Design and practical implementation of a fractional order proportional integral controller (FOPI) for a poorly damped fractional order process with time delay

One of the most popular tuning procedures for the development of fractional order controllers is by imposing frequency domain constraints such as gain crossover frequency, phase margin and iso-damping properties. The present study extends the frequency domain tuning methodology to a generalized range of fractional order processes based on second order plus time delay (SOPDT) models. A fractional order PI controller is tuned for a real process that exhibits poorly damped dynamics characterized in terms of a fractional order transfer function with time delay. The obtained controller is validated on the experimental platform by analyzing staircase reference tracking, input disturbance rejection and robustness to process uncertainties. The paper focuses around the tuning methodology as well as the fractional order modeling of the process' dynamics

Vibration reduction for vision systems on board unmanned aerial vehicles using a neuro-fuzzy controller

In this paper, an intelligent control approach based on neuro-fuzzy systems performance is presented, with the objective of counteracting the vibrations that affect the low-cost vision platform onboard an unmanned aerial system of rotating nature. A scaled dynamical model of a helicopter is used to simulate vibrations on its fuselage. The impact of these vibrations on the low-cost vision system will be assessed and an intelligent control approach will be derived in order to reduce its detrimental influence. Different trials that consider a neuro-fuzzy approach as a fundamental part of an intelligent semi-active control strategy have been carried out. Satisfactory results have been achieved compared to those obtained by means of vibration reduction passive techniques

A survey on fractional order control techniques for unmanned aerial and ground vehicles

In recent years, numerous applications of science and engineering for modeling and control of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) systems based on fractional calculus have been realized. The extra fractional order derivative terms allow to optimizing the performance of the systems. The review presented in this paper focuses on the control problems of the UAVs and UGVs that have been addressed by the fractional order techniques over the last decade

Diseño de un controlador LQG para un helicóptero de tres grados de libertad

This paper presents the design of a linear quadratic gaussian (LQG) controller to regulate the pitch, elevation and travel angles of a helicopter with three degrees of freedom (3 DOF). The LQG controller was designed on the basis of a linear model and the regulator parameters were adjusted based on the simulations performed with the non-linear model, evaluating the transient response of the system in the closed loop for different values of the reference signal, guaranteeing obtain the greatest variation of the angles around the equilibrium point. The experimental validation was done using a prototype built by the authors; the transient response of the simulated data was compared against the experimental data, for the three degrees of freedom of the helicopter, observing that the mathematical model adjusts to the dynamic of the prototype and the conditions of design were fulfilled.El artículo presenta el diseño de un controlador lineal cuadrático gaussiano (LQG) para regular los ángulos de cabeceo, elevación y viaje de un prototipo de un helicóptero de tres grados de libertad (3 DOF). El controlador LQG se diseña a partir de un modelo lineal y se ajustan los parámetros del regulador en base a simulaciones realizadas con el modelo no lineal, evaluando la respuesta transitoria del sistema en lazo cerrado para diferentes valores de la señal de referencia, garantizando obtener la mayor variación de los ángulos alrededor del punto de equilibrio. La validación experimental de la estrategia de control se realiza sobre un prototipo construido por los autores, la respuesta transitoria de los datos simulados se compara con los datos experimentales, para los tres grados de libertad del helicóptero, observando que el modelo matemático se ajusta a la dinámica del prototipo y se cumplen las condiciones de diseño.El artículo presenta el diseño de un controlador lineal cuadrático gaussiano (LQG) para regular los ángulos de elevación, cabeceo y viaje de un prototipo de un helicóptero de tres grados de libertad (3 DOF). El controlador LQG se ajusta en base del modelo matemático no lineal y se evalúa la respuesta transitoria del sistema en lazo cerrado para diferentes valores de la señal de referencia, ajustando los parámetros del controlador con el objetivo de estabilizar el sistema, disminuir el sobrepaso y el tiempo de establecimiento. La validación experimental de la estrategia de control se realiza sobre un prototipo construido por los autores, la respuesta transitoria de los datos simulados se compara con los datos experimentales, para los tres grados de libertad del helicóptero, observando que el modelo matemático se ajusta a la dinámica del prototipo y se cumplen las condiciones de diseño.This paper presents the design of a linear quadratic gaussian (LQG) controller to regulate the pitch, elevation and travel angles of a helicopter with three degrees of freedom (3 DOF). The LQG controller was designed on the basis of a linear model and the regulator parameters were adjusted based on the simulations performed with the non-linear model, evaluating the transient response of the system in the closed loop for different values of the reference signal, guaranteeing obtain the greatest variation of the angles around the equilibrium point. The experimental validation was done using a prototype built by the authors; the transient response of the simulated data was compared against the experimental data, for the three degrees of freedom of the helicopter, observing that the mathematical model adjusts to the dynamic of the prototype and the conditions of design were fulfilled

