Development of Robust Control Strategies for Autonomous Underwater Vehicles

The resources of the energy and chemical balance in the ocean sustain mankind in many ways. Therefore, ocean exploration is an essential task that is accomplished by deploying Underwater Vehicles. An Underwater Vehicle with autonomy feature for its navigation and control is called Autonomous Underwater Vehicle (AUV). Among the task handled by an AUV, accurately positioning itself at a desired position with respect to the reference objects is called set-point control. Similarly, tracking of the reference trajectory is also another important task. Battery recharging of AUV, positioning with respect to underwater structure, cable, seabed, tracking of reference trajectory with desired accuracy and speed to avoid collision with the guiding vehicle in the last phase of docking are some significant applications where an AUV needs to perform the above tasks. Parametric uncertainties in AUV dynamics and actuator torque limitation necessitate to design robust control algorithms to achieve motion control objectives in the face of uncertainties. Sliding Mode Controller (SMC), H / μ synthesis, model based PID group controllers are some of the robust controllers which have been applied to AUV. But SMC suffers from less efficient tuning of its switching gains due to model parameters and noisy estimated acceleration states appearing in its control law. In addition, demand of high control effort due to high frequency chattering is another drawback of SMC. Furthermore, real-time implementation of H / μ synthesis controller based on its stability study is restricted due to use of linearly approximated dynamic model of an AUV, which hinders achieving robustness. Moreover, model based PID group controllers suffer from implementation complexities and exhibit poor transient and steady-state performances under parametric uncertainties. On the other hand model free Linear PID (LPID) has inherent problem of narrow convergence region, i.e.it can not ensure convergence of large initial error to zero. Additionally, it suffers from integrator-wind-up and subsequent saturation of actuator during the occurrence of large initial error. But LPID controller has inherent capability to cope up with the uncertainties. In view of addressing the above said problem, this work proposes wind-up free Nonlinear PID with Bounded Integral (BI) and Bounded Derivative (BD) for set-point control and combination of continuous SMC with Nonlinear PID with BI and BD namely SM-N-PID with BI and BD for trajectory tracking. Nonlinear functions are used for all P,I and D controllers (for both of set-point and tracking control) in addition to use of nonlinear tan hyperbolic function in SMC(for tracking only) such that torque demand from the controller can be kept within a limit. A direct Lyapunov analysis is pursued to prove stable motion of AUV. The efficacies of the proposed controllers are compared with other two controllers namely PD and N-PID without BI and BD for set-point control and PD plus Feedforward Compensation (FC) and SM-NPID without BI and BD for tracking control. Multiple AUVs cooperatively performing a mission offers several advantages over a single AUV in a non-cooperative manner; such as reliability and increased work efficiency, etc. Bandwidth limitation in acoustic medium possess challenges in designing cooperative motion control algorithm for multiple AUVs owing to the necessity of communication of sensors and actuator signals among AUVs. In literature, undirected graph based approach is used for control design under communication constraints and thus it is not suitable for large number of AUVs participating in a cooperative motion plan. Formation control is a popular cooperative motion control paradigm. This thesis models the formation as a minimally persistent directed graph and proposes control schemes for maintaining the distance constraints during the course of motion of entire formation. For formation control each AUV uses Sliding Mode Nonlinear PID controller with Bounded Integrator and Bounded Derivative. Direct Lyapunov stability analysis in the framework of input-to-state stability ensures the stable motion of formation while maintaining the desired distance constraints among the AUVs

Ship Course Keeping Using Different Sliding Mode Controllers

This study addresses three sliding mode heading controllers for dealing with uncertain wave disturbances. A nonlinear steering model is derived, and the feedback linearization method is chosen to simplify the nonlinear system in this study. The adaptive method and disturbance observer technique are proposed for course keeping and ensuring robust performance of the time varying wave moment and actuator dynamics. Finally, the simulation results on a navy ship illustrate the effectiveness of the presented control algorithms for course keeping

Application of MPC and sliding mode control to IFAC benchmark models

The comparison of Model Predictive Control (MPC) and Sliding Mode Control (SMC) are presented in this paper. This paper investigates the performance of each controller as the navigation system for IFAC benchmark ship models (cargo vessel and oil tanker). In this investigation the navigation system regulates the heading angle of the two types of marine vessel with reference to a desired heading trajectory. In this investigation, the result obtained from MPC is compared with a well-established control methodology, namely Sliding Mode control theory. Wave disturbances and actuator limits are implemented to provide a more realistic evaluation and comparison for the proposed control structure

