Validation of Neural Network Controllers for Uncertain Systems Through Keep-Close Approach: Robustness Analysis and Safety Verification

Among the major challenges in neural control system technology is the validation and certification of the safety and robustness of neural network (NN) controllers against various uncertainties including unmodelled dynamics, non-linearities, and time delays. One way in providing such validation guarantees is to maintain the closed-loop system output with a NN controller when its input changes within a bounded set, close to the output of a robustly performing closed-loop reference model. This paper presents a novel approach to analysing the performance and robustness of uncertain feedback systems with NN controllers. Due to the complexity of analysing such systems, the problem is reformulated as the problem of dynamical tracking errors between the closed-loop system with a neural controller and an ideal closed-loop reference model. Then, the approximation of the controller error is characterised by adopting the differential mean value theorem (DMV) and the Integral Quadratic Constraints (IQCs) technique. Moreover, the Relative Integral Square Error (RISE) and the Supreme Square Error (SSE) bounded set are derived for the output of the error dynamical system. The analysis is then performed by integrating Lyapunov theory with the IQCs-based technique. The resulting worst-case analysis provides the user a prior knowledge about the worst case of RISE and SSE between the reference closed-loop model and the uncertain system controlled by the neural controller. The suitability of the proposed technique is demonstrated by the results obtained on a nonlinear single-link robot system with a NN trained to control the movement of this mechanical system while keeping close to an ideal closed-loop reference model.Comment: 19 pages, 10 figures, Journal Paper submitted to IEEE Transactions on Control Systems Technolog

Autonomous cooperative visual navigation for planetary exploration robots

Planetary robotics navigation has attracted the great attention of many researchers in recent years. Localization is one of the most important problems for robots on another planet in the lack of GPS. The robots need to be able to know their location and the surrounding map in the environment concurrently, to work and communicate together on another planet. In the current work, a novel algorithm is designed to cooperatively localize a team of robots on another planet. Consequently, a robust algorithm is developed for cooperative Visual Odometry (VO) to localize each robot in a planetary environment while detecting both intra-loop closure and inter-loop closures using previously observed area by the robot and shared area from other robots, respectively. To validate the proposed algorithm, a comparison is provided between the proposed cooperative VO and the single version of VO. Accordingly, a planetary analogue real dataset is employed to investigate the accuracy of the proposed algorithm. The results promise the concept of cooperative VO to significantly increase the accuracy of localization. </p

On the Admissibility and Stability of Multiagent Nonlinear Interconnected Positive Systems With Heterogeneous Delays

International audienceMany multiagent interconnected systems include typical nonlinearities, which are highly sensitive to inevitable communication delays. This makes their analysis challenging and the generalization of results from linear interconnected systems theory to those nonlinear interconnected systems very limited. This article deals with the analysis of multiagent nonlinear interconnected positive systems (MANIPS). The main contributions of this work are twofold. Based on Perron–Frobenius theorem, we first study the “admissibility” property for MANIPS and show that it is a fundamental requirement for this category of systems. Then, using admissibility/positivity properties and sequences of functions theory, we propose a suitable Lyapunov function candidate to conduct the analysis of the dynamical behavior of such systems. We show that the stability of MANIPS is reduced to the positiveness property (i.e., negative or positive definiteness) of a new specific matrix-valued function ( Z ) that we derive in this article. Furthermore, the obtained results generalize the existing theory. The quality of the results achieved is demonstrated through the applications of the developed theory on cells with a multistage maturation process dynamical models