Intelligent model-based control of complex three-link mechanisms

The aim of this study is to understand the complexity and control challenges of the locomotion of a three-link mechanism of a robot system. In order to do this a three-link robot gymnast (Robogymnast) has been built in Cardiff University. The Robogymnast is composed of three links (one arm, one torso, one leg) and is powered by two geared DC motors. Currently the robot has three potentiometers to measure the relative angles between adjacent links and only one tachometer to measure the relative angular position of the first link. A mathematical model for the robot is derived using Lagrange equations. Since the model is inherently nonlinear and multivariate, it presents more challenges when modelling the Robogymnast and dealing with control motion problems. The proposed approach for dealing with the design of the control system is based on a discrete-time linear model around the upright position of the Robogymnast. To study the swinging motion of the Robogymnast, a new technique is proposed to manipulate the frequency and the amplitude of the sinusoidal signals as a means of controlling the motors. Due to the many combinations of the frequency and amplitude, an optimisation method is required to find the optimal set. The Bees Algorithm (BA), a novel swarm-based optimisation technique, is used to enhance the performance of the swinging motion through optimisation of the manipulated parameters of the control actions. The time taken to reach the upright position at its best is 128 seconds. Two different control methods are adopted to study the balancing/stablising of the Robogymnast in both the downward and upright configurations. The first is the optimal control algorithm using the Linear Quadratic Regulator (LQR) technique with integrators to help achieve and maintain the set of reference trajectories. The second is a combination of Local Control (LC) and LQR. Each controller is implemented via reduced order state observer to estimate the unmeasured states in terms of their relative angular velocities. From the identified data in the relative angular positions of the upright balancing control, it is reported that the maximum amplitude of the deviation in the relative angles on average are approximately 7.5° for the first link and 18° for the second link. It is noted that the third link deviated approximately by 2.5° using only the LQR controller, and no significant deviation when using the LQR with LC. To explore the combination between swinging and balancing motions, a switching mechanism between swinging and balancing algorithm is proposed. This is achieved by dividing the controller into three stages. The first stage is the swinging control, the next stage is the transition control which is accomplished using the Independent Joint Control (IJC) technique and finally balancing control is achieved by the LQR. The duration time of the transition controller to track the reference trajectory of the Robogymnast at its best is found to be within 0.4 seconds. An external disturbance is applied to each link of the Robogymnast separately in order to study the controller's ability to overcome the disturbance and to study the controller response. The simulation of the Robogymnast and experimental realization of the controllers are implemented using MATLAB® software and the C++ program environment respectively

Data-Driven Control with Learned Dynamics

This research focuses on studying data-driven control with dynamics that are actively learned from machine learning algorithms. With system dynamics being identified using neural networks either explicitly or implicitly, we can apply control following either a model-based approach or a model-free approach. In this thesis, the two different methods are explained in detail and finally compared to shed light on the emerging data-driven control research field. In the first part of the thesis, we first introduce state-of-art Reinforcement Learning (RL) algorithm representing data-driven control using a model-free learning approach. We discuss the advantages and shortcomings of the current RL algorithms and motivate our study to search for a model-based control which is physics-based and also provides better model interpretability. We then propose a novel data-driven, model-based approach for the optimal control of the dynamical system. The proposed approach relies on the Deep Neural Network (DNN) based learning of Koopman operator and therefore is named as Deep Learning of Koopman Representation for Control (DKRC). In particular, DNN is employed for the data-driven identification of basis function used in the linear lifting of nonlinear control system dynamics. One a linear representation of system dynamics is learned, we can implement classic control algorithms such as iterative Linear Quadratic Regulator (iLQR) and Model Predictive Control (MPC) for optimal control design. The controller synthesis is purely data-driven and does not rely on prior domain knowledge. The OpenAI Gym environment is used for simulations of various control problems. The method is applied to three classic dynamical systems on OpenAI Gym environment to demonstrate the capability. In the second part, we compare the proposed method with a state-of-art model-free control method based on an actor-critic architecture – Deep Deterministic Policy Gradient (DDPG), which has been proved to be effective in various dynamical systems. Two examples are provided for comparison, i.e., classic Inverted Pendulum and Lunar Lander Continuous Control. We compare these two methods in terms of control strategies and the effectiveness under various initialization conditions from the results of the experiments. We also examine the learned dynamic model from DKRC with the analytical model derived from the Euler-Lagrange Linearization method, demonstrating the accuracy in the learned model for unknown dynamics from a data-driven sample-efficient approach

