This paper presents a multi-chamber hydraulic actuator controlled by digital pumps and on/off valves, in order to improve the efficiency of hydraulic systems applied in aircraft for flight control. Hydraulic positioning systems are used in many different applications, such as mobile machinery, industry and aerospace. In aircraft, the hydraulic actuators are used at flight control surfaces, cargo doors, steering, landing gear and so one. However, the mas-sive use of resistive control techniques, which throttles the passages of the hydraulic fluid, associated with internal leakage of the hydraulic components, make these systems low energy efficient. In order to improve their energy efficiency, digital hydraulics emerges as a promising solution mainly for mobile applications. In this paper a hydraulic positioning system for aircraft control surfaces using a multi-chamber actuator controlled by on/off valves and a digital pump is proposed. The use of a digital pump with three fixed displacement pumps can provide eight different volumetric displacement out-puts. The multi-chamber actuator with four areas can operate in two different modes, normal or regenerative, resulting in six different equivalent areas. The regenerative mode allows the actuator to achieve higher actuation velocity values with smaller pumps. These equivalent areas combined with the dif-ferent supplied flow rates can deliver 43 different discrete output velocity values for the actuator, in steady-state. For the system dynamic analyses, three mathematical simulation models were developed using MATLAB/Simulink and Hopsan, one for the digital system, and two for the conventional solutions applied in aircraft (Servo Hydraulic Actuators (SHA) and Electro Hydrostatic Actuator (EHA)). The simulation results demonstrate that the digital actuator can achieve, for position control, a maximum position error, in a steady-state, of 0.7 mm. From the energy consumption point of view, the digital circuit consumes 31 times less energy when compared with the SHA and 1.7 when compared to the EHA, resulting in an energy efficiency of 54%.

This paper presents a method to derive optimised energy management strate-gies for a hydraulic hybrid wheel loader. Energy efficiency is a key aspect for the sustainability of off-road mobile machines. Energy management strategies for on-road hybrid vehicles cannot be directly applied to off-road hybrid machines. One significant reason is that there are added degrees of freedom with respect to how power can be recovered, exchanged and reused in the different functions, such as drivetrain or work functions. This results in more complex energy management strategies being derived. This paper presents an analysis and preliminary conclusions for a proposed method to derive optimised online energy management strategies for a hydraulic hybrid wheel loader. Dynamic programming is used to obtain optimal offline energy management strategies for a series of drive cycles. The results are used as examples to train a neural network. The trained neural network then implements the energy management strategy and is used to make optimised control decisions. Through simulation, the neural networks ability to learn the dynamic programming decision-making process is shown, resulting in the machine operating with fuel consumption similar to that of the offline optimal energy management strategy. Aspects of simplicity to model these machines for dynamic programming optimisation, the data necessary to train the network, the training process, variables used to learn the dynamic pro-gramming decision-making process and the robustness of the network when facing unseen operational conditions are discussed. The paper demonstrates the simplicity of the method for taking into account variables that affect the control decisions, therefore achieving optimised solutions.

This paper outlines an extended analysis on how multi-chamber actuators can improve the efficiency of valve-controlled systems. Resistive control is a major source of energy losses in valve-controlled systems that share the same pump to drive multiple loads. By combining different chambers, the load on multi-chamber actuators can be transformed into different pressure and flow rate levels. This allows the adaptation of its load to the loads on other actuators. This can lead to a reduction of resistive control energy losses that occur between pump and actuators when driven simultaneously. As a case study to highlight how the system efficiency can be improved, a load sensing system with a conventional and a multi-chamber actuator is analysed. The equations that describe the system steady state behaviour are presented to evaluate the effect of the load transformations on the system efficiency. A disadvantage of such architecture is the fact that load transformations result in different actuator speeds. To reduce this effect, a compensation factor for the command signal to the proportional valve is presented. The highlight from this paper is the potential for efficiency improvement enabled by the adoption of multi-chamber actuators in a valve-controlled architecture. Further research is required for the selection of number of chambers and their areas since they directly affect the system efficiency.

