Comprehensive review on controller for leader-follower robotic system

985-1007This paper presents a comprehensive review of the leader-follower robotics system. The aim of this paper is to find and elaborate on the current trends in the swarm robotic system, leader-follower, and multi-agent system. Another part of this review will focus on finding the trend of controller utilized by previous researchers in the leader-follower system. The controller that is commonly applied by the researchers is mostly adaptive and non-linear controllers. The paper also explores the subject of study or system used during the research which normally employs multi-robot, multi-agent, space flying, reconfigurable system, multi-legs system or unmanned system. Another aspect of this paper concentrates on the topology employed by the researchers when they conducted simulation or experimental studies

An Event-Triggered Robust Attitude Control of Flexible Spacecraft With Modified Rodrigues Parameters Under Limited Communication

The attitude regulation of spacecraft using continuous time execution of the control law is not always affordable for the low-cost satellites with limited wireless resources. Of late, within the ambit of control of systems over networks, event-triggered control has proved to be instrumental in ensuring acceptable closed-loop performance while respecting bandwidth constraints of the underlying network. Aligned with these design objectives, a robust event-triggered attitude control algorithm is proposed to regulate the orientation of a flexible spacecraft subjected to parametric uncertainties, external disturbances, and vibrations due to flexible appendages. The control law is developed using a state-dependent single feedback vector, which further assists in obeying the constrained network. The current information of this vector is updated to the onboard controller only when the predefined triggering condition is satisfied. Thus, the control input is updated through communication channel only when there is a need, which ultimately helps in saving the communication resources. The system trajectories, under the proposed approach, are guaranteed to be uniformly ultimately bounded (UUB) in a small neighborhood of origin by using a high gain. Moreover, the practical applicability of the proposed scheme is also proved by showing the Zeno free behavior in the proposed control, i.e., it avoids the accumulation of the triggering sequence. The numerical simulations results are indeed encouraging and illustrate the effectiveness of the designed controller. Moreover, the numerical comparative analysis shows that the proposed approach performs better than periodically sampled data technique and sliding mode-based event-triggered technique.Qatar UniversityScopu

Nonlinear Robust Neural Control with Applications to Aerospace Vehicles

Nonlinear control has become increasingly more used over the last few decades, mainly due to the research and development of better analysis tools, that can simulate real-world problems, which are almost always, nonlinear. Nonlinear controllers have the advantage of being more accurate and efficient when dealing with complex scenarios, such as orbit control, satellite rendezvous, or attitude control, compared to linear ones. However, common nonlinear control techniques require having a high-fidelity model, which is often not the case, thereby limiting their use. Additionally, rapid advancements in the field of machine learning have raised the possibility of using tools like neural networks to learn the dynamics of nonlinear systems in an effort to compute control inputs without the need to solve the highly complex mathematical equations that some nonlinear controllers require to solve, in real-time, therefore bypassing the need of higher computational power, which can reduce costs and weight, in space missions. This dissertation will focus on the development of a neural controller based on H8 pseudolinear control, to be applied to the satellite attitude control problem, as well as the satellite orbit control problem. The resulting controller is proven to be robust when dealing with important disturbances that are relevant in space missions, due to being trained using H8 controller data. Moreover, since the original controller is pseudolinear, the neural controller can capture the nonlinearities that exist in the equations of motion as well as in the attitude dynamics equations.Nas últimas décadas, o controlo não-linear tem sido cada vez mais utilizado, maioritariamente devido ao desenvolvimento de melhores ferramentas de análise, utilizadas para a simulação problemas reais, que tendem a ser não-lineares. Os controladores não-lineares têm a vantagem de serem mais precisos e eficientes quando utilizados em situações complexas, como controlo orbital, rendezvous de satélites, e controlo de atitude, comparados com controladores lineares. No entanto, as técnicas comuns de controlo não-linear requerem o uso de modelos com alto grau de fidelidade, o que muitas vezes não é o caso, limitando assim a sua utilização. Além disso, os rápidos avanços no campo de machine learning levantaram a possibilidade de utilizar ferramentas como redes neuronais para aprender a dinâmica de sistemas não lineares, numa tentativa de poder computar as entradas de controlo sem a necessidade de resolver as equações matemáticas altamente complexas que alguns controladores não lineares necessitam que sejam resolvidas, em tempo real, contornando assim a necessidade de maior potência computacional, que pode reduzir custos e peso, em missões espaciais. Esta dissertação focar-se-á no desenvolvimento de um controlador neuronal, baseado em controlo pseudolinear por H8, com o intuito de ser aplicado no problema de controlo orbital, bem como no problema de controlo de atitude. O controlador resultante provou ser robusto ao lidar com perturbações importantes, relevantes em missões espaciais, devido ao facto de ter sido treinado usando dados do controlador H8. Além disso, como o controlador original é pseudolinear, o controlador neuronal pode captar as dinâmicas não lineares que existem nas equações de movimento, bem como nas equações da dinâmica de atitude