통합형 무인 수상선-케이블-수중선 시스템의 다물체동역학 거동 및 제어

Underwater exploration is becoming more and more important, since a vast range of unknown resources in the deep ocean remain undeveloped. This dissertation thus presents a modeling of the coupled dynamics of an Unmanned Surface Vehicle (USV) system with an Underwater Vehicles (UV) connected by an underwater cable (UC). The complexity of this multi-body dynamics system and ocean environments is very difficult to model. First, for modeling this, dynamics analysis was performed on each subsystem and further total coupled system dynamics were studied. The UV which is towed by a UC is modeled with 6-DOF equations of motion that reflects its hydrodynamic characteristic was studied. The 4th-order Runge–Kutta numerical method was used to analyze the motion of the USV with its hydrodynamic coefficients which were obtained through experiments and from the literature. To analyze the effect of the UC, the complicated nonlinear and coupled UC dynamics under currents forces, the governing equations of the UC dynamics are established based on the catenary equation method, then it is solved by applying the shooting method. The new formulation and solution of the UC dynamics yields the three dimensional position and forces of the UC end point under the current forces. Also, the advantage of the proposed method is that the catenary equations using shooting method can be solved in real time such that the calculated position and forces of UC according to time can be directly utilized to calculate the UV motion. The proposed method offers advantages of simple formulation, convenient use, and fast calculation time with exact result. Some simple numerical simulations were conducted to observe the dynamic behaviors of AUV with cable effects. The simulations results clearly reveal that the UC can greatly influence the motions of the vehicles, especially on the UV motions. Based on both the numerical model and simulation results developed in the dissertation, we may offer some valuable information for the operation of the UV and USV. Secondly, for the design controller, a PD controller and its application to automatic berthing control of USV are also studied. For this, a nonlinear mathematical model for the maneuvering of USV in the presence of environmental forces was firstly established. Then, in order to control rudder and propeller during automatic berthing process, a PD control algorithm is applied. The algorithm consists of two parts, the forward velocity control and heading angle control. The control algorithm was designed based on the longitudinal and yaw dynamic models of USV. The desired heading angle was obtained by the so-called “Line of Sight” method. To support the validity of the proposed method, the computer simulations of automatic USV berthing are carried out. The results of simulation showed good performance of the developed berthing control system. Also, a hovering-type AUV equipped with multiple thrusters should maintain the specified position and orientation in order to perform given tasks by applying a dynamic positioning (DP) system. Besides, the control allocation algorithm based on a scaling factor is presented for distributing the forces required by the control law onto the available set of actuators in the most effective and energy efficient way. Thus, it is necessary for the robust control algorithm to conduct successfully given missions in spite of a model uncertainty and a disturbance. In this dissertation, the robust DP control algorithm based on a sliding mode theory is also addressed to guarantee the stability and better performance despite the model uncertainty and disturbance of current and cable effects. Finally, a series of simulations are conducted to verify the availability of the generated trajectories and performance of the designed robust controller. Thirdly, for the navigation of UV, a method for designing the path tracking controller using a Rapidly-exploring Random Trees (RRT) algorithm is proposed. The RRT algorithm is firstly used for the generation of collision free waypoints. Next, the unnecessary waypoints are removed by a simple path pruning algorithm generating a piecewise linear path. After that, a path smoothing algorithm utilizing cubic Bezier spiral curves to generate a continuous curvature path that satisfies the minimum radius of curvature constraint of underwater is implemented. The angle between two waypoints is the only information required for the generation of the continuous curvature path. In order to underwater vehicle follow the reference path, the path tracking controller using the global Sliding Mode Control (SMC) approach is designed. To verify the performance of the proposed algorithm, some simulation results are performed. Simulation results showed that the RRT algorithm could be applied to generate an optimal path in a complex ocean environment with multiple obstacles.Acknowledgement .................................................................................................. vi Abstract……. ....................................................................................... ………….viii Nomenclature ....................................................................................................... xvi List of Abbreviations ........................................................................................... xxi List of Tables ...................................................................................................... xxiii List of Figures ..................................................................................................... xxiv Chapter 1: Introduction ......................................................................................... 1 1.1 Background .................................................................................................. 1 1.1.1 Unmanned Surface Vehicles (USVs) ...................................................... 1 1.1.2 Umbilical Cable ....................................................................................... 4 1.1.3 Unmanned Underwater Vehicles (UUVs) ............................................... 5 1.1.4 Literature on Modeling of Marine Vehicles ............................................ 9 1.1.5 Literature on Control and Guidance of Marine Vehicles ...................... 