https://www.itk.ntnu.no/fag/fordypning/TK16-filer/Samling1_MorariLee.pdf

Model predictive control: past, present and future

In this paper, a model predictive control (MPC) approach for controlling an active front steering system in an autonomous vehicle is presented. At each time step, a trajectory is assumed to be known over a finite horizon, and an MPC controller computes the front steering angle in order to follow the trajectory on slippery roads at the highest possible entry speed. We present two approaches with different computational complexities. In the first approach, we formulate the MPC problem by using a nonlinear vehicle model. The second approach is based on successive online linearization of the vehicle model. Discussions on computational complexity and performance of the two schemes are presented. The effectiveness of the proposed MPC formulation is demonstrated by simulation and experimental tests up to 21 m/s on icy roads

Predictive Active Steering Control for Autonomous Vehicle Systems

Probabilistic inference is the problem of estimating the hidden variables (states or parameters) of a system in an optimal and consistent fashion as a set of noisy or incomplete observations of the system becomes available online. The optimal solution to this problem is given by the recursive Bayesian estimation algorithm which recursively updates the posterior density of the system state as new observations arrive. This posterior density constitutes the complete solution to the probabilistic inference problem, and allows us to calculate any “optimal” estimate of the state. Unfortunately, for most real-world problems, the optimal Bayesian recursion is intractable and approximate solutions must be used. Within the space of approximate solutions, the extended Kalman filter (EKF) has become one of the most widely used algorithms with applications in state, parameter and dual estimation. Unfortunately, the EKF is based on a sub-optimal implementation of the recursive Bayesian estimation framework applied to Gaussian random variables. This can seriously affect the accuracy or even lead to divergence of any inference system that is based on the EKF or that uses the EKF as a component part. Recently a number of related novel, more accurate and theoretically better motivated algorithmic alternatives to the EKF have surfaced in the literature, with specific application to state estimation for automatic control. We have extended these algorithms, all based on derivativeless deterministic sampling based approximations of the relevant Gaussian statistics, to a family of algorithms called Sigma-Point Kalman Filters (SPKF). Furthermore, we successfully expanded the use of this group of algorithms (SPKFs) within the general field of probabilistic inference and machine learning, both as stand-alone filters and as subcomponents of more powerful sequential Monte Carlo methods (particle filters). We have consistently shown that there are large performance benefits to be gained by applying Sigma-Point Kalman filters to areas where EKFs have been used as the de facto standard in the past, as well as in new areas where the use of the EKF is impossible.

/pdf/sigma-point-kalman-filters-for-probabilistic-inference-in-1affrc62j6.pdf

Sigma-point kalman filters for probabilistic inference in dynamic state-space models

Extensions of a consensus algorithm are introduced for systems modelled by second-order dynamics. Variants of those consensus algorithms are applied to tackle formation control problems by appropriately choosing information states on which consensus is reached. Even in the absence of centralised leadership, the consensus-based formation control strategies can guarantee accurate formation maintenance in the general case of arbitrary (directed) information flow between vehicles as long as certain mild conditions are satisfied. It is shown that many existing leader-follower, behavioural and virtual structure/virtual leader formation control approaches can be unified in the general framework of consensus building. A multiple micro air vehicle formation flying example is shown in simulation to illustrate the strategies

Consensus strategies for cooperative control of vehicle formations

A fundamental aspect of autonomous vehicle guidance is planning trajectories. Historically, two fields have contributed to trajectory or motion planning methods: robotics and dynamics and control. The former typically have a stronger focus on computational issues and real-time robot control, while the latter emphasize the dynamic behavior and more specific aspects of trajectory performance. Guidance for Unmanned Aerial Vehicles (UAVs), including fixed- and rotary-wing aircraft, involves significant differences from most traditionally defined mobile and manipulator robots. Qualities characteristic to UAVs include non-trivial dynamics, three-dimensional environments, disturbed operating conditions, and high levels of uncertainty in state knowledge. Otherwise, UAV guidance shares qualities with typical robotic motion planning problems, including partial knowledge of the environment and tasks that can range from basic goal interception, which can be precisely specified, to more general tasks like surveillance and reconnaissance, which are harder to specify. These basic planning problems involve continual interaction with the environment. The purpose of this paper is to provide an overview of existing motion planning algorithms while adding perspectives and practical examples from UAV guidance approaches.

A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance

Foreword. Acknowledgements. Nomenclature. Acronyms. 1. Motivation and Background. 2. Frequency Response System Identification. 3. Development of the Identification Model. 4. Identification of the Model. 5. Characteristics of Small-Scale Rotorcraft. 6. Elements of Control Design. 7. Results, Milestones and Future Directions in Aerial Robotics. References. Index.

Identification Modeling and Characteristics of Miniature Rotorcraft

System identification modeling of a small-scale unmanned rotorcraft for flight control design

Abstmcf: Flight testing of a fully-instrumented model-scale unmanned helicopter (Yamaha R-SO with loft. diameter rotor) was conducted for the purpose of dynamic model identification. This paper describes the application of CIFER' system identification techniques, which have been developed for full size helicopters, to this aircraft. An accurate, high-bandwidth, linear state-space model was derived for the hover condition. The model structure includes the explicit representation of regressive rotor-flap dynamics, rigid-body fuselage dynamics, and the yaw damper. The R-50 codiguration and identified dynamics are compared with those of a dynamically scaled UH-1H. The identified model shows excellent predictive capability and is well suited for flight control design and simulation applications.

/pdf/system-identification-of-small-size-unmanned-helicopter-3wzlv7izeq.pdf

System identification of small-size unmanned helicopter dynamics

Poor control of the steam generator water level in the secondary circuit of a nuclear power plant can lead to frequent reactor shutdowns. Such shutdowns are caused by violation of safety limits on the water level and are common at low operating power where the plant exhibits strong nonminimum phase characteristics and flow measurements are unreliable. There is, therefore, a need to systematically investigate the problem of controlling the water level in the steam generator in order to prevent such costly reactor shutdowns. The paper presents a framework for addressing this problem based on an extension of the standard linear model predictive control algorithm to linear parameter varying systems.

Level control in the steam generator of a nuclear power plant

A nonlinear model for small-size helicopters was developed and validated using flight data collected on a small-size acrobatic helicopter. The model concentrates on the key effects in the dynamics of small-size helicopter, resulting in a simple model featuring a small set of physical parameters. Validation of the model by comparison of model responses to real flight-test data showed good fidelity for a wide range of conditions including acrobatic maneuvers. The model is integrated into a real-time hardware-inthe-loop simulation environment used for the development of flight control systems and path-planning algorithms that are needed for highly maneuverable autonomous small-size helicopters.

Bernard Mettler

Papers

Identification Modeling and Characteristics of Miniature Rotorcraft

System identification modeling of a small-scale unmanned rotorcraft for flight control design

System identification of small-size unmanned helicopter dynamics

Level control in the steam generator of a nuclear power plant

Nonlinear model for a small-size acrobatic helicopter