Coordination control of robot manipulators using flat outputs

Published ArticleThis paper focuses on the synchronizing control of multiple interconnected flexible robotic manipulators using differential flatness theory. The flatness theory has the advantage of simplifying trajectory tracking tasks of complex mechanical systems. Using this theory, we propose a new synchronization scheme whereby a formation of flatness based systems can be stabilized using their respective flat outputs. Using the flat outputs, we eliminate the need for cross coupling laws and communication protocols associated with such formations. The problem of robot coordination is reduced to synchronizing the flat outputs between the respective robot manipulators. Furthermore, the selection of the flat output used for the synchronizing control is not restricted as any system variable can be used. The problem of unmeasured states used in the control is also solved by reconstructing the missing states using flatness based interpolation. The proposed control law is less computationally intensive when compared to earlier reported work as integration of the differential equations is not required. Simulations using a formation of single link flexible joint robots are used to validate the proposed synchronizing control

Coordination control of robot manipulators using flat outputs

Published ArticleThis paper focuses on the synchronizing control of multiple interconnected flexible robotic manipulators using differential flatness theory. The flatness theory has the advantage of simplifying trajectory tracking tasks of complex mechanical systems. Using this theory, we propose a new synchronization scheme whereby a formation of flatness based systems can be stabilized using their respective flat outputs. Using the flat outputs, we eliminate the need for cross coupling laws and communication protocols associated with such formations. The problem of robot coordination is reduced to synchronizing the flat outputs between the respective robot manipulators. Furthermore, the selection of the flat output used for the synchronizing control is not restricted as any system variable can be used. The problem of unmeasured states used in the control is also solved by reconstructing the missing states using flatness based interpolation. The proposed control law is less computationally intensive when compared to earlier reported work as integration of the differential equations is not required. Simulations using a formation of single link flexible joint robots are used to validate the proposed synchronizing control

Synchronous behavior in networks of coupled systems : with applications to neuronal dynamics

Synchronization in networks of interacting dynamical systems is an interesting phenomenon that arises in nature, science and engineering. Examples include the simultaneous flashing of thousands of fireflies, the synchronous firing of action potentials by groups of neurons, cooperative behavior of robots and synchronization of chaotic systems with applications to secure communication. How is it possible that systems in a network synchronize? A key ingredient is that the systems in the network "communicate" information about their state to the systems they are connected to. This exchange of information ultimately results in synchronization of the systems in the network. The question is how the systems in the network should be connected and respond to the received information to achieve synchronization. In other words, which network structures and what kind of coupling functions lead to synchronization of the systems? In addition, since the exchange of information is likely to take some time, can systems in networks show synchronous behavior in presence of time-delays? The first part of this thesis focusses on synchronization of identical systems that interact via diffusive coupling, that is a coupling defined through the weighted difference of the output signals of the systems. The coupling might contain timedelays. In particular, two types of diffusive time-delay coupling are considered: coupling type I is diffusive coupling in which only the transmitted signals contain a time-delay, and coupling type II is diffusive coupling in which every signal is timedelayed. It is proven that networks of diffusive time-delay coupled systems that satisfy a strict semipassivity property have solutions that are ultimately bounded. This means that the solutions of the interconnected systems always enter some compact set in finite time and remain in that set ever after. Moreover, it is proven that nonlinear minimum-phase strictly semipassive systems that interact via diffusive coupling always synchronize provided the interaction is sufficiently strong. If the coupling functions contain time-delays, then these systems synchronize if, in addition to the sufficiently strong interaction, the product of the time-delay and the coupling strength is sufficiently small. Next, the specific role of the topology of the network in relation to synchronization is discussed. First, using symmetries in the network, linear invariant manifolds for networks of the diffusively time-delayed coupled systems are identified. If such a linear invariant manifold is also attracting, then the network possibly shows partial synchronization. Partial synchronization is the phenomenon that some, at least two, systems in the network synchronize with each other but not with every system in the network. It is proven that a linear invariant manifold defined by a symmetry in a network of strictly semipassive systems is attracting if the coupling strength is sufficiently large and the product of the coupling strength and the time-delay is sufficiently small. The network shows partial synchronization if the values of the coupling strength and time-delay for which this manifold is attracting differ from those for which all systems in the network synchronize. Next, for systems that interact via symmetric coupling type II, it is shown that the values of the coupling strength and time-delay for which any network synchronizes can be determined from the structure of that network and the values of the coupling strength and time-delay for which two systems synchronize. In the second part of the thesis the theory presented in the first part is used to explain synchronization in networks of neurons that interact via electrical synapses. In particular, it is proven that four important models for neuronal activity, namely the Hodgkin-Huxley model, the Morris-Lecar model, the Hindmarsh-Rose model and the FitzHugh-Nagumo model, all have the semipassivity property. Since electrical synapses can be modeled by diffusive coupling, and all these neuronal models are nonlinear minimum-phase, synchronization in networks of these neurons happens if the interaction is sufficiently strong and the product of the time-delay and the coupling strength is sufficiently small. Numerical simulations with various networks of Hindmarsh-Rose neurons support this result. In addition to the results of numerical simulations, synchronization and partial synchronization is witnessed in an experimental setup with type II coupled electronic realizations of Hindmarsh-Rose neurons. These experimental results can be fully explained by the theoretical findings that are presented in the first part of the thesis. The thesis continues with a study of a network of pancreatic -cells. There is evidence that these beta-cells are diffusively coupled and that the synchronous bursting activity of the network is related to the secretion of insulin. However, if the network consists of active (oscillatory) beta-cells and inactive (dead) beta-cells, it might happen that, due to the interaction between the active and inactive cells, the activity of the network dies out which results in a inhibition of the insulin secretion. This problem is related to Diabetes Mellitus type 1. Whether the activity dies out or not depends on the number of cells that are active relative to the number of inactive cells. A bifurcation analysis gives estimates of the number of active cells relative to the number of inactive cells for which the network remains active. At last the controlled synchronization problem for all-to-all coupled strictly semipassive systems is considered. In particular, a systematic design procedure is presented which gives (nonlinear) coupling functions that guarantee synchronization of the systems. The coupling functions have the form of a definite integral of a scalar weight function on a interval defined by the outputs of the systems. The advantage of these coupling functions over linear diffusive coupling is that they provide high gain only when necessary, i.e. at those parts of the state space of the network where nonlinearities need to be suppressed. Numerical simulations in networks of Hindmarsh-Rose neurons support the theoretical results