11 1.2 Our System Architecture ........................................................................... 12 1.3 Motivation ................................................................................................. 13 1.4 Contribution ............................................................................................... 16 1.5 Publications Associated to the Dissertation .............................................. 17 1.6 Structure of the Dissertation ...................................................................... 18 Chapter 2: Mathematical Model of Unmanned Surface Vehicle (USV) ......... 20 2.1 Basic Assumptions .................................................................................... 20 2.2 Three Coordinate Systems ......................................................................... 20 2.3 Variable Notation ...................................................................................... 22 2.4 Kinematics ................................................................................................. 23 2.5 Kinetics ...................................................................................................... 26 2.5.1 Rigid Body Equations of Motion ........................................................... 26 2.5.2 Hydrodynamic Forces and Moments ..................................................... 28 2.5.3 Restoring Forces and Moments ............................................................. 31 2.5.4 Environmental Disturbances .................................................................. 32 2.5.5 Propulsion Forces and Moments ........................................................... 35 2.6 Nonlinear 6DOF Dynamics ....................................................................... 35 2.7 Mathematical Model of USV in 3 DOF .................................................... 36 2.7.1 Planar Kinematics .................................................................................. 36 2.7.2 Planar Nonlinear 3 DOF Dynamics ....................................................... 38 2.8 Configuration of Thrusters ........................................................................ 40 2.9 General Structure and Model Parameters .................................................. 41 2.9.1 Structure of USV ................................................................................... 41 2.9.2 Control System of USV ......................................................................... 42 2.9.3 Winch Control System ........................................................................... 43 Chapter 3: Mathematical Model of the Umbilical Cable (UC) ........................ 45 3.1 Basic Assumptions for UC ........................................................................ 45 3.2 Analysis on Forces of UV ......................................................................... 47 3.3 Relation for UC Equilibrium ..................................................................... 50 3.4 Catenary Equation in the Space Case ........................................................ 51 3.5 Shooting Method ....................................................................................... 55 3.6 Boundary Conditions ................................................................................. 57 3.7 Cable Effects ............................................................................................. 58 3.8 Model Parameters and Simulation ............................................................. 59 Chapter 4: Mathematical Model of Underwater Vehicle (UV) ........................ 63 4.1 Background ................................................................................................ 63 4.1.1 Basic Assumptions................................................................................. 63 4.1.2 Reference Frames .................................................................................. 64 4.1.3 Notations ................................................................................................ 65 4.2 Kinematics Equations ................................................................................ 66 4.3 Kinetic Equations ...................................................................................... 67 4.3.1 Rigid-Body Kinetics .............................................................................. 67 4.3.2 Hydrostatic Terms ................................................................................. 69 4.3.3 Hydrodynamic Terms ............................................................................ 70 4.3.4 Actuator Modeling ................................................................................. 75 4.3.5 Umbilical Cable Forces ......................................................................... 75 4.4 Nonlinear Equations of Motion (6DOF) ................................................... 76 4.5 Simplification of UV Dynamic Model ...................................................... 77 4.5.1 Simplifying the Mass and Inertia Matrix ............................................... 78 4.5.2 Simplifying the Hydrodynamic Damping Matrix.................................. 79 4.5.3 Simplifying the Gravitational and Buoyancy Vector ............................ 80 4.6 Thruster Modeling ..................................................................................... 80 4.7 Current Modeling ...................................................................................... 83 4.8 Dynamic Model Including Ocean Currents ............................................... 84 4.9 Complete Motion Equations of AUV (6DOF) .......................................... 89 4.10 Dynamics Model Parameter Identification ................................................ 91 4.11 Numerical Solution for Equations of Motion ............................................ 93 4.12 General Structure and Model Parameters .................................................. 94 4.12.1 Structure of AUV ............................................................................... 94 4.12.2 Control System of AUV ..................................................................... 96 Chapter 5: Guidance Theory ............................................................................... 97 5.1 Configuration of GNC System .................................................................. 