Advanced Strategies for Robot Manipulators

Amongst the robotic systems, robot manipulators have proven themselves to be of increasing importance and are widely adopted to substitute for human in repetitive and/or hazardous tasks. Modern manipulators are designed complicatedly and need to do more precise, crucial and critical tasks. So, the simple traditional control methods cannot be efficient, and advanced control strategies with considering special constraints are needed to establish. In spite of the fact that groundbreaking researches have been carried out in this realm until now, there are still many novel aspects which have to be explored

Bio-Inspired Robotics

Modern robotic technologies have enabled robots to operate in a variety of unstructured and dynamically-changing environments, in addition to traditional structured environments. Robots have, thus, become an important element in our everyday lives. One key approach to develop such intelligent and autonomous robots is to draw inspiration from biological systems. Biological structure, mechanisms, and underlying principles have the potential to provide new ideas to support the improvement of conventional robotic designs and control. Such biological principles usually originate from animal or even plant models, for robots, which can sense, think, walk, swim, crawl, jump or even fly. Thus, it is believed that these bio-inspired methods are becoming increasingly important in the face of complex applications. Bio-inspired robotics is leading to the study of innovative structures and computing with sensory–motor coordination and learning to achieve intelligence, flexibility, stability, and adaptation for emergent robotic applications, such as manipulation, learning, and control. This Special Issue invites original papers of innovative ideas and concepts, new discoveries and improvements, and novel applications and business models relevant to the selected topics of ``Bio-Inspired Robotics''. Bio-Inspired Robotics is a broad topic and an ongoing expanding field. This Special Issue collates 30 papers that address some of the important challenges and opportunities in this broad and expanding field

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

Lab experiences for teaching undergraduate dynamics

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2003.Includes bibliographical references (p. 443-466).This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.This thesis describes several projects developed to teach undergraduate dynamics and controls. The materials were developed primarily for the class 2.003 Modeling Dynamics and Control I. These include (1) a set of ActivLab modular experiments that illustrate the dynamics of linear time-invariant (LTI) systems and (2) a two wheeled mobile inverted pendulum. The ActivLab equipment has been designed as shareware, and plans for it are available on the web. The inverted pendulum robot developed here is largely inspired by the iBOT and Segway transportation devices invented by Dean Kamen.by Katherine A. Lilienkamp.S.M

Exploiting inherent robustness and natural dynamics in the control of bipedal walking robots

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2000.Includes bibliographical references (p. 115-120).Walking is an easy task for most humans and animals. Two characteristics which make it easy are the inherent robustness (tolerance to variation) of the walking problem and the natural dynamics of the walking mechanism. In this thesis we show how understanding and exploiting these two characteristics can aid in the control of bipedal robots. Inherent robustness allows for the use of simple, low impedance controllers. Natural dynamics reduces the requirements of the controller. We present a series of simple physical models of bipedal walking. The insight gained from these models is used in the development of three planar (motion only in the sagittal plane) control algorithms. The first uses simple strategies to control the robot to walk. The second exploits the natural dynamics of a kneecap, compliant ankle, and passive swing-leg. The third achieves fast swing of the swing-leg in order to enable the robot to walk quickly (1.25m). These algorithms are implemented on Spring Flamingo, a planar bipedal walking robot, which was designed and built for this thesis. Using these algorithms, the robot can stand and balance, start and stop walking, walk at a range of speeds, and traverse slopes and rolling terrain. Three-dimensional walking on flat ground is implemented and tested in simulation. The dynamics of the sagittal plane are sufficiently decoupled from the dynamics of the frontal and transverse planes such that control.-of each can be treated separately. We achieve three-dimensional walking by adding lateral balance to the planar algorithms. Tests of this approach on a real three-dimensional robot will lead to a more complete understanding of the control of bipedal walking in robots and humans.by Jerry E. Pratt.Ph.D

Bio-inspired robotic control in underactuation: principles for energy efficacy, dynamic compliance interactions and adaptability.

