Investigations of Model-Free Sliding Mode Control Algorithms including Application to Autonomous Quadrotor Flight

Sliding mode control is a robust nonlinear control algorithm that has been used to implement tracking controllers for unmanned aircraft systems that are robust to modeling uncertainty and exogenous disturbances, thereby providing excellent performance for autonomous operation. A significant advance in the application of sliding mode control for unmanned aircraft systems would be adaptation of a model-free sliding mode control algorithm, since the most complex and time-consuming aspect of implementation of sliding mode control is the derivation of the control law with incorporation of the system model, a process required to be performed for each individual application of sliding mode control. The performance of four different model-free sliding mode control algorithms was compared in simulation using a variety of aerial system models and real-world disturbances (e.g. the effects of discretization and state estimation). The two best performing algorithms were shown to exhibit very similar behavior. These two algorithms were implemented on a quadrotor (both in simulation and using real-world hardware) and the performance was compared to a traditional PID-based controller using the same state estimation algorithm and control setup. Simulation results show the model-free sliding mode control algorithms exhibit similar performance to PID controllers without the tedious tuning process. Comparison between the two model-free sliding mode control algorithms showed very similar performance as measured by the quadratic means of tracking errors. Flight testing showed that while a model-free sliding mode control algorithm is capable of controlling realworld hardware, further characterization and significant improvements are required before it is a viable alternative to conventional control algorithms. Large tracking errors were observed for both the model-free sliding mode control and PID based flight controllers and the performance was characterized as unacceptable for most applications. The poor performance of both controllers suggests tracking errors could be attributed to errors in state estimation, which effectively introduce unknown dynamics into the feedback loop. Further testing with improved state estimation would allow for more conclusions to be drawn about the performance characteristics of the model-free sliding mode control algorithms

Diseño de controladores continuos convergentes por un tiempo fijo para sistemas dinámicos con incertidumbre

Este documento presenta controladores no lineales que proveen convergencia en tiempo fijo al origen (o a una vecindad del origen) para sistemas dinámicos de alto orden sujetos a incertidumbres (disturbios deterministicos no desvanescentes y disturbios estocásticos desvanescentes dependientes de los estados y el tiempo). Dos de los tres controladores diseñados incluyen un diferenciador convergente en tiempo fijo, un observador de disturbios convergente en tiempo fijo, y un regulador convergente en tiempo fijo. El diferenciador se da en el caso que el ´único estado medible del sistema dinámico es el de mayor grado relativo. El observador de disturbios convergente en tiempo fijo se emplea para estimar variaciones de disturbios no desvanecentes y no acotados. En caso de que las cotas para los disturbios sean desconocidas se incluye un observador adaptable convergente en tiempo fijo caracterizado por no incrementar de manera excesiva las ganancias del controlador. En cuanto a la presencia simultanea de disturbios determinísticos no desvanescentes y disturbios estocásticos desvanescentes dependientes de los estados y el tiempo, se presenta un algoritmo Super-twisting estocástico convergente en tiempo fijo. El problema de estimación del tiempo de convergencia de los controladores se resuelve calculando una cota superior uniforme del tiempo fijo de convergencia. Finalmente, los algoritmos diseñados se verifican en dos casos de estudio: Un motor DC con armadura y un problema de gestión de stocks. Resultados de las simulaciones confirman convergencia en tiempo fijo y robustez de los controladores diseñados

A High-Order Sliding-Mode Adaptive Observer for Uncertain Nonlinear Systems

International audienceA high-order sliding-mode adaptive observer is proposed to solve the problem of adaptive estimation, i.e., the simultaneous estimation of the state and parameters, for a class of uncertain nonlinear systems in the presence of external disturbances, that does not need to satisfy a relative degree condition equal to one. This approach is based on a highorder sliding-mode observer and a nonlinear parameter identification algorithm. The practical, global and uniform asymptotic stability of the adaptive estimation error, despite the external disturbances, is guaranteed through the small-gain theorem. The convergence proofs are developed based on Lyapunov and inputto-state stability theories. Some simulation results illustrate the performance of the proposed high-order sliding-mode adaptive observer

Vision-based control of multi-agent systems

Scope and Methodology of Study: Creating systems with multiple autonomous vehicles places severe demands on the design of decision-making supervisors, cooperative control schemes, and communication strategies. In last years, several approaches have been developed in the literature. Most of them solve the vehicle coordination problem assuming some kind of communications between team members. However, communications make the group sensitive to failure and restrict the applicability of the controllers to teams of friendly robots. This dissertation deals with the problem of designing decentralized controllers that use just local sensor information to achieve some group goals.Findings and Conclusions: This dissertation presents a decentralized architecture for vision-based stabilization of unmanned vehicles moving in formation. The architecture consists of two main components: (i) a vision system, and (ii) vision-based control algorithms. The vision system is capable of recognizing and localizing robots. It is a model-based scheme composed of three main components: image acquisition and processing, robot identification, and pose estimation.Using vision information, we address the problem of stabilizing groups of mobile robots in leader- or two leader-follower formations. The strategies use relative pose between a robot and its designated leader or leaders to achieve formation objectives. Several leader-follower formation control algorithms, which ensure asymptotic coordinated motion, are described and compared. Lyapunov's stability theory-based analysis and numerical simulations in a realistic tridimensional environment show the stability properties of the control approaches

Control of a Silicone soft tripod robot via uncertainty compensation

International audienceSoft robot is an emergent research field which has variant promising applications, and the control of such robots is still challenging. Unlike using different techniques (such as Beam theory, Cosserat theory or high dimensional finite-element method) to model the dynamics of soft robots, this paper introduces a simplified nominal model with uncertainty to describe its dynamic behavior. The link between this simple model and the finite-element method has been established, and a robust controller is proposed, by compensating the uncertainty which is estimated in a finite time by applying different types of estimators. The experiments have been made for different scenarios, and the corresponding results show the efficiency of the proposed method

Nonlinear Adaptive Control of Drilling Processes

This work deals with the modeling and control of automated drilling operations. Advances in drilling automation are of substantial importance because improvements in drilling control algorithms will result in more efficient drilling, which is beneficial from both economic and environmental points of view. While the primary application of the results is extraction of natural resources, potentially there exists a wide range of applications, including offshore exploration, archaeological research, and automated extraterrestrial mining, where implementation of new methods and control algorithms for drilling processes can bring substantial benefits. The main contribution of the thesis is development of new methods and algorithms for control of drilling processes in industrial drilling systems, ensuring stability and high performance characteristics. The problems of regulation of vertical penetration rate and drilling power in rotary drilling systems are solved; as a result, stability and vibration mitigation is ensured. A number of challenges is addressed, such as complexity and nonlinearity of the drilling model, lack of information about environment and parameters of the drilling system itself, and poor communication between downhole sensors and ground-level equipment. Several cases are considered, depending on the amount of information that is available in advance or in real time. Two mathematical models of the drilling system are investigated: one is finite-dimensional, and another is a distributed parameter model. Several solutions are proposed for both of them, using methods of adaptive, robust, and sliding mode control, and comparisons are made. Feasibility and efficiency of the proposed control algorithms are confirmed by simulations in MATLAB/Simulink