97 5.1.1 Guidance ................................................................................................ 98 5.1.2 Navigation .............................................................................................. 98 5.1.3 Control ................................................................................................... 98 5.2 Maneuvering Problem Statement .............................................................. 99 5.3 Guidance Objectives ................................................................................ 100 5.3.1 Target Tracking ................................................................................... 100 5.3.2 Trajectory Tracking ............................................................................. 100 5.4 Waypoint Representation ........................................................................ 101 5.5 Path Following ......................................................................................... 102 5.6 Line of Sight (LOS) Waypoint Guidance ................................................ 102 5.6.1 Enclosure-Based Steering .................................................................... 104 5.6.2 Look-ahead Based Steering ................................................................. 105 5.6.3 LOS Control......................................................................................... 106 5.7 Cubic Polynomial for Path-Following ..................................................... 107 Chapter 6: Control Algorithm Design and Analysis ....................................... 110 6.1 Proportional Integral Differential (PID) Controller ................................ 110 6.1.1 General Theory .................................................................................... 110 6.1.2 Stability of General PID Controller ..................................................... 112 6.1.3 PID Tuning .......................................................................................... 114 6.1.4 Nonlinear PID for Marine Vehicles ..................................................... 116 6.1.5 Nonlinear PD for Marine Vehicles ...................................................... 117 6.1.6 Stability of Designed PD Controller .................................................... 117 6.2 Sliding Mode Controller .......................................................................... 118 6.2.1 Tracking Error and Sliding Surface ..................................................... 119 6.2.2 Chattering Situation ............................................................................. 120 6.2.3 Control Law and Stability .................................................................... 121 6.3 Allocation Control ................................................................................... 124 6.3.1 Linear Quadratic Unconstrained Control Allocation Using Lagrange Multipliers ................................................................................................ 125 6.3.2 Thruster Allocation with a Constrained Linear Model ........................ 127 6.4 Simulation Results and Discussion ......................................................... 131 6.4.1 Berthing (parking) Control of USV ..................................................... 133 6.4.2 Motion Control of UV ......................................................................... 136 Chapter 7: Obstacle Avoidance and Path Planning for Vehicle Using Rapidly-Exploring Random Trees Algorithm.................................................................. 168 7.1 Path Planning and Guidance: Two Interrelated Problems ....................... 168 7.2 RRT Algorithm for Exploration .............................................................. 171 7.2.1 Random Node Selection ...................................................................... 172 7.2.2 Nearest Neighbor Node Selection ....................................................... 173 7.2.3 RRT Exploration with Obstacles ......................................................... 174 7.3 RRT Algorithm for Navigation of AUV ................................................. 176 7.3.1 Basic RRT Algorithm .......................................................................... 176 7.3.2 Biased-Greedy RRT Algorithm ........................................................... 178 7.3.3 Synchronized Biased-Greedy RRT Algorithm .................................... 179 7.4 Path Pruning ............................................................................................ 182 7.4.1 Path Pruning Using LOS ..................................................................... 182 7.4.2 Global Path Pruning ............................................................................. 183 7.5 Summarize the Proposed RRT Algorithm ............................................... 185 7.6 Simulation for Path Following of AUV .................................................. 187 Chapter 8: Simulation of Complete USV-UC-UV Systems ............................ 196 8.1 Simulation Procedure .............................................................................. 196 8.2 Simulation Results and Discussion ......................................................... 201 8.2.1 Dynamic Behaviors of Complete USV (Stable)-Cable- AUV (Turning Motion) ..................................................................................................... 201 8.2.2 Dynamic Behaviors of Complete USV (Forward motion)-Cable- AUV (Turning Motion) ...................................................................................... 207 8.2.3 Applied Controller to Complete USV -Cable- AUV ........................... 215 Chapter 9: Conclusions and Future Works ..................................................... 238 9.1 Modeling of Complete USV-Cable-AUV System .................................. 238 9.2 Motion Control ........................................................................................ 239 9.3 Cable Force and Moment at the Tow Points ........................................... 239 9.4 Path Planning ........................................................................................... 239 9.5 Future Works ........................................................................................... 240Docto

