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Injection molding is a critical component of modern industrial operations, and achieving fast and stable control of injection molding machines (IMMs) is essential for producing high-quality plastic products. This paper focuses on solving an optimal tracking control problem of the injection velocity that arises in a typical nonlinear IMM. To this end, an efficient optimal robust controller is proposed and designed. The nonlinear injection velocity servo system is first approximately linearized at iteration points using the first-order Taylor expansion approach. Then, at each time node in the optimization process, the relevant algebraic Riccati equation is introduced, and the solution is used to construct an optimal robust feedback controller. Furthermore, a rigorous Lyapunov theorem analysis is employed to demonstrate the global stability properties of the proposed feedback controller. The results from numerical simulations show that the proposed optimal robust control strategy can successfully and rapidly achieve the best tracking of the intended injection velocity trajectory within a given time.

Robots operating in human environments require a diverse set of skills, including slow and fast walking, turning, side-stepping, and more. However, developing robot controllers capable of exhibiting such a broad range of behaviors is a challenging problem that necessitates meticulous investigation for each task. To address this challenge, we introduce a trajectory optimization method that resolves the kinematic infeasibility of reference animal motions. This method, combined with a model-based controller, results in a unified data-driven model-based control framework capable of imitating various animal gaits without the need for expensive simulation training or real-world fine-tuning. Our framework is capable of imitating a variety of motor skills such as trotting, pacing, turning, and side-stepping with ease. It shows superior tracking capabilities in both simulations and the real world compared to other imitation controllers, including a model-based one and a learning-based motion imitation technique.

The fed-batch cultivation is in many ways a benchmark for fermentation processes, and it has been an attractive choice for the biotechnological production of various products in the past decades. The majority of biopharmaceuticals that are presently undergoing clinical trials or are available on the market are manufactured through fed-batch fermentations. A crucial process parameter in fed-batch cultivations is the substrate feed rate, which directly influences the overall process productivity, product quality and process repeatability; henceforth, effective control of this parameter is imperative for a successful fed-batch fermentation process. Two distinct control strategies can be distinguished—open-loop and closed-loop (feedback) control. Each of these methods has its own set of benefits, limitations and suitability for specific bioprocesses. This article surveys and compares the most popular open- and closed-loop methods for substrate feed rate control in fed-batch fermentations. Emphasis is placed on model-predictive feed rate control (MPC)—a stand out among other methods that offers a promising application perspective. The authors also demonstrate a practical example of the implementation of a robust, flexible MPC solution that is suitable for various cultures and runs on standard computer hardware, thus overcoming one of the main reported MPC drawbacks—high computational requirements.

With the increasing number of applications for both surface and underwater autonomous vehicles, a great amount of control methods and guidance principles has been developed over the years. This work proposes a review of the most common of these methods. It is mainly focused on model-based nonlinear control methods and guidance principles. Notably, this work details examples and variations of model-based linearizing controllers, applications of line of sight guidance, sliding mode controllers and several other less common control methods for both fully-actuated and underactuated vehicles. Additionally, this work proposes an alternative definition of underactuation with respect to the task allowing for a better understanding of the consequences of underactuation on control. Comparison of fully-actuated and underactuated cases shows how control laws can be used to solve the problems of underactuation and what mechanisms can be used to compensate for the lack of actuation on a degree of freedom. The reviewed methods are compared and discussed with respect to their capabilities, limitations and suitability for typical tasks.

Soft growing robots, which mimic the biological growth of plants, have demonstrated excellent performance in navigating tight and distant environments due to their flexibility and extendable lengths of several tens of meters. However, controlling the position of the tip of these robots can be challenging due to the lack of precise methods for measuring the robots’ Cartesian position in their working environments. Moreover, classical control techniques are not suitable for these robots because they involve the irreversible addition of materials, which introduces process constraints. In this paper, we propose two optimization-based approaches, combining Moving Horizon Estimation (MHE) with Nonlinear Model Predictive Control (NMPC), to achieve superior performance in point stabilization, trajectory tracking, and obstacle avoidance for these robots. MHE is used to estimate the entire state of the robot, including its unknown Cartesian position, based on available configuration measurements. The proposed NMPC approach considers process constraints by relying on the estimated state to ensure optimal performance. We perform numerical simulations using the nonlinear kinematic model of a vine-like robot, one of the newly introduced plant-inspired growing robots, and achieve satisfactory results in terms of reduced computation times and tracking error.

