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

DECENTRALIZED ROBUST NONLINEAR MODEL PREDICTIVE CONTROLLER FOR UNMANNED AERIAL SYSTEMS

The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1 A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2 A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3 An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4 A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible

Active control of turbulence-induced helicopter vibration

Helicopter vibration signatures induced by severe atmospheric turbulence have been shown to differ considerably from nominal, still air vibration. The perturbations of the transmission frequency have significant implications for the design of passive and active vibration alleviation devices, which are generally tuned to the nominal vibration frequency. This thesis investigates the existence of the phenomena in several realistic atmospheric turbulence environments, generated using Computational Fluid Dynamic (CFD) engineering software and assimilated within a high-fidelity rotorcraft simulation, RASCAL. The RASCAL simulation is modified to calculate blade element sampling of the gust, enabling thorough, high frequency analyses of the rotor response. In a final modification, a numerical, integration-based inverse simulation algorithm, GENISA is incorporated and the augmented simulation is henceforth referred to as HISAT. Several implementation issues arise from the symbiosis, principally because of the modelling of variable rotorspeed and lead-lag motion. However, a novel technique for increasing the numerical stability margins is proposed and tested successfully. Two active vibration control schemes, higher harmonic control 'HHC' and individual blade control 'IBC', are then evaluated against a 'worst-case' sharp-edged gust field. The higher harmonic controller demonstrates a worrying lack of robustness, and actually begins to contribute to the vibration levels. Several intuitive modifications to the algorithm are proposed but only disturbance estimation is successful. A new simulation model of coupled blade motion is derived and implemented using MATLAB and is used to design a simple IBC compensator. Following bandwidth problems, a redesign is proposed using H theory which improves the controller performance. Disturbance prediction/estimation is attempted using artificial neural networks to limited success. Overall, the aims and objectives of the research are met

Identification and augmentation of a civil light helicopter: transforming a helicopter into a Personal Aerial Vehicle

In 2011 the European Commission funded an out of the box study, the myCopter project, with the aim of identifying new concepts for air transport that could be used to achieve a Personal Aerial Transport (PAT) system in the second half of the 21st century. Although designing a new vehicle was not among the project's goal, it was considered important to assess vehicle response types and handling qualities that Personal Aerial Vehicles (PAVs) should have to be part of a PAT. This thesis proposes to consider civil light helicopters as possible PAVs candidates. The thesis goal is to investigate whether it is possible to transform civil light helicopters into PAVs through the use of system identification methods and control techniques. The transformation here is envisaged in terms of vehicle dynamics and handling qualities. To achieve this goal, the thesis is divided into three main steps. The first step, focuses on the identification of a Robinson R44 Raven II helicopter model in hover. The hover condition is considered well suited for the goal of the thesis as it represents one of the most difficult to perform, particularly for inexperienced pilots. The second step consists of augmenting the identified helicopter model to achieve response types and handling qualities defined for PAVs. An optimal H-infinity and a robust mu-synthesis techniques are implemented for this purpose. The third step consists of assessing the magnitude of the discrepancies between the two implemented augmented systems and the PAV reference model. An experiment is conducted for this purpose, consisting of piloted closed-loop control tasks performed in the MPI CyberMotion Simulator by participants without prior flight experience. Results, evaluated in terms of objective and subjective workload and performance, show that both augmented control systems are able to resemble PAVs handling qualities and response types in piloted closed-loop control tasks. This result demonstrates that it is possible to transform helicopter dynamics into PAVs ones. Therefore, the approach proposed in this thesis represents a valid alternative to the common practice of implementing new vehicles that can achieve specific requirements like those defined for PAVs

Co-Simulation in Virtual Verification of Vehicles with Mechatronic Systems

In virtual verification of vehicle and mechatronic systems, a mixture of subsystems are integrated numerically in an offline simulation or integrated physically in a hardware-in-loop (HIL) simulation. This heterogeneous engineering approach is crucial for system-level development and widely spreads with\ua0the industrial standard, e.g. Functional Mock-Up Interface (FMI) standard.For the engineers, not only the local subsystem and solver should be known,\ua0but also the global coupled dynamic system and its coupling effect need to be\ua0understood. Both the local and global factors influence the stability, accuracy, numerical efficiency and further on the real-time simulation capability.In this thesis, the explicit parallel co-simulation, which is the most common and closest to the integration with a physical system, is investigated.In the vehicle development, the vehicle and the mechatronic system, e.g. an\ua0Electrcial Power Assisted Steering (EPAS) system can be simulated moreefficiently by a tailored solver and communicative step. The accuracy and\ua0numerical stability problem, which highly depends on the interface dynamics, can be investigated similarly in the linear robust control framework. The\ua0vehicle-mechatronic system should be coupled to give a smaller loop gain for robustness and stability. Physically, it indicates that the splitting part\ua0should be less stiff and the force or torque variable should be applied towardsthe part with a higher impedance in the force-displacement coupling. Furthermore, to compensate the troublesome low-passed and delay effect fromthe coupling, a new coupling method based on H∞ synthesis is developed,\ua0which can improve the accuracy of co-simulation. The method shows robustness to the system dynamics, which makes it more applicable for a complex\ua0vehicle-mechatronic system