Rotor Speed Analysis of SMC-based IFOC for Low-Speed Induction Motor Control

The control of electric motors, particularly three-phase induction motors, has developed rapidly due to their application in industry. Indirect Field Oriented Control (IFOC) is one of the most widely used control systems due to its ease of application. IFOC controls a three-phase induction motor in the same way as a DC motor. However, IFOC requires a Sliding Mode Control (SMC) controller with Lyapunov stability theory to ensure robustness and stability. In exceptional conditions, such as low-speed settings, the SMC-based IFOC requires unique sets to operate with a steady-state error (Ess) at a speed response of less than 2%. Other parameters to be considered are rise time and electromagnetic torque response at low speeds. The addition of the boundary layer of the hyperbolic tangent function to a first-order SMC can increase induction motor (IM) control up to 175 rpm with a value of Ess = 1.96% compared to the saturation and signum functions, which are only capable of a reference speed of 300 rpm in no-load conditions with a value of Ess = 2% for the saturation function and 1.94% for the signum function. SMC with the hyperbolic tangent function boundary layer performs best under load conditions. The rising time value does not significantly differ under no-load or torque-load conditions between the SMC with the saturation, hyperbolic tangent function boundary layers and without the boundary layer. Adding a boundary layer with the hyperbolic tangent function can reduce ripple significantly compared to the saturation function under no-load or load conditions

Pose Detection and Control of Unmanned Underwater Vehicles (UUVs) Utilizing an Optical Detector Array

As part of the research for development of a leader-follower formation between unmanned underwater vehicles (UUVs), this study presents an optical feedback system for UUV navigation via an optical detector array. Capabilities of pose detection and control in a static-dynamic system (e.g. UUV navigation into a docking station) and a dynamic-dynamic system (e.g. UUV to UUV leader-follower system) are investigated. In both systems, a single light source is utilized as a guiding beacon for a tracker/follower UUV. The UUV uses an optical array consisting of photodiodes to receive the light field emitted from the light source. For UUV navigation applications, accurate pose estimation is essential. In order to evaluate the feasibility of underwater distance detection, the effective communication range between two platforms, i.e. light source and optical detector, and the optimum spectral range that allowed maximum light transmission are calculated. Based on the light attenuation in underwater, the geometry and dimensions of an optical detector array are determined, and the boundary conditions for the developed pose detection algorithms along with the error sources in the experiments are identified. As a test bed to determine optical array dimensions and size, a simulator, i.e. numerical software, is developed, where planar and curved array geometries of varying number of elements are analytically compared and evaluated. Results show that the curved optical detector array is able to distinguish 5 degree- of-freedom (DOF) motion (translation in x, y, z-axes and pitch and yaw rotations) with respect to a single light source. Analytical pose detection and control algorithms are developed for both static-dynamic and dynamic-dynamic systems. Results show that a 5 x 5 curved detector array with the implementation of SMC is reasonably sufficient for practical UUV positioning applications. The capabilities of an optical detector array to determine the pose of a UUV in 3-DOF (x, y and z-axes) are experimentally tested. An experimental platform consisting of a 5 x 5 photodiode array mounted on a hemispherical surface is used to sample the light field emitted from a single light source. Pose detection algorithms are developed to detect pose for steady-state and dynamic cases. Monte Carlo analysis is conducted to assess the pose estimation uncertainty under varying environmental and hardware conditions such as water turbidity, temperature variations in water and electrically-based noise. Monte Carlo analysis results show that the pose uncertainties (within 95% confidence interval) associated with x, y and z-axes are 0.78 m, 0.67 m and 0.56 m, respectively. Experimental results demonstrate that x, y and z-axes pose estimates are accurate to within 0.5 m, 0.2 m and 0.2 m, respectively