A novel approach to the control of quad-rotor helicopters using fuzzy-neural networks

Quad-rotor helicopters are agile aircraft which are lifted and propelled by four rotors. Unlike traditional helicopters, they do not require a tail-rotor to control yaw, but can use four smaller fixed-pitch rotors. However, without an intelligent control system it is very difficult for a human to successfully fly and manoeuvre such a vehicle. Thus, most of recent research has focused on small unmanned aerial vehicles, such that advanced embedded control systems could be developed to control these aircrafts. Vehicles of this nature are very useful when it comes to situations that require unmanned operations, for instance performing tasks in dangerous and/or inaccessible environments that could put human lives at risk. This research demonstrates a consistent way of developing a robust adaptive controller for quad-rotor helicopters, using fuzzy-neural networks; creating an intelligent system that is able to monitor and control the non-linear multi-variable flying states of the quad-rotor, enabling it to adapt to the changing environmental situations and learn from past missions. Firstly, an analytical dynamic model of the quad-rotor helicopter was developed and simulated using Matlab/Simulink software, where the behaviour of the quad-rotor helicopter was assessed due to voltage excitation. Secondly, a 3-D model with the same parameter values as that of the analytical dynamic model was developed using Solidworks software. Computational Fluid Dynamics (CFD) was then used to simulate and analyse the effects of the external disturbance on the control and performance of the quad-rotor helicopter. Verification and validation of the two models were carried out by comparing the simulation results with real flight experiment results. The need for more reliable and accurate simulation data led to the development of a neural network error compensation system, which was embedded in the simulation system to correct the minor discrepancies found between the simulation and experiment results. Data obtained from the simulations were then used to train a fuzzy-neural system, made up of a hierarchy of controllers to control the attitude and position of the quad-rotor helicopter. The success of the project was measured against the quad-rotor’s ability to adapt to wind speeds of different magnitudes and directions by re-arranging the speeds of the rotors to compensate for any disturbance. From the simulation results, the fuzzy-neural controller is sufficient to achieve attitude and position control of the quad-rotor helicopter in different weather conditions, paving way for future real time applications

Advanced Feedback Linearization Control for Tiltrotor UAVs: Gait Plan, Controller Design, and Stability Analysis

Three challenges, however, can hinder the application of Feedback Linearization: over-intensive control signals, singular decoupling matrix, and saturation. Activating any of these three issues can challenge the stability proof. To solve these three challenges, first, this research proposed the drone gait plan. The gait plan was initially used to figure out the control problems in quadruped (four-legged) robots; applying this approach, accompanied by Feedback Linearization, the quality of the control signals was enhanced. Then, we proposed the concept of unacceptable attitude curves, which are not allowed for the tiltrotor to travel to. The Two Color Map Theorem was subsequently established to enlarge the supported attitude for the tiltrotor. These theories were employed in the tiltrotor tracking problem with different references. Notable improvements in the control signals were witnessed in the tiltrotor simulator. Finally, we explored the control theory, the stability proof of the novel mobile robot (tilt vehicle) stabilized by Feedback Linearization with saturation. Instead of adopting the tiltrotor model, which is over-complicated, we designed a conceptual mobile robot (tilt-car) to analyze the stability proof. The stability proof (stable in the sense of Lyapunov) was found for a mobile robot (tilt vehicle) controlled by Feedback Linearization with saturation for the first time. The success tracking result with the promising control signals in the tiltrotor simulator demonstrates the advances of our control method. Also, the Lyapunov candidate and the tracking result in the mobile robot (tilt-car) simulator confirm our deductions of the stability proof. These results reveal that these three challenges in Feedback Linearization are solved, to some extents.Comment: Doctoral Thesis at The University of Toky