The 1st International Conference on Computational Engineering and Intelligent Systems

Computational engineering, artificial intelligence and smart systems constitute a hot multidisciplinary topic contrasting computer science, engineering and applied mathematics that created a variety of fascinating intelligent systems. Computational engineering encloses fundamental engineering and science blended with the advanced knowledge of mathematics, algorithms and computer languages. It is concerned with the modeling and simulation of complex systems and data processing methods. Computing and artificial intelligence lead to smart systems that are advanced machines designed to fulfill certain specifications. This proceedings book is a collection of papers presented at the first International Conference on Computational Engineering and Intelligent Systems (ICCEIS2021), held online in the period December 10-12, 2021. The collection offers a wide scope of engineering topics, including smart grids, intelligent control, artificial intelligence, optimization, microelectronics and telecommunication systems. The contributions included in this book are of high quality, present details concerning the topics in a succinct way, and can be used as excellent reference and support for readers regarding the field of computational engineering, artificial intelligence and smart system

Integrated Optimal and Robust Control of Spacecraft in Proximity Operations

With the rapid growth of space activities and advancement of aerospace science and technology, many autonomous space missions have been proliferating in recent decades. Control of spacecraft in proximity operations is of great importance to accomplish these missions. The research in this dissertation aims to provide a precise, efficient, optimal, and robust controller to ensure successful spacecraft proximity operations. This is a challenging control task since the problem involves highly nonlinear dynamics including translational motion, rotational motion, and flexible structure deformation and vibration. In addition, uncertainties in the system modeling parameters and disturbances make the precise control more difficult. Four control design approaches are integrated to solve this challenging problem. The first approach is to consider the spacecraft rigid body translational and rotational dynamics together with the flexible motion in one unified optimal control framework so that the overall system performance and constraints can be addressed in one optimization process. The second approach is to formulate the robust control objectives into the optimal control cost function and prove the equivalency between the robust stabilization problem and the transformed optimal control problem. The third approach is to employ the è-D technique, a novel optimal control method that is based on a perturbation solution to the Hamilton-Jacobi-Bellman equation, to solve the nonlinear optimal control problem obtained from the indirect robust control formulation. The resultant optimal control law can be obtained in closedorm, and thus facilitates the onboard implementation. The integration of these three approaches is called the integrated indirect robust control scheme. The fourth approach is to use the inverse optimal adaptive control method combined with the indirect robust control scheme to alleviate the conservativeness of the indirect robust control scheme by using online parameter estimation such that adaptive, robust, and optimal properties can all be achieved. To show the effectiveness of the proposed control approaches, six degree-offreedom spacecraft proximity operation simulation is conducted and demonstrates satisfying performance under various uncertainties and disturbances

