Tamás Péni

Robust Control for Urban Road Traffic Networks

The aim of the presented research is to elaborate a traffic-responsive optimal signal split algorithm taking uncertainty into account. The traffic control objective is to minimize the weighted link queue lengths within an urban network area. The control problem is formulated in a centralized rolling-horizon fashion in which unknown but bounded demand and queue uncertainty influences the prediction. An efficient constrained minimax optimization is suggested to obtain the green time combination, which minimizes the objective function when worst case uncertainty appears. As an illustrative example, a simulation study is carried out to demonstrate the effectiveness and computational feasibility of the robust predictive approach. By using real-world traffic data and microscopic traffic simulator, the proposed robust signal split algorithm is analyzed and compared with well-tuned fixed-time signal timing and to nominal predictive solutions under different traffic conditions.

Collision avoidance for UAV using visual detection

Unmanned Arial Vehicles (UAVs) require the development of some on-board safety equipments before inheriting the sky. An on-board collision avoidance system is being built by our team. Due to the strict size, weight, power, and costs constraints, visual intruder airplane detection is the only option. This paper introduces our visual airplane detector algorithm, which is designed to be operational in clear and in cloudy situations under regular daylight visual conditions. To be able to implement the algorithm on-board, we have carefully selected topographic operators, which can be efficiently solved on cellular processor arrays.

Practical approach to real-time trajectory tracking of UAV formations

An unmanned aerial vehicle (UAV) formation in a leader-follower structure, where the UAVs are flying a common trajectory determined by a route planner hosted on the leader is considered. The path description is compressed by polynomial functions with respect to the flight envelope constraints and transmitted to the followers, where a model predictive control (MPC) outer loop controller specifies the command signals for the 7-h locally controlled dynamics with respect to the nonlinear constraints of the aircraft dynamics. Real time feasibility issues associated with the design are discussed.

Performance Characteristics of a Complete Vision Only Sense and Avoid System

The present paper investigates the real world feasibility of a purely vision based sense and avoid system, required for small unmanned aerial vehicles (UAV) to routinely access the national airspace. The two distinct functions, sensing and avoidance are integrated into a common framework. No information is exchanged between aircraft, only passive 2-D vision information is available to estimate the encountering traffic. Based on the predicted intruder motion the time of the encounter and the minimum distance are predicted. In case an intruder violates the minimum separation the onboard autopilot initiates an avoidance maneuver. The viability of the system is demonstrated on several estimation approaches, using Extended Kalman filter (EKF) and Unscented Kalman filter (UKF) implementations. Since it is shown that for certain type of observer movements the estimation process remains unobservable in bearings-only problems, the sensitivity of the estimation performance and the resulting avoidance response with respect to different intruder motion is investigated in a Monte-Carlo simulation. The system is tested on a high fidelity Hardwarein-the-Loop (HIL) simulation platform, where flight control algorithms, scene rendering, image processing and estimation algorithms are implemented individually over a network of computers with special emphasis on parallel implementation of computationally intensive tasks. Representative encounter scenarios are presented to provide performance measures, including detection time and achieved miss distance of distinctive approaches to assess the applicability of the results.

Performance analysis of a vision only sense and avoid system for small UAVs

This paper investigates the real world feasibility of a purely vision based sense and avoid system, required for small unmanned aerial vehicles (UAV) to routinely access the national airspace. No information is exchanged between aircraft, only passive 2-D vision information is available to estimate the encountering tra c. The viability of the system is demonstrated on several estimation approaches, using Extended Kalman lter (EKF) and Unscented Kalman lter (UKF) implementations. Since it is shown that for certain type of observer movements the estimation process remains unobservable in bearings-only problems, the sensitivity of the estimation performance with respect to the self motion of the own craft is investigated. The system is tested on a high delity Hardware-in-the-Loop (HIL) simulation platform, where ight control algorithms, scene rendering, image processing and estimation algorithms are implemented individually over a network of computers with special emphasis on parallel implementation of computationally intensive tasks. Representative encounter scenarios are presented to provide performance measures, including detection time and miss distance of distinctive approaches to assess the applicability of the results.

Constrained Freeway Traffic Control via Linear Parameter Varying Paradigms

A novel freeway traffic control design framework is proposed in the chapter. The derivation is based on the parameter-dependent reformulation of the second-order macroscopic freeway model. Hard physical constraints are handled implicitly, by introducing additional scheduling parameter for controller saturation measure. The ramp metering problem is then formulated as an induced \({\mathcal{L}}_{2}\) norm minimization, where the effects of undesired traffic phenomena are attenuated on the network throughput. The solution of the resulting problem involves convex optimization methods subjected to Linear Matrix Inequalities. A numerical example is given to validate the parameter-dependent controller and evaluate its effectiveness under various traffic situations.

Visual Detection and Implementation Aspects of a UAV See and Avoid System

One of the missing critical on-board safety equipment of the Unmanned Arial Vehicles (UAVs) is the collision avoidance system. In 2010 we launched a project to research and develop an SAA system for UAS. As the system will be on-board in a small aircraft we have to minimize the weight, the volume, and the power consumption. The acceptable power consumption is 1–2W and the mass of the control system is maximum 300–500g. Here we present the concept of a visual input based See and Avoid (SAA) system. This paper introduces the long range visual detection algorithm and the implementation aspect of the many core processor device.

Supervisory fault tolerant control of the GTM UAV using LPV methods

Abstract A multi-level reconfiguration framework is proposed for fault tolerant control of over-actuated aerial vehicles, where the levels indicate how much authority is given to the reconfiguration task. On the lowest, first level the fault is accommodated by modifying only the actuator/sensor configuration, so the fault remains hidden from the baseline controller. A dynamic reallocation scheme is applied on this level. The allocation mechanism exploits the actuator/sensor redundancy available on the aircraft. When the fault cannot be managed at the actuator/sensor level, the reconfiguration process has access to the baseline controller. Based on the LPV control framework, this is done by introducing fault-specific scheduling parameters. The baseline controller is designed to provide an acceptable performance level along all fault scenarios coded in these scheduling variables. The decision on which reconfiguration level has to be initiated in response to a fault is determined by a supervisor unit. The method is demonstrated on a full six-degrees-of-freedom nonlinear simulation model of the GTM UAV.

Estimation of Relative Direction Angle of Distant, Approaching Airplane in Sense-and-Avoid

Visual detection based sense and avoid problem is more and more important nowadays as UAVs are getting closer to entering remotely piloted or autonomously into the airspace. It is critical to gain as much information as possible from the silhouettes of the distant aircrafts. In our paper, we investigate the reachable accuracy of the orientation information of remote planes under different geometrical conditions, by identifying their wing lines from their detected wingtips. Under the assumption that the remote airplane is on a straight course, the error of the spatial discretization (pixelization), and the automatic detection error is calculated.

Dynamic inversion based velocity tracking control of road vehicles

Abstract The paper proposes a dynamic inversion based velocity tracking controller for four-wheeled road vehicles, that fits well into cooperating control systems. First, the dynamic inverse of the linear parameter varying (LPV) single-track model is constructed, and the global asymptotic stability of the zero dynamics is proved. Using the obtained stable inverse two controllers are designed depending on whether the yaw rate is measurable on not. In both cases the quadratic stability of the resulted closed-loop control structure is checked and proved. The dynamic properties of the proposed controllers are illustrated on simulation examples.