Time-optimal trajectory and robust adaptive control for hybrid underwater glider

The undersea environment is generally still a mystery for the human race, although it has been with us for a long time. To explore under the sea, the underwater glider is the efficient equipment capable of sustainable operation for several months. For faster and longer duration performance, a new design of underwater glider (UG) shaping ray type is proposed. To have the shortest settling time, a new design of time-optimal trajectory (TOT) for controlling the states of the ray-type hybrid underwater glider (RHUG) is proposed. And for the stable flight control, a robust adaptive controller is designed for the RHUG with unknown parameters and environmental disturbances. The heading dynamics of the RHUG is presented with linear and quadratic damping. A closed form solution of the heading dynamics is realized for designing the time-optimal trajectory. The conventional and super-twisting sliding mode control will be constructed for tracking this trajectory. The tracking performance considering the disturbance effect will be discussed in simulations. For identification of unknown parameters of the system, the adaptive control is designed and implemented by the heading experiment. The RHUG uses the net buoyancy force for gliding under the water, so the depth control is essential. In this dissertation, a robust control algorithm with TOT will be carried out for the heaving motion using a hybrid actuation of the buoyancy engine and the propeller. The net buoyancy force with a constant rate is generated by the buoyancy engine for both descending and ascending motion. And the second actuator for the depth control is the propeller with quick response in producing thrusting force. To apply the robust control with TOT, the control input is designed for the buoyancy engine and thruster individually. And finally, the robust control with TOT using the buoyancy engine and thruster is simulated with consideration of external disturbances. When the RHUG is the underactuated system, a robust adaptive control is designed for the RHUG dynamics based on Lyapunov’s direct method using the backstepping and sliding mode control techniques. The performance of this controller is simulated for gliding motion and depth control with unknown parameters and bounded disturbances.Contents Contents i List of Tables iv List of Figures v Chapter 1. Introduction 1 1.1. Hybrid underwater glider 1 1.2. Time-optimal trajectory 4 1.3. Nonlinear control design 5 Chapter 2. Dynamics of RHUG 8 2.1 Dynamics of underwater vehicles 8 2.2 Design of RHUG platform 11 2.2.1 Hull design 11 2.2.2 Buoyancy engine and mass-shifter 12 2.2.3 Battery 13 2.2.4 Sensors 14 2.2.5 Assembly 16 2.3 Dynamics of RHUG 17 2.4 Hydrodynamic coefficients 19 2.5 Thruster modeling 21 2.6 Buoyancy engine modeling 22 2.7 Mass-shifter modeling 23 Chapter 3. Time-optimal trajectory with actuator saturation for heading control 25 3.1 Time-optimal trajectory 25 3.2 Heading motion 25 3.3 Analytic solution of heading dynamic equation 26 3.3.1 Right-hand direction 29 3.3.2 Left-hand direction 36 3.4 Time-optimal trajectory 42 3.5 Super-twisting sliding mode control 44 3.6 Computer simulation 46 3.6.1 Simulation 1 46 3.6.2 Simulation 2 47 3.6.3 Simulation 3 49 Chapter 4. Time-optimal trajectory for heaving motion control using buoyancy engine and propeller individually 51 4.1. Heave dynamics and TOT 51 4.2. Analytical solution of heave dynamics with buoyancy and thruster force individually 54 4.2.1 First segment with positive rate 54 4.2.2 Second segment with maximum input 55 4.2.3 Third segment with constant velocity 56 4.2.4 Fourth segment with negative rate 57 4.2.5 Fifth segment with minimum input 58 4.3. Time-optimal trajectory for depth motion 59 4.3.1 Find z1, w1 and w1 59 4.3.2 Find t2, z2, w2 and w2 61 4.3.3 Find w3, z4 and w4 62 4.3.4 Find z3, t3 and t4 63 4.3.5 Find α and t5 64 4.4. Sliding mode control for heave dynamics 64 4.5. Computer simulation 66 4.5.1. Simulation 1 66 4.5.2. Simulation 2 69 Chapter 5. Experimental study of direct adaptive control along TOT for heading motion 72 5.1. Motivation 72 5.2. Composition of RHUG 73 5.3. Robust adaptive control for heading dynamics 77 5.4. Computer simulation 79 5.5 Experiment 82 5.5.1 First experiment with k1=2.5,k2=30 82 5.5.2 Second experiment with k1=2,k2=30 83 5.5.3 Third experiment with k1=2,k2=50 85 Chapter 6. Robust adaptive control design for vertical motion 89 6.1. Dynamics of vertical plane 89 6.2. Adaptive sliding-mode control for pitch motion 91 6.3. Adaptive sliding-mode control for surge motion 93 6.4. LOS and PI depth-keeping guidance 95 6.5. Computer simulation 97 6.5.1 Simulation 1 97 6.5.2 Simulation 2 104 Chapter 7. Conclusion 111 Reference 113Docto

Development of Self-Learning Type-2 Fuzzy Systems for System Identification and Control of Autonomous Systems