Hybrid electric vehicles are promising solutions to the need for cleaner transport. Their ability to drive fully electric also opens the possibility of zero local emission operation by turning off the internal combustion engine. However, prolonged periods with the engine turned off result in a cooldown of the aftertreatment system resulting in increased emissions when the engine is restarted. To remedy this problem, an emission management strategy that, based on pre-heating of the aftertreatment system, aims to reduce the impact of a prolonged engine-off event on NOx emissions is developed. The method works by locating each engine-off event and then handling each event separately using an optimization scheme that combines pre-heating and a causal heuristic emission management strategy. The individual events are linked using an equivalence factor that describes the decided trade-off between fuel and NOx. The equivalence factor can be chosen heuristically or iteratively to give the desired result in terms of NOx reduction and fuel consumption. The strategy is evaluated using simulations of a drayage drive cycle with multiple engine-off events. The results from the simulations show that for engine-off times below 0.5 h the strategy can reduce NOx compared to the baseline strategy while using the same amount of fuel. If the strategy is allowed more fuel, significant reductions in NOx can be seen for engine-off times up to 1.5 h, after which an exponential decay in the effectivity of the strategy is observed. It is also shown that the reduction in NOx is fairly linear in the equivalence factor, which gives the procedure of choosing it a predictable behavior.

This article proposes a review of the modeling approaches for floating offshore wind turbines (FOWTs) for nonlinear control design. The aerodynamic, hydrodynamic and mooring line dynamic modules for the FOWT have been reviewed to provide an overview of several modeling approaches with their respective features. Next, three control-oriented models from the literature are revisited by presenting their methodological approaches to modeling and identification. These three models cover the three most popular types of FOWTs. Then, the performances of these models are validated with the open fatigue, aerodynamics, structures, and turbulence (OpenFAST) code, and their performances are evaluated according to several criteria. Finally, one of the three models is used to illustrate a nonlinear second-order sliding mode control based on the twisting algorithm to optimize the performance of the FOWT in terms of energy extraction and reduction in the platform pitch oscillation.

This work presents a methodological framework for the design of a resource-aware supervisory control system for process networks with model uncertainty and communication resource constraints. The developed framework aims to balance the objective of closed-loop stabilization of the overall network with that of meeting the local performance requirements of the component subsystems while keeping the rate of data transfer between the local control systems to a minimum. First, a quasi-decentralized networked control structure, with a set of local model-based controllers communicating with one another over a shared communication medium at discrete times, is designed. A Lyapunov stability analysis of the closed-loop system is then carried out, and the results are used to derive appropriate bounds on the local model state estimation errors as well as the dissipation rates of the local control Lyapunov functions. These bounds are used as stability and performance thresholds to trigger communication between the local control systems and a higher-level supervisor that coordinates the transfer of state measurements between the distributed control systems. A breach of the local stability and performance thresholds generates alarm signals which are transmitted to the supervisor to determine which subsystems should communicate with one another. The supervisor employs a composite Lyapunov function to assess the impact of the local threshold breaches on the stability of the overall closed-loop system. The supervisory communication logic takes account of the evolution of the local and composite Lyapunov functions in order to balance the stability and local performance requirements. Finally, the developed framework is demonstrated using a representative chemical process network and compared with other unsupervised event-based control approaches. It is shown that the supervisory event-based control approach leads to a more judicious utilization of network resources that helps improve closed-loop process performance in the presence of unexpected disturbances and input rate constraints.

Rehabilitation in the form of locomotion assistance and gait training through robotic exoskeletons requires both precision and accuracy to achieve effective results. The essential challenge is to ensure robust tracking of the reference signal, i.e., of the gait or locomotion. This paper presents the design of model-based (MB) and model-free (MF) robust control strategies to achieve desired performance and robustness in terms of transient behavior and steady-state/tracking error, implementable to the locomotion assistance and gait training by exoskeletons. The dynamic responses of the exoskeleton system were investigated with both the control strategies. The study was carried out with a variety of reference signals and performance was evaluated to identify the best suited approach for rehabilitation exoskeletons. In case of the model-based control, a mathematical model of the system was developed using a bond graph modeling technique and a lead compensated H-infinity reference gain controller was designed to ensure robust tracking performance. In the model-free control strategy, however, the system function is approximated using radial basis function neural networks (RBFNNs) and an adaptive proportional-derivative RBFNN controller was designed to achieve the desired results with minimum tracking error. Both strategies make the system robust and stable. However, the MF control strategy is faster for all reference inputs as compared to the MB control strategy i.e., faster to approach the peak value and settle, and rapidly approaches the zero steady-state/tracking error. The rise time in the case of a sinusoidal input for model-free control is 0.4 s faster than the rise time in model-based control. Similarly, the settling time is 3.9 s faster in the case of model-free control, which is a prominent difference and can provide better rehabilitation results.

