Benchmarking Cerebellar Control

Cerebellar models have long been advocated as viable models for robot dynamics control. Building on an increasing insight in and knowledge of the biological cerebellum, many models have been greatly refined, of which some computational models have emerged with useful properties with respect to robot dynamics control. Looking at the application side, however, there is a totally different picture. Not only is there not one robot on the market which uses anything remotely connected with cerebellar control, but even in research labs most testbeds for cerebellar models are restricted to toy problems. Such applications hardly ever exceed the complexity of a 2 DoF simulated robot arm; a task which is hardly representative for the field of robotics, or relates to realistic applications. In order to bring the amalgamation of the two fields forwards, we advocate the use of a set of robotics benchmarks, on which existing and new computational cerebellar models can be comparatively tested. It is clear that the traditional approach to solve robotics dynamics loses ground with the advancing complexity of robotic structures; there is a desire for adaptive methods which can compete as traditional control methods do for traditional robots. In this paper we try to lay down the successes and problems in the fields of cerebellar modelling as well as robot dynamics control. By analyzing the common ground, a set of benchmarks is suggested which may serve as typical robot applications for cerebellar models

Predictive control for packet dropouts in wireless networked control systems

A predictive PID controller is presented to achieve stability in wireless networked control systems, where the communication is subject to data packet dropouts in both communication routes: sensor to control and control to actuator transmission. The control strategy is based on General Predictive Control (GPC). A Kalman filter and a consecutive dropouts compensator algorithm have been added to the control scheme. The purpose of the algorithm is to develop an estimation and control system that maintains information of the sensor packets and the control actions. Several experiments using the TrueTime network simulator are provided to demonstrate the algorithm and its effectiveness

Концептуальные основы адаптивных авторулевых

Проблематика. Роботу присвячено критичному аналізу літератури, що охоплює головні аспекти створення адаптивних систем керування рухом судна. Мета дослідження. Метою роботи є визначення перспективних напрямів досліджень у галузі створення адаптивних систем керування рухом судна. Методика реалізації. Проведено аналіз існуючих підходів до ідентифікації параметрів моделі судна (зокрема, ідентифікації на зиґзаґу, на циркуляції та за допомогою калманівської фільтрації), визначено переваги і недоліки цих методів, що можуть бути покладені в основу створення адаптивних автостернових. Наведено критичний аналіз підходів до керування судном за допомогою класичних та новітніх методів автоматичного керування об’єктами, зокрема параметричного настроювання класичних ПІД-регуляторів, перемикання регуляторів, застосування нелінійних регуляторів – лінійно-квадратичних (LQ), “ковзного режиму” (sliding mode), а також штучного інтелекту – нейромереж, нечіткої логіки та гібридних підходів. Окремо в огляді наведено аналіз розробок вітчизняних авторів, присвячених розробці адаптивних автостернових та адаптивному керуванню рухом судна. Результати дослідження. В результаті аналізу літературних джерел визначено перспективні напрями досліджень у галузі створення адаптивних систем керування рухом судна. Висновки. Перспективними напрямами досліджень є: 1) розробка нових підходів до ідентифікації параметрів моделі руху судна та збурень, що діють на нього; 2) застосування методів штучного інтелекту, зокрема нечіткої логіки та нейромереж, до адаптивного керування судном; 3) побудова адаптивних нелінійних систем керування рухом судна.Background. The paper is devoted to critical analysis of literature that covers the major aspects of adaptive ship motion control systems. Objective. The objective of a study is identifying the promising areas of research in the field of adaptive ship motion control. Methods. The analysis of existing approaches to ship model parameters identification (including identification during zig-zag motion, during circulation and identification using Kalman filtering) is done; advantages and disadvantages of those methods are determined. The methods mentioned can be used as a basis for creating adaptive gyropilots. A critical review of approaches to ship control by means of classical and modern methods of automatic control, including the parametric adjustment of classic PID regulators, switching of regulators, use of nonlinear regulators — linear-quadratic (LQ), sliding mode regulators, and artificial intelligence — neural networks, fuzzy logic and hybrid approaches, is done. Separately, in the survey analysis of papers of Ukrainian authors, which are devoted to the development of adaptive gyropilots and adaptive ship motion control, is presented. Results. As a result of literature survey, prospective areas of studies in the field of adaptive ship control are determined. Conclusions. Most promising research areas are: 1) development of novel approaches to the identification of the vessel model parameters and disturbances acting on it; 2) application of artificial intelligence, including fuzzy logic and neural networks, to adaptive ship control methods; 3) development of adaptive nonlinear systems for ship motion control.Проблематика. Работа посвящена критическому анализу литературы, охватывающей основные аспекты создания адаптивных систем управления движением судна. Цель исследования. Цель работы – определение перспективных направлений исследований в области создания адаптивных систем управления движением судна. Методика реализации. Проведен анализ существующих подходов к идентификации параметров модели судна (в частности, идентификации на зигзаге, на циркуляции и с помощью калмановской фильтрации), определены преимущества и недостатки этих методов, которые могут быть положены в основу создания адаптивных авторулевых. Приведен критический анализ подходов к управлению судном с помощью классических и новых методов автоматического управления объектами, в частности параметрической настройки классических ПИД-регуляторов, переключения регуляторов, применения нелинейных регуляторов – линейно-квадратичных (LQ), “скользящего режима” (sliding mode), а также искусственного интеллекта – нейросетей, нечеткой логики и гибридных подходов. Отдельно в обзоре приведен анализ разработок отечественных авторов, посвященных разработке адаптивных авторулевых и адаптивному управлению движением судна. Результаты исследования. В результате анализа литературных источников определены перспективные направления исследований в области создания адаптивных систем управления движением судна. Выводы. Перспективными направлениями исследований являются: 1) разработка новых подходов к идентификации параметров модели движения судна и действующих на него возмущений; 2) применение методов искусственного интеллекта, в частности нечеткой логики и нейронных сетей, к адаптивному управлению судном; 3) построение адаптивных нелинейных систем управления движением судна