Methods of system identification, parameter estimation and optimisation applied to problems of modelling and control in engineering and physiology

Mathematical and computer-based models provide the foundation of most methods of engineering design. They are recognised as being especially important in the development of integrated dynamic systems, such as “control-configured” aircraft or in complex robotics applications. These models usually involve combinations of linear or nonlinear ordinary differential equations or difference equations, partial differential equations and algebraic equations. In some cases models may be based on differential algebraic equations. Dynamic models are also important in many other fields of research, including physiology where the highly integrated nature of biological control systems is starting to be more fully understood. Although many models may be developed using physical, chemical, or biological principles in the initial stages, the use of experimentation is important for checking the significance of underlying assumptions or simplifications and also for estimating appropriate sets of parameters. This experimental approach to modelling is also of central importance in establishing the suitability, or otherwise, of a given model for an intended application – the so-called “model validation” problem. System identification, which is the broad term used to describe the processes of experimental modelling, is generally considered to be a mature field and classical methods of identification involve linear discrete-time models within a stochastic framework. The aspects of the research described in this thesis that relate to applications of identification, parameter estimation and optimisation techniques for model development and model validation mainly involve nonlinear continuous time models Experimentally-based models of this kind have been used very successfully in the course of the research described in this thesis very in two areas of physiological research and in a number of different engineering applications. In terms of optimisation problems, the design, experimental tuning and performance evaluation of nonlinear control systems has much in common with the use of optimisation techniques within the model development process and it is therefore helpful to consider these two areas together. The work described in the thesis is strongly applications oriented. Many similarities have been found in applying modelling and control techniques to problems arising in fields that appear very different. For example, the areas of neurophysiology, respiratory gas exchange processes, electro-optic sensor systems, helicopter flight-control, hydro-electric power generation and surface ship or underwater vehicles appear to have little in common. However, closer examination shows that they have many similarities in terms of the types of problem that are presented, both in modelling and in system design. In addition to nonlinear behaviour; most models of these systems involve significant uncertainties or require important simplifications if the model is to be used in a real-time application such as automatic control. One recurring theme, that is important both in the modelling work described and for control applications, is the additional insight that can be gained through the dual use of time-domain and frequency-domain information. One example of this is the importance of coherence information in establishing the existence of linear or nonlinear relationships between variables and this has proved to be valuable in the experimental investigation of neuromuscular systems and in the identification of helicopter models from flight test data. Frequency-domain techniques have also proved useful for the reduction of high-order multi-input multi-output models. Another important theme that has appeared both within the modelling applications and in research on nonlinear control system design methods, relates to the problems of optimisation in cases where the associated response surface has many local optima. Finding the global optimum in practical applications presents major difficulties and much emphasis has been placed on evolutionary methods of optimisation (both genetic algorithms and genetic programming) in providing usable methods for optimisation in design and in complex nonlinear modelling applications that do not involve real-time problems. Another topic, considered both in the context of system modelling and control, is parameter sensitivity analysis and it has been found that insight gained from sensitivity information can be of value not only in the development of system models (e.g. through investigation of model robustness and the design of appropriate test inputs), but also in feedback system design and in controller tuning. A technique has been developed based on sensitivity analysis for the semi-automatic tuning of cascade and feedback controllers for multi-input multi-output feedback control systems. This tuning technique has been applied successfully to several problems. Inverse systems also receive significant attention in the thesis. These systems have provided a basis for theoretical research in the control systems field over the past two decades and some significant applications have been reported, despite the inherent difficulties in the mathematical methods needed for the nonlinear case. Inverse simulation methods, developed initially by others for use in handling-qualities studies for fixed-wing aircraft and helicopters, are shown in the thesis to provide some important potential benefits in control applications compared with classical methods of inversion. New developments in terms of methodology are presented in terms of a novel sensitivity based approach to inverse simulation that has advantages in terms of numerical accuracy and a new search-based optimisation technique based on the Nelder-Mead algorithm that can handle inverse simulation problems involving hard nonlinearities. Engineering applications of inverse simulation are presented, some of which involve helicopter flight control applications while others are concerned with feed-forward controllers for ship steering systems. The methods of search-based optimisation show some important advantages over conventional gradient-based methods, especially in cases where saturation and other nonlinearities are significant. The final discussion section takes the form of a critical evaluation of results obtained using the chosen methods of system identification, parameter estimation and optimisation for the modelling and control applications considered. Areas of success are highlighted and situations are identified where currently available techniques have important limitations. The benefits of an inter-disciplinary and applications-oriented approach to problems of modelling and control are also discussed and the value in terms of cross-fertilisation of ideas resulting from involvement in a wide range of applications is emphasised. Areas for further research are discussed