Modelling, real-time simulation and control of automotive windscreen wiper systems for electronic control unit development

In recent years there has been a growth in the automotive industry, coupled with a growth in the amount of electronic components and systems in a modern vehicle. The higher amount of electronics has led to an increased amount of Electronic Control Units (ECU) in a vehicle which require advanced simulation based testing procedures throughout their development process. One such method is Hardware in the Loop (HIL) simulation in which a real ECU is connected to simulation models of its environment via a real-time simulator. This project is concerned with developing a plant model of a windscreen wiper system for use in the development of Jaguar Land Rover’s (JLR) body electronics ECU. The system is divided into four parts which are modelled separately: Wiper motor, linkages, arm and blades, and the windscreen environment. The wiper motor and mechanical elements models are derived and implemented using the physical modelling tools SimScape and SimMechanics. A dynamic friction model describing the interaction between the wiper blades and the windscreen is developed, based on results presented in the literature. A simple aerodynamic model describing the forces on the wiper blades is also established. The parameters of the models are derived using three sequential optimisation methods: Transfer function parameter identification, Genetic Algorithms (GA) and a nonlinear least squares local optimiser. A transfer function relating the motor current to the voltage was derived for step one, and a bespoke GA has been developed for step two. The parameters were successfully identified. Following this, Artificial Neural Networks (ANN) were used to convert the physical models into real-time capable models suitable for HIL simulation. Finally, adaptive control systems are designed in order to maintain the motor at a constant velocity. The models are presented in a Simulink library and graphical user interface modelling tool for ease of use

Managing electric vehicles with renewable generation through energy storage and smart grid principles

Electric vehicles (EVs) are the most comprehensive method of sustainable transportation because they are environmentally friendly, quiet and low maintenance. However, they suffer from low usability because of the limited distance that can be covered on a single charge, which limits the freedom of transportation. Further, the charging process to restore the initial driving range is relatively long compared with conventional solutions. The only proposed way to improve the distance on a charge is to install a large energy storage system (ESS), which takes up more space, thereby limiting the usability of space by passengers and increasing the weight of the EV. The increased weight and size of the EV also negatively affect the distance range. In addition, the larger size of the battery, which is the main component of the ESS, requires a longer charging time. The current solution for fast charging requires more time than traditional refuelling techniques. This study aims to design, develop and analyse a novel approach for improving the energy consumption of EVs using optimisation techniques. In the first phase of the study, detailed analysis is conducted of the existing systems of EVs to determine which areas can be improved. The outcome of this investigation is used to determine presented loading profile of the various loads in EVs and determine the way to characterise them. These results are applied to design the new architecture for the loads to improve the connectivity of the various components of EVs and introduce interaction between loads. The developed architecture has centralised topology with separated control bus for the safety systems to satisfy the ISO 26262 safety standard. The newly developed system considers various loading requests at the same time to supply the load. The control algorithm schedules the power supply to the selected loads or, in some cases, clips the load request to decrease the momentary energy consumption. To achieve better optimisation, the thermal energy generation is analysed because it has a significant effect on the electric energy consumption in the heating elements. The second part of the developed approach is deep integration of loads with the overall energy flow in EVs. As a result, the recuperated energy in the propulsion system can be transferred to the components of the ESS and to supply the auxiliary loads on demand. The xviii generation units are combined with a photovoltaic system to improve the generation capability of the architecture. One of the key aims of this research is the simulation and experimental study of the developed architecture to identify weak spots in the solution and compare its performance with existing solutions under various solutions that go beyond traditional driving cycles