適応制御戦略に基づく車輪型二足歩行ロボットのバランス安定性の向上

Tohoku University林部充宏課

Correct-By-Construction Control Synthesis for Systems with Disturbance and Uncertainty

This dissertation focuses on correct-by-construction control synthesis for Cyber-Physical Systems (CPS) under model uncertainty and disturbance. CPSs are systems that interact with the physical world and perform complicated dynamic tasks where safety is often the overriding factor. Correct-by-construction control synthesis is a concept that provides formal performance guarantees to closed-loop systems by rigorous mathematic reasoning. Since CPSs interact with the environment, disturbance and modeling uncertainty are critical to the success of the control synthesis. Disturbance and uncertainty may come from a variety of sources, such as exogenous disturbance, the disturbance caused by co-existing controllers and modeling uncertainty. To better accommodate the different types of disturbance and uncertainty, the verification and control synthesis methods must be chosen accordingly. Four approaches are included in this dissertation. First, to deal with exogenous disturbance, a polar algorithm is developed to compute an avoidable set for obstacle avoidance. Second, a supervised learning based method is proposed to design a good student controller that has safety built-in and rarely triggers the intervention of the supervisory controller, thus targeting the design of the student controller. Third, to deal with the disturbance caused by co-existing controllers, a Lyapunov verification method is proposed to formally verify the safety of coexisting controllers while respecting the confidentiality requirement. Finally, a data-driven approach is proposed to deal with model uncertainty. A minimal robust control invariant set is computed for an uncertain dynamic system without a given model by first identifying the set of admissible models and then simultaneously computing the invariant set while selecting the optimal model. The proposed methods are applicable to many real-world applications and reflect the notion of using the structure of the system to achieve performance guarantees without being overly conservative.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145933/1/chenyx_1.pd

Impacts of Connected and Automated Vehicles on Energy and Traffic Flow: Optimal Control Design and Verification Through Field Testing

This dissertation assesses eco-driving effectiveness in several key traffic scenarios that include passenger vehicle transportation in highway driving and urban driving that also includes interactions with traffic signals, as well as heavy-duty line-haul truck transportation in highway driving with significant road grade. These studies are accomplished through both traffic microsimulation that propagates individual vehicle interactions to synthesize large-scale traffic patterns that emerge from the eco-driving strategies, and through experimentation in which real prototyped connected and automated vehicles (CAVs) are utilized to directly measure energy benefits from the designed eco-driving control strategies. In particular, vehicle-in-the-loop is leveraged for the CAVs driven on a physical test track to interact with surrounding traffic that is virtually realized through said microsimulation software in real time. In doing so, model predictive control is designed and implemented to create performative eco-driving policies and to select vehicle lane, as well as enforce safety constraints while autonomously driving a real vehicle. Ultimately, eco-driving policies are both simulated and experimentally vetted in a variety of typical driving scenarios to show up to a 50% boost in fuel economy when switching to CAV drivers without compromising traffic flow. The first part of this dissertation specifically assesses energy efficiency of connected and automated passenger vehicles that exploit intention-sharing sourced from both neighboring vehicles in a highway scene and from traffic lights in an urban scene. Linear model predictive control is implemented for CAV motion planning, whereby chance constraints are introduced to balance between traffic compactness and safety, and integer decision variables are introduced for lane selection and collision avoidance in multi-lane environments. Validation results are shown from both large-scale microsimulation and through experimentation of real prototyped CAVs. The second part of this dissertation then assesses energy efficiency of automated line-haul trucks when tasked to aerodynamically platoon. Nonlinear model predictive control is implemented for motion planning, and simulation and experimentation are conducted for platooning verification under highway conditions with traffic. Then, interaction-aware and intention-sharing cooperative control is further introduced to eliminate experimentally measured platoon disengagements that occur on real highways when using only status-sharing control. Finally, the performance of automated drivers versus human drivers are compared in a point-to-point scenario to verify fundamental eco-driving impacts -- experimentally showing eco-driving to boost energy economy by 11% on average even in simple driving scenarios