High energy efficiency is a key requirement for modern construction machinery. This is because of stricter environmental targets, electrification, and reduction of operation costs. To meet this requirement, the powertrain architectures of the machines are becoming increasingly complex, for example through hybridisation of drivetrain and work functions, or with improved hydraulic systems. However, the more complex the architecture is, the harder the management of splitting power between different sources and consumers. The number of work functions, operating environments, and tasks these machines engage in, along with the added degrees of freedom with respect to how energy can be recovered, ex-changed, and reused, makes them unique. Therefore, the development of control strategies for energy management in such machines requires specific research and development with their architecture and application in focus. This doctoral thesis presents an analysis of two methods for the development of machine learning-based energy management strategies for construction machines. One is based on supervised learning and the other on reinforcement learning. The methods use optimisation to find optimised solutions for the control problem of the systems and machine learning for learning and implementing the control decisions. In both methods, models of the physical systems are used for the learning and training. The thesis highlights and confirms, with experimental results, the potential of such methods to derive control strategies for these machines. The studied methods can learn and implement improved control decisions in the real systems that result in the potential for increased efficiency. At the same time, their robustness is shown in the application to unseen scenarios during training, although that does not eliminate the need for further training in the real systems after deployment. The thesis also increases the comprehensiveness on energy management for construction machines. The thesis was completed in a double-degree format between the Federal University of Santa Catarina, Florianópolis, Brazil, and Linköping University, Linköping, Sweden.

Hög effektivitet är ett nyckelkrav för moderna byggmaskiner. Detta på grund av strängare miljömål, elektrifiering och sänkta driftskostnader. För att möta detta krav blir maskinernas arkitektur mer komplex, till exempel genom hybridisering av drivlina och arbetsfunktioner. Men ju mer komplex arkitekturen är desto svårare blir hanteringen av maktdelning mellan olika källor och konsumenter. Antalet arbetsfunktioner, drifts-miljö och uppgifter de engagerar tillsammans med de ökade frihetsgraderna med avseende på hur kraft kan återvinnas, bytas ut och återanvändas, gör dem unika. Därför kräver utvecklingen av styrstrategier specifik utveckling med deras arkitektur och tillämpning i fokus. Denna doktors-avhandling presenterar en analys av två metoder för utveckling av optimerade och intelligenta energihanteringsstrategier i realtid för delsystem av komplexa entreprenadmaskiner. De utvärderade metoderna använder optimering för att hitta optimala lösningar för systemens kontrollproblem, och använder maskininlärning som ett sätt att lära sig och implementera de optimerade besluten. I båda metoderna används modeller för lärandet och träningen. Avhandlingen belyser och bekräftar experimentellt potentialen hos sådana metoder för att härleda kontrollstrategier för dessa maskiner. De studerade metoderna kan lära sig och implementera optimerade styrbeslut i de verkliga systemen vilket leder till ökad effektivitet. Samtidigt visar det sig att de är robusta mot osynliga scenarier under träning, även om det inte eliminerar behovet av vidare-utbildning i de verkliga systemen efter utplacering. Examensarbetet ökar också heltäckningen om energihantering för entreprenadmaskiner. Avhandlingen har utvecklats i ett dubbelgradersformat med Federal University of Santa Catarina, Florianópolis, Brasilien och Linköpings Universitet, Linköping, Sverige.