Electrohydraulic drives consist of several nonlinear elements, and the use and design of their control system is difficult. One of the most important parameters of the industrial drive is its settling time, which characterizes their dynamics. An interesting control method, which can be used for the improvement of electrohydraulic drive properties, is the model-following control (MFC). This technique enables to ensure the good control parameters of nonlinear systems in quite an easy way. In this paper, a modification consisting of changing the oil flow factor in the reference model is proposed. This improves both the dynamics of the electrohydraulic servo drive and the adaptation to the load forces, and thus energy consumption. The article describes the application of the MFC method to control an electrohydraulic servo drive in which a special proportional valve, with an permanent magnet synchronous motor (PMSM), is used. To verify the effectiveness of this method, a theoretical description of the drive is proposed. The applied MFC control system is then modeled and investigated in simulations. Lastly, laboratory investigations are made and chosen results are presented and described in the paper. The results obtained in simulation are compared with results obtained in laboratory tests. The investigations show that the usage of MFC shortens the drive settling time and, in this way, improves its dynamics.

Recent advances in neuroscience have characterised brain function using mathematical formalisms and first principles that may be usefully applied elsewhere. In this paper, we explain how active inference—a well-known description of sentient behaviour from neuroscience—can be exploited in robotics. In short, active inference leverages the processes thought to underwrite human behaviour to build effective autonomous systems. These systems show state-of-the-art performance in several robotics settings; we highlight these and explain how this framework may be used to advance robotics.

Reactivity controlled compression ignition (RCCI) is a highly efficient and clean combustion concept, which enables the use of a wide range of renewable fuels. Consequently, this promising dual fuel combustion concept is of great interest for realizing climate neutral future transport. RCCI is very sensitive for operating conditions and requires advanced control strategies to guarantee stable and safe operation. For real-world RCCI implementation, we face control challenges related to transients and varying ambient conditions. Currently, a multivariable air–fuel path controller that can guarantee robust RCCI engine operation is lacking. In this work, we present a RCCI engine controller, which combines static decoupling and a diagonal MIMO feedback controller. For control design, a frequency domain-based approach is presented, which explicitly deals with cylinder-to-cylinder variations using data-driven, cylinder-individual combustion models. This approach enables a systematic trade-off between fast and robust performance and gives clear design criteria for stable operation. The performance of the developed multivariable engine controller is demonstrated on a six-cylinder diesel-E85 RCCI engine. From experimental results, it is concluded that the RCCI engine controller accurately tracks the five desired combustion and air path parameters, simultaneously. For the studied transient cycle, this results in 12.8% reduction in NOx emissions and peak in-cylinder pressure rise rates are reduced by 3.8 bar/deg CA. Compared to open-loop control, the stable and safe operating range is increased from 25 °C up to 35 °C intake manifold temperature and maximal load range is increased by 14.7% up to BMEP = 14.8 bar.

A new procedure, based on measurement of intake CO2 concentration and ambient humidity was developed and assessed in this study for different diesel engines in order to evaluate the oxygen concentration in the intake manifold. Steady-state and transient datasets were used for this purpose. The method is very fast to implement since it does not require any tuning procedure and it involves just one engine-related input quantity. Moreover, its accuracy is very high since it was found that the absolute error between the measured and predicted intake O2 levels is in the ±0.15% range. The method was applied to verify the performance of a previously developed NOx model under transient operating conditions. This model had previously been adopted by the authors during the IMPERIUM H2020 EU project to set up a model-based controller for a heavy-duty diesel engine. The performance of the NOx model was evaluated considering two cases in which the intake O2 concentration is either derived from engine-control unit sub-models or from the newly developed method. It was found that a significant improvement in NOx model accuracy is obtained in the latter case, and this allowed the previously developed NOx model to be further validated under transient operating conditions.

The Brushless Doubly Fed Reluctance Machine (BDFRM) is an emerging alternative for variable speed drive systems, providing a significant downsizing of the power electronics converter. This paper proposes a new view on the machine equations, allowing the reuse of the standard control system design for conventional synchronous and asynchronous machines: a cascade control system with an inner current control- and outer speed control loop. The assumptions and simplifications made on the machine model allow for a simple, model-based approach to set the controller gains in a brushless doubly fed machine drive system. The cascade control scheme is combined with an Unscented Kalman Filter as a state observer, capable of estimating the load torque and losses. The performance of the proposed control system design is checked in simulation and tested in real-time on a low power BDFRM prototype.

