Nonlinear Deterministic Observer for Inertial Navigation using Ultra-wideband and IMU Sensor Fusion

Navigation in Global Positioning Systems (GPS)-denied environments requires robust estimators reliant on fusion of inertial sensors able to estimate rigid-body's orientation, position, and linear velocity. Ultra-wideband (UWB) and Inertial Measurement Unit (IMU) represent low-cost measurement technology that can be utilized for successful Inertial Navigation. This paper presents a nonlinear deterministic navigation observer in a continuous form that directly employs UWB and IMU measurements. The estimator is developed on the extended Special Euclidean Group $\mathbb{SE}_{2}\left(3\right)$ and ensures exponential convergence of the closed loop error signals starting from almost any initial condition. The discrete version of the proposed observer is tested using a publicly available real-world dataset of a drone flight. Keywords: Ultra-wideband, Inertial measurement unit, Sensor Fusion, Positioning system, GPS-denied navigation.Comment: 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS

Online Multi-Objective Model-Independent Adaptive Tracking Mechanism for Dynamical Systems

The optimal tracking problem is addressed in the robotics literature by using a variety of robust and adaptive control approaches. However, these schemes are associated with implementation limitations such as applicability in uncertain dynamical environments with complete or partial model-based control structures, complexity and integrity in discrete-time environments, and scalability in complex coupled dynamical systems. An online adaptive learning mechanism is developed to tackle the above limitations and provide a generalized solution platform for a class of tracking control problems. This scheme minimizes the tracking errors and optimizes the overall dynamical behavior using simultaneous linear feedback control strategies. Reinforcement learning approaches based on value iteration processes are adopted to solve the underlying Bellman optimality equations. The resulting control strategies are updated in real time in an interactive manner without requiring any information about the dynamics of the underlying systems. Means of adaptive critics are employed to approximate the optimal solving value functions and the associated control strategies in real time. The proposed adaptive tracking mechanism is illustrated in simulation to control a flexible wing aircraft under uncertain aerodynamic learning environment

Model-Free Gradient-Based Adaptive Learning Controller for an Unmanned Flexible Wing Aircraft

Classical gradient-based approximate dynamic programming approaches provide reliable and fast solution platforms for various optimal control problems. However, their dependence on accurate modeling approaches poses a major concern, where the efficiency of the proposed solutions are severely degraded in the case of uncertain dynamical environments. Herein, a novel online adaptive learning framework is introduced to solve action-dependent dual heuristic dynamic programming problems. The approach does not depend on the dynamical models of the considered systems. Instead, it employs optimization principles to produce model-free control strategies. A policy iteration process is employed to solve the underlying Hamilton⁻Jacobi⁻Bellman equation using means of adaptive critics, where a layer of separate actor-critic neural networks is employed along with gradient descent adaptation rules. A Riccati development is introduced and shown to be equivalent to solving the underlying Hamilton⁻Jacobi⁻Bellman equation. The proposed approach is applied on the challenging weight shift control problem of a flexible wing aircraft. The continuous nonlinear deformation in the aircraft’s flexible wing leads to various aerodynamic variations at different trim speeds, which makes its auto-pilot control a complicated task. Series of numerical simulations were carried out to demonstrate the effectiveness of the suggested strategy

A Non-Linear-Threshold-Accepting Function Based Algorithm for the Solution of Economic Dispatch Problem

This article introduces a novel heuristic algorithm based on Non-Linear Threshold Accepting Function to solve the challenging non-convex economic dispatch problem. Economic dispatch is a power system management tool; it is used to allocate the total power generation to the generating units to meet the active load demand. The power systems are highly nonlinear due to the physical and operational constraints. The complexity of the resulting non-convex objective cost function led to inabilities to solve the problem by using analytical approaches, especially in the case of large-scale problems. Optimization techniques based on heuristics are used to overcome these difficulties. The Non-Linear Threshold Accepting Algorithm has demonstrated efficiency in solving various instances of static and dynamic allocation and scheduling problems but has never been applied to solve the economic dispatch problem. Existing benchmark systems are used to evaluate the performance of the proposed heuristic. Additional random instances with different sizes are generated to compare the adopted heuristic to the Harmony Search and the Whale Optimization Algorithms. The obtained results showed the superiority of the proposed algorithm in finding, for all considered instances, a high-quality solution in minimum computational time

Approximate and reinforcement learning techniques to solve non-convex economic dispatch problems

Economic Dispatch is one of the power systems management tools. It is used to allocate an amount of power generation to the generating units to meet the active load demands. The Economic Dispatch problem is a large-scale nonlinear constrained optimization problem. In this paper, two novel techniques are developed to solve the non-convex Economic Dispatch problem. Firstly, a novel approximation of the non-convex generation cost function is developed to solve non-convex Economic Dispatch problem with the transmission losses. This approximation enables the use of gradient and Newton techniques to solve the non-convex Economic Dispatch problem. Secondly, Q-Learning with eligibility traces technique is adopted to solve the non-convex Economic Dispatch problem with valve point loading effects, multiple fuel options, and power transmission losses. The eligibility traces are used to speed up the Q-Learning process. This technique showed superior results compared to other heuristic techniques