Biological systems achieve energy efficient and adaptive behaviours through extensive autologous and exogenous compliant interactions. Active dynamic compliances are created and enhanced from musculoskeletal system (joint-space) to external environment (task-space) amongst the underactuated motions. Underactuated systems with viscoelastic property are similar to these biological systems, in that their self-organisation and overall tasks must be achieved by coordinating the subsystems and dynamically interacting with the environment. One important question to raise is: How can we design control systems to achieve efficient locomotion, while adapt to dynamic conditions as the living systems do? In this thesis, a trajectory planning algorithm is developed for underactuated microrobotic systems with bio-inspired self-propulsion and viscoelastic property to achieve synchronized motion in an energy efficient, adaptive and analysable manner. The geometry of the state space of the systems is explicitly utilized, such that a synchronization of the generalized coordinates is achieved in terms of geometric relations along the desired motion trajectory. As a result, the internal dynamics complexity is sufficiently reduced, the dynamic couplings are explicitly characterised, and then the underactuated dynamics are projected onto a hyper-manifold. Following such a reduction and characterization, we arrive at mappings of system compliance and integrable second-order dynamics with the passive degrees of freedom. As such, the issue of trajectory planning is converted into convenient nonlinear geometric analysis and optimal trajectory parameterization. Solutions of the reduced dynamics and the geometric relations can be obtained through an optimal motion trajectory generator. Theoretical background of the proposed approach is presented with rigorous analysis and developed in detail for a particular example. Experimental studies are conducted to verify the effectiveness of the proposed method. Towards compliance interactions with the environment, accurate modelling or prediction of nonlinear friction forces is a nontrivial whilst challenging task. Frictional instabilities are typically required to be eliminated or compensated through efficiently designed controllers. In this work, a prediction and analysis framework is designed for the self-propelled vibro-driven system, whose locomotion greatly relies on the dynamic interactions with the nonlinear frictions. This thesis proposes a combined physics-based and analytical-based approach, in a manner that non-reversible characteristic for static friction, presliding as well as pure sliding regimes are revealed, and the frictional limit boundaries are identified. Nonlinear dynamic analysis and simulation results demonstrate good captions of experimentally observed frictional characteristics, quenching of friction-induced vibrations and satisfaction of energy requirements. The thesis also performs elaborative studies on trajectory tracking. Control schemes are designed and extended for a class of underactuated systems with concrete considerations on uncertainties and disturbances. They include a collocated partial feedback control scheme, and an adaptive variable structure control scheme with an elaborately designed auxiliary control variable. Generically, adaptive control schemes using neural networks are designed to ensure trajectory tracking. Theoretical background of these methods is presented with rigorous analysis and developed in detail for particular examples. The schemes promote the utilization of linear filters in the control input to improve the system robustness. Asymptotic stability and convergence of time-varying reference trajectories for the system dynamics are shown by means of Lyapunov synthesis

Towards Agility: Definition, Benchmark and Design Considerations for Small, Quadrupedal Robots

Agile quadrupedal locomotion in animals and robots is yet to be fully understood, quantified or achieved. An intuitive notion of agility exists, but neither a concise definition nor a common benchmark can be found. Further, it is unclear, what minimal level of mechatronic complexity is needed for this particular aspect of locomotion. In this thesis we address and partially answer two primary questions: (Q1) What is agile legged locomotion (agility) and how can wemeasure it? (Q2) How can wemake agile legged locomotion with a robot a reality? To answer our first question, we define agility for robot and animal alike, building a common ground for this particular component of locomotion and introduce quantitative measures to enhance robot evaluation and comparison. The definition is based on and inspired by features of agility observed in nature, sports, and suggested in robotics related publications. Using the results of this observational and literature review, we build a novel and extendable benchmark of thirteen different tasks that implement our vision of quantitatively classifying agility. All scores are calculated from simple measures, such as time, distance, angles and characteristic geometric values for robot scaling. We normalize all unit-less scores to reach comparability between different systems. An initial implementation with available robots and real agility-dogs as baseline finalize our effort of answering the first question. Bio-inspired designs introducing and benefiting from morphological aspects present in nature allowed the generation of fast, robust and energy efficient locomotion. We use engineering tools and interdisciplinary knowledge transferred from biology to build low-cost robots able to achieve a certain level of agility and as a result of this addressing our second question. This iterative process led to a series of robots from Lynx over Cheetah-Cub-S, Cheetah-Cub-AL, and Oncilla to Serval, a compliant robot with actuated spine, high range of motion in all joints. Serval presents a high level of mobility at medium speeds. With many successfully implemented skills, using a basic kinematics-duplication from dogs (copying the foot-trajectories of real animals and replaying themotion on the robot using a mathematical interpretation), we found strengths to emphasize, weaknesses to correct and made Serval ready for future attempts to achieve even more agile locomotion. We calculated Servalâs agility scores with the result of it performing better than any of its predecessors. Our small, safe and low-cost robot is able to execute up to 6 agility tasks out of 13 with the potential to reachmore after extended development. Concluding, we like to mention that Serval is able to cope with step-downs, smooth, bumpy terrain and falling orthogonally to the ground