Review of advanced guidance and control algorithms for space/aerospace vehicles

The design of advanced guidance and control (G&C) systems for space/aerospace vehicles has received a large amount of attention worldwide during the last few decades and will continue to be a main focus of the aerospace industry. Not surprisingly, due to the existence of various model uncertainties and environmental disturbances, robust and stochastic control-based methods have played a key role in G&C system design, and numerous effective algorithms have been successfully constructed to guide and steer the motion of space/aerospace vehicles. Apart from these stability theory-oriented techniques, in recent years, we have witnessed a growing trend of designing optimisation theory-based and artificial intelligence (AI)-based controllers for space/aerospace vehicles to meet the growing demand for better system performance. Related studies have shown that these newly developed strategies can bring many benefits from an application point of view, and they may be considered to drive the onboard decision-making system. In this paper, we provide a systematic survey of state-of-the-art algorithms that are capable of generating reliable guidance and control commands for space/aerospace vehicles. The paper first provides a brief overview of space/aerospace vehicle guidance and control problems. Following that, a broad collection of academic works concerning stability theory-based G&C methods is discussed. Some potential issues and challenges inherent in these methods are reviewed and discussed. Then, an overview is given of various recently developed optimisation theory-based methods that have the ability to produce optimal guidance and control commands, including dynamic programming-based methods, model predictive control-based methods, and other enhanced versions. The key aspects of applying these approaches, such as their main advantages and inherent challenges, are also discussed. Subsequently, a particular focus is given to recent attempts to explore the possible uses of AI techniques in connection with the optimal control of the vehicle systems. The highlights of the discussion illustrate how space/aerospace vehicle control problems may benefit from these AI models. Finally, some practical implementation considerations, together with a number of future research topics, are summarised

Neural Network-Based Adaptive Control for Spacecraft Under Actuator Failures and Input Saturations

In this article, we develop attitude tracking control methods for spacecraft as rigid bodies against model uncertainties, external disturbances, subsystem faults/failures, and limited resources. A new intelligent control algorithm is proposed using approximations based on radial basis function neural networks (RBFNNs) and adopting the tunable parameter-based variable structure (TPVS) control techniques. By choosing different adaptation parameters elaborately, a series of control strategies are constructed to handle the challenging effects due to actuator faults/failures and input saturations. With the help of the Lyapunov theory, we show that our proposed methods guarantee both finite-time convergence and fault-tolerance capability of the closed-loop systems. Finally, benefits of the proposed control methods are illustrated through five numerical examples

Optimized state feedback regulation of 3DOF helicopter system via extremum seeking

In this paper, an optimized state feedback regulation of a 3 degree of freedom (DOF) helicopter is designed via extremum seeking (ES) technique. Multi-parameter ES is applied to optimize the tracking performance via tuning State Vector Feedback with Integration of the Control Error (SVFBICE). Discrete multivariable version of ES is developed to minimize a cost function that measures the performance of the controller. The cost function is a function of the error between the actual and desired axis positions. The controller parameters are updated online as the optimization takes place. This method significantly decreases the time in obtaining optimal controller parameters. Simulations were conducted for the online optimization under both fixed and varying operating conditions. The results demonstrate the usefulness of using ES for preserving the maximum attainable performance

Development of Robust Control Laws for Disturbance Rejection in Rotorcraft UAVs

Inherent stability inside the flight envelope must be guaranteed in order to safely introduce private and commercial UAV systems into the national airspace. The rejection of unknown external wind disturbances offers a challenging task due to the limited available information about the unpredictable and turbulent characteristics of the wind. This thesis focuses on the design, development and implementation of robust control algorithms for disturbance rejection in rotorcraft UAVs. The main focus is the rejection of external disturbances caused by wind influences. Four control algorithms are developed in an effort to mitigate wind effects: baseline nonlinear dynamic inversion (NLDI), a wind rejection extension for the NLDI, NLDI with adaptive artificial neural networks (ANN) augmentation, and NLDI with L1 adaptive control augmentation. A simulation environment is applied to evaluate the performance of these control algorithms under external wind conditions using a Monte Carlo analysis. Outdoor flight test results are presented for the implementation of the baseline NLDI, NLDI augmented with adaptive ANN and NLDI augmented with L1 adaptive control algorithms in a DJI F330 Flamewheel quadrotor UAV system. A set of metrics is applied to compare and evaluate the overall performance of the developed control algorithms under external wind disturbances. The obtained results show that the extended NLDI exhibits undesired characteristics while the augmentation of the baseline NLDI control law with adaptive ANN and L1 output-feedback adaptive control improve the robustness of the translational and rotational dynamics of a rotorcraft UAV in the presence of wind disturbances