Quadcopter: Design, modelling, control and trajectory tracking

A quadcopter is a type of unmanned aerial vehicles (UAV). The industry of this type of UAVs is growing exponentially in terms of new technology development and the increase of potential applications that may cover construction inspections, search and rescue, surveillance, aerial photography, monitoring, mapping, etc. A quadcopter is a nonlinear and under-actuated system that introduces complex aerodynamics properties and create challenges which demands the development of new, reliable and effective control techniques to enhance the stability of flight control, plan and track a desired trajectory while minimizing the effect induced by the operational environment and its own sensors. Hence, many control techniques have been developed and researched. Some of such developments work well with the provision of having an accurate mathematical model of the system while other work is associated with a mathematical model that can accommodate certain level of wind disturbances and uncertainties related to measurement noise. Moreover, various linear, nonlinear and intelligent control techniques were developed and recognized in the literature. Each one of such control techniques has some aspect that excels in under certain conditions. The focus of this thesis is to develop different control techniques that can improve flight control stability, trajectory tracking of a quadcopter and evaluate their performance to select the best suitable control technique that can realize the stated technical flight control requirements. Accordingly, three main techniques have been developed: Standard PID, Fuzzy based control technique that tune PID parameters in real time (FPID) and a Hybrid control strategy that consists of three control techniques: (a) FPID with state coordinates transformation (b) State feedback (c) Sliding mode The configuration of the hybrid control strategy consists of two control loops. The inner control loop aims to control the quadcopter\u27s attitude and altitude while the outer control loop aims to control the quadcopter\u27s position. Two configurations were used to configure the developed control techniques of the control loops. These configurations are: (a) A sliding mode control is used for the outer loop while for the inner loop two control techniques are used to realize it: a Fuzzy gain scheduled PID with state coordinates transformation and a state feedback control. (b) Fuzzy gain scheduled PID control is used for the outer loop while for the inner loop two control techniques are used to realize it using the same formation as in (a) above. Furthermore, in order to ensure a feasible desired trajectory before tracking it, a trajectory planning algorithm has been developed and tested successfully. Subsequently, a simulation testing environment with friendly graphical User Interface (GUI) has been developed to simulate the quadcopter mathematical model and then to use it as a test bed to validate the developed control techniques with and without the effect of wind disturbance and measurement noise. The quadcopter with each control technique has been tested using the simulation environment under different operational conditions. The results in terms of tracking a desired trajectory shows the robustness of the first configuration of control techniques within the hybrid control strategy under the presence of wind disturbance and measurement noise compared to all the other techniques developed. Then, the second configuration of the control techniques came second in terms of results quality. The third and fourth results in the sequence shown by the fuzzy scheduled PID and the standard PID respectively. Finally, Validating the simulation results on a real system, a quadcopter has been successfully designed, implemented and tested. The developed control techniques were tested using the implemented quadcopter and the results were demonstrated and compared with the simulation results

Design and Implementation of an Artificial Neural Network Controller for Quadrotor Flight in Confined Environment

