quality control with a short settle time, steady-state error, and periodical step response. The traditional PSO algorithm is very sensitive and it sometimes affects the quality of good PID controller tuning. This research has proposed a new equation for improving the PSO algorithm. The proposed algorithm is the combination of linearly decreasing inertia weight and chaotic inertia weight, after which a control factor was introduced as an exponential factor. This was very useful for simulations as it is adjustable. The Matlab simulation results of the experiments show that the simulations as it is adjustable. The Matlab simulation results of the experiments show that the new proposed equation converges faster and it gives the best fitness compared to linear inertia weight and oscillating inertia weight and other old equations. The MIMO PID controller system that consists of four plants was tuned based on the new proposed equation for the PSO algorithm (LCPSO). The optimized results show the best rise time, settling time, time delays, and steady-state compared to the systems that are tuned using the old equations. The exploration was directed at considering the impact of using the PSO calculation as an instrument for MIMO PID tuning. The results obtained in the examination reveal that the PSO tuning output improved reactions and can be applied to various system models in the measure control industry. The results for the MIMO PID controller tuned using PSO were assessed using integral square error (ISE), integral absolute error (IAE), and the integral of time expanded by absolute error (ITAE). The five well-known benchmark functions were also used to endorse the feasibility of the improved PSO and excellent results in terms of convergence and best fitness were attained. / Electrical and Mining Engineering / M. Tech. (Electrical Engineering)

No description available.