Integrated Optimal and Robust Control of Spacecraft in Proximity Operations

With the rapid growth of space activities and advancement of aerospace science and technology, many autonomous space missions have been proliferating in recent decades. Control of spacecraft in proximity operations is of great importance to accomplish these missions. The research in this dissertation aims to provide a precise, efficient, optimal, and robust controller to ensure successful spacecraft proximity operations. This is a challenging control task since the problem involves highly nonlinear dynamics including translational motion, rotational motion, and flexible structure deformation and vibration. In addition, uncertainties in the system modeling parameters and disturbances make the precise control more difficult. Four control design approaches are integrated to solve this challenging problem. The first approach is to consider the spacecraft rigid body translational and rotational dynamics together with the flexible motion in one unified optimal control framework so that the overall system performance and constraints can be addressed in one optimization process. The second approach is to formulate the robust control objectives into the optimal control cost function and prove the equivalency between the robust stabilization problem and the transformed optimal control problem. The third approach is to employ the è-D technique, a novel optimal control method that is based on a perturbation solution to the Hamilton-Jacobi-Bellman equation, to solve the nonlinear optimal control problem obtained from the indirect robust control formulation. The resultant optimal control law can be obtained in closedorm, and thus facilitates the onboard implementation. The integration of these three approaches is called the integrated indirect robust control scheme. The fourth approach is to use the inverse optimal adaptive control method combined with the indirect robust control scheme to alleviate the conservativeness of the indirect robust control scheme by using online parameter estimation such that adaptive, robust, and optimal properties can all be achieved. To show the effectiveness of the proposed control approaches, six degree-offreedom spacecraft proximity operation simulation is conducted and demonstrates satisfying performance under various uncertainties and disturbances

Review of advanced guidance and control algorithms for space/aerospace vehicles

The design of advanced guidance and control (G&C) systems for space/aerospace vehicles has received a large amount of attention worldwide during the last few decades and will continue to be a main focus of the aerospace industry. Not surprisingly, due to the existence of various model uncertainties and environmental disturbances, robust and stochastic control-based methods have played a key role in G&C system design, and numerous effective algorithms have been successfully constructed to guide and steer the motion of space/aerospace vehicles. Apart from these stability theory-oriented techniques, in recent years, we have witnessed a growing trend of designing optimisation theory-based and artificial intelligence (AI)-based controllers for space/aerospace vehicles to meet the growing demand for better system performance. Related studies have shown that these newly developed strategies can bring many benefits from an application point of view, and they may be considered to drive the onboard decision-making system. In this paper, we provide a systematic survey of state-of-the-art algorithms that are capable of generating reliable guidance and control commands for space/aerospace vehicles. The paper first provides a brief overview of space/aerospace vehicle guidance and control problems. Following that, a broad collection of academic works concerning stability theory-based G&C methods is discussed. Some potential issues and challenges inherent in these methods are reviewed and discussed. Then, an overview is given of various recently developed optimisation theory-based methods that have the ability to produce optimal guidance and control commands, including dynamic programming-based methods, model predictive control-based methods, and other enhanced versions. The key aspects of applying these approaches, such as their main advantages and inherent challenges, are also discussed. Subsequently, a particular focus is given to recent attempts to explore the possible uses of AI techniques in connection with the optimal control of the vehicle systems. The highlights of the discussion illustrate how space/aerospace vehicle control problems may benefit from these AI models. Finally, some practical implementation considerations, together with a number of future research topics, are summarised

Dual-Quaternion-Based Fault-Tolerant Control for Spacecraft Tracking With Finite-Time Convergence

Results are presented for a study of dual-quaternion-based fault-tolerant control for spacecraft tracking. First, a six-degrees-of-freedom dynamic model under a dual-quaternion-based description is employed to describe the relative coupled motion of a target-pursuer spacecraft tracking system. Then, a novel fault-tolerant control method is proposed to enable the pursuer to track the attitude and the position of the target even though its actuators have multiple faults. Furthermore, based on a novel time-varying sliding manifold, finite-time stability of the closed-loop system is theoretically guaranteed, and the convergence time of the system can be given explicitly. Multiple-task capability of the proposed control law is further demonstrated in the presence of disturbances and parametric uncertainties. Finally, numerical simulations are presented to demonstrate the effectiveness and advantages of the proposed control method

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 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

A brief review of neural networks based learning and control and their applications for robots

As an imitation of the biological nervous systems, neural networks (NN), which are characterized with powerful learning ability, have been employed in a wide range of applications, such as control of complex nonlinear systems, optimization, system identification and patterns recognition etc. This article aims to bring a brief review of the state-of-art NN for the complex nonlinear systems. Recent progresses of NNs in both theoretical developments and practical applications are investigated and surveyed. Specifically, NN based robot learning and control applications were further reviewed, including NN based robot manipulator control, NN based human robot interaction and NN based behavior recognition and generation

Wind Preview-Based Model Predictive Control of Multi-Rotor UAVs Using LiDAR

Autonomous outdoor operations of Unmanned Aerial Vehicles (UAVs), such as quadrotors, expose the aircraft to wind gusts causing a significant reduction in their position-holding performance. This vulnerability becomes more critical during the automated docking of these vehicles to outdoor charging stations. Utilising real-time wind preview information for the gust rejection control of UAVs has become more feasible due to the advancement of remote wind sensing technology such as LiDAR. This work proposes the use of a wind-preview-based Model Predictive Controller (MPC) to utilise remote wind measurements from a LiDAR for disturbance rejection. Here a ground-based LiDAR unit is used to predict the incoming wind disturbance at the takeoff and landing site of an autonomous quadrotor UAV. This preview information is then utilised by an MPC to provide the optimal compensation over the defined horizon. Simulations were conducted with LiDAR data gathered from field tests to verify the efficacy of the proposed system and to test the robustness of the wind-preview-based control. The results show a favourable improvement in the aircraft response to wind gusts with the addition of wind preview to the MPC; An 80% improvement in its position-holding performance combined with reduced rotational rates and peak rotational angles signifying a less aggressive approach to increased performance when compared with only feedback based MPC disturbance rejection. System robustness tests demonstrated a 1.75 s or 120% margin in the gust preview’s timing or strength respectively before adverse performance impact. The addition of wind-preview to an MPC has been shown to increase the gust rejection of UAVs over standard feedback-based MPC thus enabling their precision landing onto docking stations in the presence of wind gusts

Automatic Control and Routing of Marine Vessels

Due to the intensive development of the global economy, many problems are constantly emerging connected to the safety of ships’ motion in the context of increasing marine traffic. These problems seem to be especially significant for the further development of marine transportation services, with the need to considerably increase their efficiency and reliability. One of the most commonly used approaches to ensuring safety and efficiency is the wide implementation of various automated systems for guidance and control, including such popular systems as marine autopilots, dynamic positioning systems, speed control systems, automatic routing installations, etc. This Special Issue focuses on various problems related to the analysis, design, modelling, and operation of the aforementioned systems. It covers such actual problems as tracking control, path following control, ship weather routing, course keeping control, control of autonomous underwater vehicles, ship collision avoidance. These problems are investigated using methods such as neural networks, sliding mode control, genetic algorithms, L2-gain approach, optimal damping concept, fuzzy logic and others. This Special Issue is intended to present and discuss significant contemporary problems in the areas of automatic control and the routing of marine vessels