Quadrotors offer practical solutions for many applications, such as emergency rescue, surveillance, military operations, videography and many more. For this reason, they have recently attracted the attention of research and industry. Even though they have been intensively studied, quadrotors still suffer from some challenges that limit their use, such as trajectory measurement, attitude estimation, obstacle avoidance, safety precautions, and land cybersecurity. One major problem is flying in a confined environment, such as closed buildings and tunnels, where the aerodynamics around the quadrotor are affected by close proximity objects, which result in tracking performance deterioration, and sometimes instability. To address this problem, researchers followed three different approaches; the Modeling approach, which focuses on the development of a precise dynamical model that accounts for the different aerodynamic effects, the Sensor Integration approach, which focuses on the addition of multiple sensors to the quadrotor and applying algorithms to stabilize the quadrotor based on their measurements, and the Controller Design approach, which focuses on the development of an adaptive and robust controller. In this research, a learning controller is proposed as a solution for the issue of quadrotor trajectory control in confined environments. This controller utilizes Artificial Neural Networks to adjust for the unknown aerodynamics on-line. A systematic approach for controller design is developed, so that, the approach could be followed for the development of controllers for other nonlinear systems of similar form. One goal for this research is to develop a global controller that could be applied to any quadrotor with minimal adjustment. A novel Artificial Neural Network structure is presented that increases learning efficiency and speed. In addition, a new learning algorithm is developed for the Artificial Neural Network, when utilized with the developed controller. Simulation results for the designed controller when applied to the Qball-X4 quadrotor are presented that show the effectiveness of the proposed Artificial Neural Network structure and the developed learning algorithm in the presence of variety of different unknown aerodynamics. These results are confirmed with real time experimentation, as the developed controller was successfully applied to Quanser’s Qball-X4 quadrotor for the flight control in confined environment. The practical challenges associated with the application of such a controller for quadrotor flight in confined environment are analyzed and adequately resolved to achieve an acceptable tracking performance

Development of Fault Diagnosis and Fault Tolerant Control Algorithms with Application to Unmanned Systems

Unmanned vehicles have been increasingly employed in real life. They include unmanned air vehicles (UAVs), unmanned ground vehicles (UGVs), unmanned spacecrafts, and unmanned underwater vehicles (UUVs). Unmanned vehicles like any other autonomous systems need controllers to stabilize and control them. On the other hand unmanned systems might subject to different faults. Detecting a fault, finding the location and severity of it, are crucial for unmanned vehicles. Having enough information about a fault, it is needed to redesign controller based on post fault characteristics of the system. The obtained controlled system in this case can tolerate the fault and may have a better performance. The main focus of this thesis is to develop Fault Detection and Diagnosis (FDD) algorithms, and Fault Tolerant Controllers (FTC) to increase performance, safety and reliability of various missions using unmanned systems. In the field of unmanned ground vehicles, a new kinematical control method has been proposed for the trajectory tracking of nonholonomic Wheeled Mobile Robots (MWRs). It has been experimentally tested on an UGV, called Qbot. A stable leader-follower formation controller for time-varying formation configuration of multiple nonholonomic wheeled mobile robots has also been presented and is examined through computer simulation. In the field of unmanned aerial vehicles, Two-Stage Kalman Filter (TSKF), Adaptive Two-Stage Kalman Filter (ATSKF), and Interacting Multiple Model (IMM) filter were proposed for FDD of the quadrotor helicopter testbed in the presence of actuator faults. As for space missions, an FDD algorithm for the attitude control system of the Japan Canada Joint Collaboration Satellite - Formation Flying (JC2Sat-FF) mission has been developed. The FDD scheme was achieved using an IMM-based FDD algorithm. The efficiency of the FDD algorithm has been shown through simulation results in a nonlinear simulator of the JC2Sat-FF. A fault tolerant fuzzy gain-scheduled PID controller has also been designed for a quadrotor unmanned helicopter in the presence of actuator faults. The developed FDD algorithms and fuzzy controller were evaluated through experimental application to a quadrotor helicopter testbed called Qball-X4