A path planning control for a vessel dynamic positioning system based on robust adaptive fuzzy strategy

The thrusters and propulsion propellers systems, as well as the operating situations, are all well-known nonlinearities which are caused less accuracy of the dynamic positioning system (DPS) of vessels in the path planning control process. In this study, to enhance the robust performance of the DPS, we proposed a robust adaptive fuzzy control model to reduce the effect of uncertainty problems and disturbances on the DPS. Firstly, the adaptive fuzzy controller with adaptive law is designed to adjust the membership function of the fuzzy controller to minimize the error in path planning control of the vessel. Secondly, the H∞ performance of robust tracking is proved by the Lyapunov theory. Moreover, compared to the other controller, a simulation experiment comprising two case studies confirmed the efficiency of the approach. Finally, the results showed that the proposed controller reaches control quality, performance and stability

Multistage Rule-Based Positioning Optimization for High-Precision LPAT

This paper proposes a multistage rule-based precision positioning control method for the linear piezoelectrically actuated table (LPAT). During the coarse-tuning stage, the LPAT is actuated by coarse voltage schemes toward the target of 20 μm at a higher velocity, and during the fine-tuning stage, it is steadily and accurately driven by the fine voltage scheme to reach the target position. The rule-based method is employed to establish the control rules for the voltages and displacements of the two stages using statistical methods. The experimental results demonstrate that the proposed control method can quickly reach steady state, and the steady-state error can be reduced to less than or equal to 0.02 μm for small travel (±0.1 μm) and large travel (±20 mm)

Biped dynamic walking using reinforcement learning

This thesis presents a study of biped dynamic walking using reinforcement learning. A hardware biped robot was built. It uses low gear ratio DC motors in order to provide free leg movements. The Self Scaling Reinforcement learning algorithm was developed in order to deal with the problem of reinforcement learning in continuous action domains. A new learning architecture was designed to solve complex control problems. It uses different modules that consist of simple controllers and small neural networks. The architecture allows for easy incorporation of modules that represent new knowledge, or new requirements for the desired task. Control experiments were carried out using a simulator and the physical biped. The biped learned dynamic walking on flat surfaces without any previous knowledge about its dynamic model

