Robust Output Regulation for Autonomous Robots:self-learning mechanisms, task-space control and multi-agent systems

This thesis focuses on robust output regulation for autonomous robots. The control objective of output regulation is to design a feedback controller to achieve asymptotic tracking and/or disturbance rejection for a class of exogenous reference and/or disturbance while maintaining closed-loop stability. We investigate three research problems that pertain to the constructive design of robust output regulation for fully actuated Euler-Lagrange systems from centralized to distributed fashions. The first one is the global robust output regulation of second-order affine nonlinear systems with input disturbances that encompass the fully-actuated Euler-Lagrange systems. Based on a certainty equivalence principle method, we proposed a novel class of nonlinear internal models taking a cascade interconnection structure with strictly relaxed conditions than before. The second one is the output regulation for robot manipulators working in task-space. An internal model-based adaptive controller is designed to cope with uncertain manipulator kinematic and dynamic parameters, as well as unknown periodic reference trajectories generated by harmonic oscillators. The last one is the formation control of manipulators’ end-effector subject to external disturbances or parameter uncertainties. We present and analyze gradient descent-based distributed formation controllers for end-effectors. Internal models are used to reject external disturbances. Moreover, by introducing an extra integrator and an adaptive estimator for gravitational compensation and stabilization, respectively, we extend the proposed gradient-based design to the case where the plant parameters are not exactly known

Development of a MATLAB/Simulink - Arduino environment for experimental practices in control engineering teaching

This project presents the steps followed when implementing a platform based on MATLAB/Simulink and Arduino for the restoration of digital control practices. During this project, an Arduino shield has being designed. Along with this, a web page has also been created where all the material done during all this project is available and can be freely used. So anyone interested on doing a project can have a starting point instead of starting a project from scratch, which most of times this results hard to implement. Taking all this into account, the document is structured in the following manner. The first chapter talks about the hardware used and designed. The second one explains the software used and the configurations done on the laboratory’s PCs. After that, the web page Duino-Based Learning is explained, where you can find the five projects carried out in the "Control Automàtic" subject with their corresponding results. In this section too, as an additional research, the implemented indirect adaptive control will be explained, where the parameter estimation has been done by the Recursive Least Square algorithm. The last four sections before presenting the conclusions of the work, correspond to a satisfaction questionnaire done to the teachers that have used the setup, the costs and saves of the project, the environmental impact and the planning of the project respectively

Composite Learning Control With Application to Inverted Pendulums

Composite adaptive control (CAC) that integrates direct and indirect adaptive control techniques can achieve smaller tracking errors and faster parameter convergence compared with direct and indirect adaptive control techniques. However, the condition of persistent excitation (PE) still has to be satisfied to guarantee parameter convergence in CAC. This paper proposes a novel model reference composite learning control (MRCLC) strategy for a class of affine nonlinear systems with parametric uncertainties to guarantee parameter convergence without the PE condition. In the composite learning, an integral during a moving-time window is utilized to construct a prediction error, a linear filter is applied to alleviate the derivation of plant states, and both the tracking error and the prediction error are applied to update parametric estimates. It is proven that the closed-loop system achieves global exponential-like stability under interval excitation rather than PE of regression functions. The effectiveness of the proposed MRCLC has been verified by the application to an inverted pendulum control problem.Comment: 5 pages, 6 figures, conference submissio

Active Sampling-based Binary Verification of Dynamical Systems

Nonlinear, adaptive, or otherwise complex control techniques are increasingly relied upon to ensure the safety of systems operating in uncertain environments. However, the nonlinearity of the resulting closed-loop system complicates verification that the system does in fact satisfy those requirements at all possible operating conditions. While analytical proof-based techniques and finite abstractions can be used to provably verify the closed-loop system's response at different operating conditions, they often produce conservative approximations due to restrictive assumptions and are difficult to construct in many applications. In contrast, popular statistical verification techniques relax the restrictions and instead rely upon simulations to construct statistical or probabilistic guarantees. This work presents a data-driven statistical verification procedure that instead constructs statistical learning models from simulated training data to separate the set of possible perturbations into "safe" and "unsafe" subsets. Binary evaluations of closed-loop system requirement satisfaction at various realizations of the uncertainties are obtained through temporal logic robustness metrics, which are then used to construct predictive models of requirement satisfaction over the full set of possible uncertainties. As the accuracy of these predictive statistical models is inherently coupled to the quality of the training data, an active learning algorithm selects additional sample points in order to maximize the expected change in the data-driven model and thus, indirectly, minimize the prediction error. Various case studies demonstrate the closed-loop verification procedure and highlight improvements in prediction error over both existing analytical and statistical verification techniques.Comment: 23 page

Adaptive and Supertwisting Adaptive Spacecraft Orbit Control Around Asteroids

This paper addresses the development of control systems for the orbit control of spacecraft around irregularly shaped rotating asteroids with uncertain parameters. The objective is to steer the spacecraft along prescribed orbits. First, a nonlinear adaptive law for orbit control was designed. This was followed by the design of a supertwisting adaptive (STWA) control system. In the closed-loop system, which includes the adaptive law or the STWA law, all the signals remain bounded, and the trajectory tracking error asymptotically converges to zero for any initial condition. Finally, under the assumption of boundedness of the derivative of the uncertain functions of the model in a region of the state space, a supertwisting control (STW) law for finite-time convergence of the trajectory was obtained. Based on the Lyapunov theory, stability properties of the closed-loop systems were analyzed. Simulation results for 433 Eros and Ida asteroids were presented for illustration. The results showed that control of spacecraft along closed orbits or to a fixed point is accomplished using each of these controllers, despite uncertainties in the parameters of the asteroid models