Modelling and control of dynamic systems are faced by multiple technical challenges, mainly due to the nature of uncertain complex, nonlinear, and time-varying systems. Traditional modelling techniques require a complete understanding of system dynamics and obtaining comprehensive mathematical models is not always achievable due to limited knowledge of the systems as well as the presence of multiple uncertainties in the environment. As universal approximators, fuzzy logic systems (FLSs), neural networks (NNs) and neuro-fuzzy systems have proved to be successful computational tools for representing the behaviour of complex dynamical systems. Moreover, FLSs, NNs and learning-based techniques have been gaining popularity for controlling complex, ill-defined, nonlinear, and time-varying systems in the face of uncertainties. However, fuzzy rules derived by experts can be too ad-hoc, and the performance is less than optimum. In other words, generating fuzzy rules and membership functions in fuzzy systems is a potential challenge especially for systems with many variables. Moreover, under the umbrella of FLSs, although type-1 fuzzy logic control systems (T1-FLCs) have been applied to control various complex nonlinear systems, they have limited capability to handle uncertainties. Aiming to accommodate uncertainties, type-2 fuzzy logic control systems (T2-FLCs) were established. This thesis aims to address the shortcomings of existing fuzzy techniques by utilisation of type-2 FLCs with novel adaptive capabilities. The first contribution of this thesis is a novel online system identification technique by means of a recursive interval type-2 Takagi-Sugeno fuzzy C-means clustering technique (IT2-TS-FC) to accommodate the footprint-of-uncertainties (FoUs). This development is meant to specifically address the shortcomings of type-1 fuzzy systems in capturing the footprint-of-uncertainties such as mechanical wear, rotor damage, battery drain and sensor and actuator faults. Unlike previous type-2 TS fuzzy models, the proposed method constructs two fuzzifiers (upper and lower) and two regression coefficients in the consequent part to handle uncertainties. The weighted least square method is employed to compute the regression coefficients. The proposed method is validated using two benchmarks, namely, real flight test data of a quadcopter drone and Mackey-Glass time series data. The algorithm has the capability to model uncertainties (e.g., noisy dataset). The second contribution of this thesis is the development of a novel self-adaptive interval type-2 fuzzy controller named the SAF2C for controlling multi-input multi-output (MIMO) nonlinear systems. The adaptation law is derived using sliding mode control (SMC) theory to reduce the computation time so that the learning process can be expedited by 80% compared to separate single-input single-output (SISO) controllers. The system employs the `Enhanced Iterative Algorithm with Stop Condition' (EIASC) type-reduction method, which is more computationally efficient than the `Karnik-Mendel' type-reduction algorithm. The stability of the SAF2C is proven using the Lyapunov technique. To ensure the applicability of the proposed control scheme, SAF2C is implemented to control several dynamical systems, including a simulated MIMO hexacopter unmanned aerial vehicle (UAV) in the face of external disturbance and parameter variations. The ability of SAF2C to filter the measurement noise is demonstrated, where significant improvement is obtained using the proposed controller in the face of measurement noise. Also, the proposed closed-loop control system is applied to control other benchmark dynamic systems (e.g., a simulated autonomous underwater vehicle and inverted pendulum on a cart system) demonstrating high accuracy and robustness to variations in system parameters and external disturbance. Another contribution of this thesis is a novel stand-alone enhanced self-adaptive interval type-2 fuzzy controller named the ESAF2C algorithm, whose type-2 fuzzy parameters are tuned online using the SMC theory. This way, we expect to design a computationally efficient adaptive Type-2 fuzzy system, suitable for real-time applications by introducing the EIASC type-reducer. The proposed technique is applied on a quadcopter UAV (QUAV), where extensive simulations and real-time flight tests for a hovering QUAV under wind disturbances are also conducted to validate the efficacy of the ESAF2C. Specifically, the control performance is investigated in the face of external wind gust disturbances, generated using an industrial fan. Stability analysis of the ESAF2C control system is investigated using the Lyapunov theory. Yet another contribution of this thesis is the development of a type-2 evolving fuzzy control system (T2-EFCS) to facilitate self-learning (either from scratch or from a certain predefined rule). T2-EFCS has two phases, namely, the structure learning and the parameters learning. The structure of T2-EFCS does not require previous information about the fuzzy structure, and it can start the construction of its rules from scratch with only one rule. The rules are then added and pruned in an online fashion to achieve the desired set-point. The proposed technique is applied to control an unmanned ground vehicle (UGV) in the presence of multiple external disturbances demonstrating the robustness of the proposed control systems. The proposed approach turns out to be computationally efficient as the system employs fewer fuzzy parameters while maintaining superior control performance

Sliding Mode Control

The main objective of this monograph is to present a broad range of well worked out, recent application studies as well as theoretical contributions in the field of sliding mode control system analysis and design. The contributions presented here include new theoretical developments as well as successful applications of variable structure controllers primarily in the field of power electronics, electric drives and motion steering systems. They enrich the current state of the art, and motivate and encourage new ideas and solutions in the sliding mode control area