Alta eficiência energética é um requisito para máquinas de construção modernas, sendo este uma consequência das metas ambientais, eletrificação e redução de custos. Para atender este requisito as arquiteturas dos trens de potência das máquinas têm se tornado mais complexas, por exemplo, através da hibridização do sistema de tração e funções de trabalho, ou  sistemas hidráulicos melhorados. Entretanto, quanto mais complexa a arquitetura, mais difícil se torna o gerenciamento da divisão de potência entre as fontes e consumidores. O número de funções de trabalho, ambientes de operação e tarefas que elas executam, juntamente com os graus de liberdade relacionados à como energia pode ser recuperada, trocada e reutilizada, às tornam únicas. Dessa maneira, o desenvolvimento de estratégias de controle requer pesquisa e desenvolvimento específicos com as suas arquiteturas e aplicações em foco. Esta tese de doutorado apresenta uma análise de dois métodos para o desenvolvimento de estratégias baseadas em aprendizado de máquina para o gerenciamento de energia em máquinas de construção. Um é baseado em aprendizado supervisionado e outro em aprendizado por reforço. Os métodos avaliados usam otimização para encontrar soluções otimizadas para o problema de controle dos sistemas, e usam aprendizado de máquina como meio para aprender e implementar as decisões de controle. Em ambos os métodos, modelos dos sistemas físicos são utilizados para o aprendizado e treinamento. A tese destaca e confirma através de resultados experimentais, o potencial destes métodos em obter estratégias de controle para estas máquinas. Os métodos estudados são capazes de aprender e implementar decisões de controle melhores nos sistemas reais resultando em potencial aumento de eficiência energética. Ao mesmo tempo, é mostrado a sua robustez na prática a cenários não vistos durante o treinamento, apesar de isso não eliminar a necessidade de continuar o treinamento depois de implementadas no sistema real. A tese também aumenta a compressão sobre gerenciamento de energia em máquinas de construção. A tese foi desenvolvida em formato de cotutela com a Universidade Federal de Santa Catarina, Florianópolis, Brasil, e a Universidade de Linköping, Linköping, Suécia.

This paper highlights the importance of considering required control rules from the real-world implementation inoffline optimal control optimisations used to generate online energy management strategies (EMS). The controlrules are constraints on the optimal control problem. If not considered, the control optimisation results do notrepresent the reality and the EMS will have poor performance. In this paper, a neural network predicts the optimalcontrol decisions whenever the rules are not taking place. It is a rule- and neural network-based energymanagement strategy. A limitation to the use of neural networks as part of the EMS is that they do not ensurestable behaviour outside the region they were trained for. In the real application – in this case, a hybrid wheelloader – they will be deployed alongside control rules to ensure safety and reasonable operation. Hence theimportance of implementing the rules in the optimal control problem. Results show that better performance of theEMS is achieved if the rules from the application are considered in the optimal control problem.

This paper outlines how multi-chamber actuatorscan improve the efficiency of valve-controlled systems.Resistive control is a major source of energy losses invalve-controlled systemsthat share the same pumpto drive multiple loads. In the proposed concept, by selectingdifferent chambers,the load on the multi-chamber actuator can be transformed into different pressure and flow rate levels, allowingthe adaptation of its load to the loads on otheractuators. Thiscan lead to a reduction of resistive control energy losses that occur between pump and actuatorswhen driven simultaneously.Suchsystemsareseen as an intermediate solution between resistive conventionalhydraulics and throttle-less digital hydraulics. As a casestudyto highlight the possible efficiency improvement, a concept of a load sensing system with a conventional and a multi-chamber actuatoris analysed. To determine itsefficiency,the equations that describe its static behaviour are presented. Evaluating them for a set ofload forces and speeds demonstrates how the load transformation occursand how it canimprove efficiency.

This paper presents a method to derive optimized energy management strategies for a hydraulic hybrid wheel loader. Energy efficiency is a key aspect for the sustainability of off-road mobile machines. Energy management strategies for on-road hybrid vehicles cannot be directly applied to off-road hybrid machines. One significant reason is that there are added degrees of freedom with respect to how power can be recovered, exchanged and reused in the different functions, such as drivetrain or work functions. This results in more complex energy management strategies being derived. This paper presents an analysis and preliminary conclusions for a proposed method to derive optimised online energy management strategies for a hydraulic hybrid wheel loader. Dynamic programming is used to obtain optimal offline energy management strategies for a series of drive cycles. The results are used as examples to train a neural network. The trained neural network then implements the energy management strategy and is used to make optimised control decisions. Through simulation, the neural network’s ability to learn the dynamic programming decision-making process is shown, resulting in the machine operating with fuel consumption similar to that of the offline optimal energy management strategy. Aspects of simplicity to model these machines for dynamic programming optimisation, the data necessary to train the network, the training process, variables used to learn the dynamic programming decision-making process and the robustness of the network when facing unseen operational conditions are discussed. The paper demonstrates the simplicity of the method for taking into account variables that affect the control decisions, therefore achieving optimised solutions.