Holger Voos

Nonlinear control of a quadrotor micro-UAV using feedback-linearization

Four-rotor micro aerial robots, so called quadrotor UAVs, are one of the most preferred type of unmanned aerial vehicles for near-area surveillance and exploration both in military and commercial in- and outdoor applications. The reason is the very easy construction and steering principle using four rotors in a cross configuration. However, stabilizing control and guidance of these vehicles is a difficult task because of the nonlinear dynamic behavior. In addition, the small payload and the reduced processing power of the onboard electronics are further limitations for any control system implementation. This paper describes the development of a nonlinear vehicle control system based on a decomposition into a nested structure and feedback linearization which can be implemented on an embedded microcontroller. Some first simulation results underline the performance of this new control approach for the current realization.

Observer-Based Approach for Fractional-Order Chaotic Synchronization and Secure Communication

This paper presents a method based on the state observer design for constructing a chaotically synchronized systems. Fractional-order direct Lyapunov theorem is used to derive the closed-loop asymptotic stability. The gains of the observer and observer-based controller are obtained in terms of linear matrix inequalities (LMIs) formulation. The proposed approach is then applied to secure communications. The method combines chaotic masking and chaotic modulation, where the information signal is injected into the transmitter and simultaneously transmitted to the receiver. Chaotic synchronization and chaotic communication are achieved simultaneously via a state observer design technique. The fractional-order chaotic Lorenz and Lü systems are given to demonstrate the applicability of the proposed approach.

Design of unknown input fractional-order observers for fractional-order systems

Abstract This paper considers a method of designing fractional-order observers for continuous-time linear fractional-order systems with unknown inputs. Conditions for the existence of these observers are given. Sufficient conditions for the asymptotical stability of fractional-order observer errors with the fractional order α satisfying 0 < α < 2 are derived in terms of linear matrix inequalities. Two numerical examples are given to demonstrate the applicability of the proposed approach, where the fractional order α belongs to 1≤α<2 and 0<α≤1, respectively. A stability analysis of the fractional-order error system is made and it is shown that the fractional-order observers are as stable as their integer order counterpart and guarantee better convergence of the estimation error.

Nonlinear tracking and landing controller for quadrotor aerial robots

Quadrotor UAVs are one of the most preferred type of small unmanned aerial vehicles because of the very simple mechanical construction and propulsion principle. However, the nonlinear dynamic behavior requires a more advanced stabilizing control and guidance of these vehicles. In addition, the small payload reduces the amount of batteries that can be carried and thus also limits the operating range of the UAV. One possible solution for a range extension is the application of a base station for recharging purpose even during operation. In order to increase the efficiency of the overall system further, a mobile base station will be applied here. However, landing on a moving base station requires autonomous tracking and landing control of the UAV. In this paper, a novel nonlinear autopilot for quadrotor UAVs is extended with a tracking and landing controller to fulfill the required task. First simulation and experimental results underline the performance of this new control approach for the current realization.

Nonlinear and neural network-based control of a small four-rotor aerial robot

Small four-rotor aerial robots, so called quadrotor UAVs, have an enormous potential for all kind of near-area surveillance and exploration in military and commercial applications. In addition, they offer the possibility to fly either in-or outdoor. However, stabilizing control and guidance of these vehicles is a difficult task because of the nonlinear dynamic behavior. This paper describes the development of a nonlinear vehicle control system based on a combination of state-dependent Riccati equations (SDRE) and neural networks. Some first simulation results underline the performance of this new control approach for the current realization.

Controller design for quadrotor UAVs using reinforcement learning

Quadrotor UAVs are one of the most preferred type of small unmanned aerial vehicles because of the very simple mechanical construction and propulsion principle. However, the nonlinear dynamic behavior requires a rather advanced stabilizing control of these vehicles. One possible approach that relaxes the difficult task of nonlinear control design is the application of a learning algorithm that allows the training of suitable control actions. Here we apply reinforcement learning as one form of unsupervised learning. In this paper, we first propose a nonlinear autopilot for quadrotor UAVs based on feedback linearization. This controller is then compared to an autopilot which has been learned by reinforcement learning using fitted value iteration with regard to design effort and performance. First simulation and experimental results underline the outcome of this comparison.

Towards an Autonomous Vision-Based Unmanned Aerial System against Wildlife Poachers.

Poaching is an illegal activity that remains out of control in many countries. Based on the 2014 report of the United Nations and Interpol, the illegal trade of global wildlife and natural resources amounts to nearly

Design and simulation of a real-time implementable energy-efficient model-predictive cruise controller for electric vehicles

213 billion every year, which is even helping to fund armed conflicts. Poaching activities around the world are further pushing many animal species on the brink of extinction. Unfortunately, the traditional methods to fight against poachers are not enough, hence the new demands for more efficient approaches. In this context, the use of new technologies on sensors and algorithms, as well as aerial platforms is crucial to face the high increase of poaching activities in the last few years. Our work is focused on the use of vision sensors on UAVs for the detection and tracking of animals and poachers, as well as the use of such sensors to control quadrotors during autonomous vehicle following and autonomous landing.

Vision based fuzzy control autonomous landing with UAVs: From V-REP to real experiments

This paper presents the design of a novel energy-efficient model-predictive cruise controller for electric vehicles as well a simulation model of the longitudinal vehicle dynamics and its energy consumption. Both, the controller and the dynamic model are designed to meet the properties of a series-production electric vehicle whose characteristics are identified and verified by measurements. A predictive eco-cruise controller involves the minimisation of a compromise between terms related to driving speed and energy consumption which are in general both described by nonlinear differential equations. Considering the nonlinearities is essential for a proper prediction of the system states over the prediction horizon to achieve the desired energy-saving behaviour. In this work, the vehicle motion equation is reformulated in terms of the kinetic energy of the moving vehicle which leads to a linear differential equation without loss of information. The energy consumption is modeled implicitly by exploiting the special form of the optimisation problem. The reformulations finally lead to a model-predictive control approach with quadratic cost function, linear prediction model and linear constraints that corresponds to a piecewise linear system behaviour and allows a fast real-time implementation with guaranteed convergence. Simulation results of the MPC controller and the simulation model in closed-loop operation finally provide a proof of concept.

An efficient nonlinear model-predictive eco-cruise control for electric vehicles

This paper is focused on the design of a vision based control approach for the autonomous landing task of Vertical Take-off and Landing (VTOL) Unmanned Aerial Vehicles (UAVs). Here is presented the setup of a simulated environment developed in V-REP connected to ROS, and its uses for tuning a vision based control approach. In this work, a Fuzzy control approach was proposed to command the UAVs vertical, longitudinal, lateral and orientation velocities. The UAVs pose estimation was done based on a vision algorithm and the knowledge of the landing target. Real experiments with a quadrotor landing in a moving platform are also presented.