Current Trends in Tactical Missile Guidance

The problem of tactical missile guidance is very challenging and has been treated using several basic metlfodologies in the past four decades. Major techniques can be grouped underclassical guidance laws, modern guidance laws, l'aws for manoeuvring targets, predictive guidance for endgame scenario, and guidance laws based on intelligent control methods. Each technique has some advantages and disadvantages while implementing in a practical system. Guidance law selection is dictated by nature of flight profile like boost, midcourse, terminal homing, etc, and also miss-distance and a single-shot kill probability. This paper presents a brief survey of the existing techniques and current trends in tactical missile guidance

Adaptive and Optimal Motion Control of Multi-UAV Systems

This thesis studies trajectory tracking and coordination control problems for single and multi unmanned aerial vehicle (UAV) systems. These control problems are addressed for both quadrotor and fixed-wing UAV cases. Despite the fact that the literature has some approaches for both problems, most of the previous studies have implementation challenges on real-time systems. In this thesis, we use a hierarchical modular approach where the high-level coordination and formation control tasks are separated from low-level individual UAV motion control tasks. This separation helps efficient and systematic optimal control synthesis robust to effects of nonlinearities, uncertainties and external disturbances at both levels, independently. The modular two-level control structure is convenient in extending single-UAV motion control design to coordination control of multi-UAV systems. Therefore, we examine single quadrotor UAV trajectory tracking problems to develop advanced controllers compensating effects of nonlinearities and uncertainties, and improving robustness and optimality for tracking performance. At fi rst, a novel adaptive linear quadratic tracking (ALQT) scheme is developed for stabilization and optimal attitude control of the quadrotor UAV system. In the implementation, the proposed scheme is integrated with Kalman based reliable attitude estimators, which compensate measurement noises. Next, in order to guarantee prescribed transient and steady-state tracking performances, we have designed a novel backstepping based adaptive controller that is robust to effects of underactuated dynamics, nonlinearities and model uncertainties, e.g., inertial and rotational drag uncertainties. The tracking performance is guaranteed to utilize a prescribed performance bound (PPB) based error transformation. In the coordination control of multi-UAV systems, following the two-level control structure, at high-level, we design a distributed hierarchical (leader-follower) 3D formation control scheme. Then, the low-level control design is based on the optimal and adaptive control designs performed for each quadrotor UAV separately. As particular approaches, we design an adaptive mixing controller (AMC) to improve robustness to varying parametric uncertainties and an adaptive linear quadratic controller (ALQC). Lastly, for planar motion, especially for constant altitude flight of fixed-wing UAVs, in 2D, a distributed hierarchical (leader-follower) formation control scheme at the high-level and a linear quadratic tracking (LQT) scheme at the low-level are developed for tracking and formation control problems of the fixed-wing UAV systems to examine the non-holonomic motion case. The proposed control methods are tested via simulations and experiments on a multi-quadrotor UAV system testbed

Quadcopter stabilization with neural network

UAVs (Unmanned Aerial Vehicle), also known as drones, are becoming attractive in the consumer space due to their relatively low cost and their ability to operate autonomously with minimal human intervention. A user could program the drone with GPS coordinates, and the drone would comply with utmost precision. In order for the drone to operate a preprogrammed flight path, it requires a host of sensors for it to gather data and operate on that data in real time. For instance, a consumer drone typically has obstacle avoidance sensors, a GPS sensor for routing and navigation, and an IMU (Inertial Measurement Unit) for tracking position and orientation. These sensors play a crucial role in both stabilization and navigation of the drone. This report aims to investigate, analyze and understand the complexity involved in designing and implementing an autonomous quadcopter; specifically, the stabilization algorithms. In general, stabilization is achieved using some form of control algorithm. The report covers a popular approach for stabilization (PID Control) found with many open source libraries and contrasts it with an alternative machine learning approach (Neural Networks). Finally, a machine learning based algorithm is implemented and evaluated on a prototype quadcopter, and its results are presented.Electrical and Computer Engineerin