Abstract

The plants of nano air vehicles (NAVs) are generally unstable, adversely coupled, and uncertain. Besides, the autopilot hardware of a NAV has limited sensing and computational capabilities. Hence, these vehicles need a single controller referred to as robust simultaneously stabilizing decoupling (RSSD) output feedback controller that achieves simultaneous stabilization (SS), desired decoupling, robustness, and performance for a finite set of unstable multi-input-multioutput adversely coupled uncertain plants. To synthesize an RSSD output feedback controller, a new method that is based on a central plant is proposed in this article. Given a finite set of plants for SS, we considered a plant in this set that has the smallest maximum v-gap metric as the central plant. Following this, the sufficient condition for the existence of a simultaneous stabilizing controller associated with such a plant is described. The decoupling feature is then appended to this controller using the properties of the eigenstructure assignment method. Afterward, the sufficient conditions for the existence of an RSSD output feedback controller are obtained. Using these sufficient conditions, a new optimization problem for the synthesis of an RSSD output feedback controller is formulated. To solve this optimization problem, a new genetic algorithm-based offline iterative algorithm is developed. The effectiveness of this iterative algorithm is then demonstrated by generating an RSSD controller for a fixed-wing NAV. The performance of this controller is validated through numerical and hardware-in-the-loop simulations.

Abstract

Control of batch processes is a difficult task due to their complex nonlinear dynamics and unsteady-state operating conditions within batch and batch-to-batch. It is expected that some of these challenges can be addressed by developing control strategies that directly interact with the process and learning from experiences. Recent studies in the literature have indicated the advantage of having an ensemble of actors in actor-critic Reinforcement Learning (RL) frameworks for improving the policy. The present study proposes an actor-critic RL algorithm, namely, twin actor twin delayed deep deterministic policy gradient (TATD3), by incorporating twin actor networks in the existing twin-delayed deep deterministic policy gradient (TD3) algorithm for the continuous control. In addition, two types of novel reward functions are also proposed for TATD3 controller. We showcase the efficacy of the TATD3 based controller for various batch process examples by comparing it with some of the existing RL algorithms presented in the literature.

Abstract

Control barrier function (CBF) constraints provide a rigorous characterization of the space of control inputs that ensure the satisfaction of state constraints, such as collision avoidance, at all time instants. However, CBFs are highly nonlinear and non-convex and thus, when incorporated within an optimization-based algorithm such as Model Predictive Control (MPC), leads to a computationally challenging problem. Existing works by-pass the computational intractability by collapsing the horizon of the MPC to a single step, although this comes at the cost of severe degradation of performance. In this paper, we present two contributions to ensure the real-time performance of CBFs based MPC over long horizons in the context of multi-robot collision avoidance. First, we propose a customized Project Gradient Descent Method that incurs minimal computational overhead over existing one-step approaches but leads to a substantial improvement in trajectory smoothness, time to reach the goal, etc. Our second contribution lies in applying the proposed MPC to both quadrotors and fixed-wing aerial aircrafts (FWA). In particular, we show that the formulation for the quadrotors can be readily extended to the latter by deriving additional CBFs for the curvature and forward velocity constraints. We validate our algorithm with an extensive simulation of up to 10 robots in challenging benchmark scenarios.

Abstract

Current aerial robots are increasingly adaptive; they can morph to enable operation in changing conditions to complete diverse missions. Each mission may require the robot to conduct a different task. A conventional learning approach can handle these variations when the system is trained for similar tasks in a representative environment. However, it may result in overfitting to the new data stream or the failure to adapt, leading to degradation or a potential crash. These problems can be mitigated with an excessive amount of data and embedded model, but the computational power and the memory of the aerial robots are limited. In order to address the variations in the model, environment as well as the tasks within onboard computation limitations, we propose a deep neuromorphic controller approach with variable topologies to handle each different condition and the data stream with a feasible computation and memory allocation. The proposed approach is based on a deep neuromorphic (multi and variable layered neural network) controller with dynamic depth and progressive layer adaptation for each new data stream. This adaptive structure is combined with a switching function to form a sliding mode controller. The network parameter update rule guarantees the stability of the closed loop system by the convergence of the error dynamics to the sliding surface. Being the first implementation on an aerial robot in this context, the results illustrate the adaptation capability, stability, computational efficiency as well as the real-time validation.

Abstract

— This paper presents a model predictive control (MPC) based algorithm for tracking multiple targets using a swarm of unmanned aerial vehicles (UAVs). All the UAVs belong to fixed-wing category with constraints on flight velocity, climb rate and turn rate. Each UAV carries a camera to detect and track the target. Two cases are considered where for the first case, the number of the UAVs is equal to the number of targets. For the second case, the number of UAVs is lesser than the number of targets leading to a conservative solution where the objective is to maximize the average time duration for which the targets are in the field-of-view (FOV) of any one of the UAV’s camera. A data driven Gaussian process (GP) based model is developed to relate the hyperparameters used in MPC to the mission efficiency. Bayesian optimization is performed to obtain the hyperparameters of the MPC that maximize the mission efficiency. Numerical simulations are performed for both cases using algorithm based on distributed MPC formulation. A performance comparison is provided with the centralized MPC formulation.

Abstract

The persistent depletion of fossil fuels has encouraged mankind to look for alternatives fuels that are renewable and environment-friendly. One of the promising and renewable alternatives to fossil fuels is bio-diesel produced by means of the batch transesterification process. Control of the batch transesterification process is difficult due to its complex and non-linear dynamics. It is expected that some of these challenges can be addressed by developing control strategies that directly interact with the process and learning from the experiences. To achieve the same, this study explores the feasibility of reinforcement learning (RL) based control of the batch transesterification process. In particular, the present study exploits the application of twin delayed deep deterministic policy gradient (TD3) based RL for the continuous control of the batch transesterification process. These results showcase that TD3 based controller is able to control batch transesterification process and can be a promising direction towards the goal of artificial intelligence-based control in process industries.

Abstract

In this article, we propose a bilevel segmentation framework with metacognitive learning (BS-McL) to detect power lines with an RGB camera mounted on an unmanned aerial vehicle (UAV) platform. The proposed framework consists of two levels based on spectral and spatial techniques. In the first level, spectral classification is carried out using the McL method, which is an evolving online learning neural network architecture. Due to similarities in spectral intensities, few nonpower line pixels are grouped along with power line pixels. The nonpower line pixels are removed by spatial segmentation in the second level. The second level includes morphological operations such as geometric features (shape and density indices), which are applied to detect the power lines. The processing steps of BS-McL are illustrated using a synthetic image of size 9 x 6 pixels. Also, two datasets consisting of 64 images with varying backgrounds, different locations, and dimensions of power lines are used to demonstrate the performance of the proposed BS-McL. The obtained results for BS-McL are compared with five commonly used methods. For both datasets, the efficiency of the BS-McL for power line extraction is better than for the methods used for comparison. Furthermore, the trained knowledge from our experimental set-up (Dataset 1: suburban scene) can be transferred to another dataset that is available publicly (Dataset 2: urban and mountain scenes) if the power line spectral values are in relevance with the distribution in the training dataset. The proposed approach BS-McL is based on online learning with a self-adaptive architecture, which provides improved generalization ability.

Abstract

The recent research is focused on the development of an advanced interceptor missile that has hit-to-kill accuracy against ballistic targets performing evasive maneuvers. In this paper, a guidance law that achieves hit-to-kill accuracy against ballistic target executing worst-case maneuvers is developed using second-order sliding mode control and optimal control. The guidance law thus developed is continuous and has minimum time convergence for worst-case target maneuvers. The performance of the continuous guidance law with minimum time convergence is evaluated through numerical simulations against ballistic targets executing step maneuvers with changing polarity and weaving maneuvers.

Abstract

A solution to the waypoint navigation problem for the fixed wing micro air vehicles (MAV) having a severe coupling between longitudinal and lateral dynamics, in the framework of integrated guidance and control (IGC) is addressed in this paper. IGC yields a single step solution to the waypoint navigation problem, unlike conventional multiple loop design. The pure proportional navigation (PPN) guidance law is integrated with the coupled MAV dynamics. A multivariable static output feedback (SOF) controller is designed for the linear state space model formulated in IGC framework. A waypoint navigation algorithm is proposed that handles the minimum turn radius constraint of the MAV and also evaluates the feasibility of reaching a waypoint. Non-linear simulations with and without wind disturbances are performed on a high fidelity 150 mm wingspan MAV model to demonstrate the proposed waypoint navigation algorithm.

Abstract

This paper analyzes the effects of propeller flow on the linear coupled longitudinal and lateral dynamics of a 150 mm wingspan fixed wing micro air vehicle (MAV). The effects propeller flow on the lift, drag, pitching moment and side force is obtained through wind tunnel tests. The aerodynamic forces and moments are modeled as a function of angle of attack, sideslip angle, control surface deflection and propeller rotation per minute. The nonlinear six degrees of freedom model is linearized about straight and constant altitude flight conditions for different trim airspeed to obtain linear coupled longitudinal and lateral state space model. The eigenvalues and eigenvectors of linear coupled longitudinal and lateral state space model are compared with and without propeller flow effects. The variation in the natural frequencies and damping ratios of short period mode, phugoid mode and Dutch roll mode are analyzed for various trim airspeed. An increase in the natural frequency is observed for phugoid mode and Dutch roll mode with propeller effects. The stability of the spiral mode is enhanced by the propeller flow and also the response of the roll subsidence mode is faster with propeller effects. Detailed analysis of eigenvalues and eigenvectors shows the importance of incorporating propeller flow in analyzing the dynamics of the MAV