This paper presents a motion control scheme for a new concept of omnidirectional aerial vehicle for transportation and manipulation tasks. The considered aerial platform is a novel quadrotor with the capability of providing multi-directional thrust by adding an actuated gimbal mechanism in charge of modifying the orientation of the frame on which the four rotors are mounted. The above mechanical design, differently from other omnidirectional unmanned aerial vehicles (UAVs) with tilted propellers, avoids internal forces and energy dissipation due to non-parallel propellers’ axes. The proposed motion controller is based on a hierarchical two-loop scheme. The external loop computes the force to be applied to the vehicle and the reference values for the additional joints, while the inner loop computes the joint torques and the moment to be applied to the multirotor. In order to make the system robust with respect to the external loads, a compensation of contact forces is introduced by exploiting the estimate provided by a momentum based observer. The stability of the motion control scheme is proven via Lyapunov arguments. Finally, two simulation case studies prove the capability of the omnidirectional UAV platform to track a 6-DoFs trajectory both in free motion and during a task involving grasping and transportation of an unknown object.

As the share of power converter-based renewable energy sources (RESs) is high, a microgrid, in islanded mode, is more vulnerable to frequency instability due to (1) sudden power imbalance and (2) low inertia. One of the most common approaches to address this issue is to provide virtual inertia to the system by appropriately controlling the grid-side converter of the RESs. However, the primary frequency controller (PFC) presented in this paper focuses on the fast compensation of power imbalance without adding inertia to the system. The proposed method is based on estimating the real-time power imbalance caused by a disturbance and compensating it using multiple small-scale distributed battery energy storage systems (BESSs). The power imbalance is estimated by observing the initial rate of change of frequency (RoCoF) following a disturbance. Based on the estimated power imbalance and the rating of the BESSs, the reference power for the BESSs is determined. The BESSs are controlled in grid-following mode to compensate for the power imbalance. The performance of the proposed PFC is verified using a Typhoon real-time simulator for various scenarios and is compared with the conventional virtual synchronous generator (VSG) controller.

In recent years the increasing needs of reducing the costs of car development expressed by the automotive market have determined a rapid development of virtual driver prototyping tools that aims at reproducing vehicle behaviors. Nevertheless, these advanced tools are still not designed to exploit the entire vehicle dynamics potential, preferring to assure the minimum requirements in the worst possible operating conditions instead. Furthermore, their calibration is typically performed in a pre-defined strict range of operating conditions, established by specific regulations or OEM routines. For this reason, their performance can considerably decrease in particularly crucial safetycritical situations, where the environmental conditions (rain, snow, ice), the road singularities (oil stains, puddles, holes), and the tyre thermal and ageing phenomena can deeply affect the adherence potential. The objective of the work is to investigate the possibility of the physical model-based control to take into account the variations in terms of the dynamic behavior of the systems and of the boundary conditions. Different scenarios with specific tyre thermal and wear conditions have been tested on diverse road surfaces validating the designed model predictive control algorithm in a hardware-in-the-loop real-time environment and demonstrating the augmented reliability of an advanced virtual driver aware of available information concerning the tyre dynamic limits. The multidisciplinary proposal will provide a paradigm shift in the development of strategies and a solid breakthrough towards enhanced development of the driving automatization systems, unleashing the potential of physical modeling to the next level of vehicle control, able to exploit and to take into account the multi-physical tyre variations.

This paper discusses the stability of systems controlling patient body weight support systems which are used in gait re-education. These devices belong to the class of underactuated mechanical systems. This is due to the application of elastic shock-absorbing connections between the active part of the system and the passive part which impacts the patient. The model takes into account properties of the system, such as inertia, attenuation and susceptibility to the elements. Stability is an essential property of the system due to human–device interaction. In order to demonstrate stability, Lyapunov’s theory of stability, which is based on the model of system dynamics, was applied. The stability of the control system based on a model that requires knowledge of the structure and parameters of the equations of motion was demonstrated. Due to inaccuracies in the modeling of the rope (one of the basic elements of the device), an adaptive control system was introduced and its stability was also proved. The authors conducted simulation and experimental tests that illustrate the functionality of the analyzed control systems.

In this study, we proposed an advanced control system for tilting-ladle-type automatic pouring machines in the casting industry. Automatic pouring machines have been introduced recently to improve the working environment of the pouring process. In the conventional study on pouring control, it has been confirmed that the pouring flow rate control contributes to improving the accuracy of the entire automatic pouring machine, such as the outflow liquid’s falling position from the ladle, the liquid’s weight filled in the mold, and the sprue cup’s liquid level. However, the conventional control system has problems: it is not easy to precisely pour the liquid in the ladle with a large tilting angle, and it takes time to adjust the control parameters. Therefore, we proposed the feedforward pouring flow rate control system, constructed by the pouring process’ inverse model with the online model parameters identification. In this approach, we derived the pouring process’ mathematical model, representing precisely the pouring process with the ladle’s large tilting angle. The model parameters in the pouring process’ inverse model in the controller are updated online via the model parameters identification. To verify the proposed pouring control system’s efficacy, we experimented using the tilting-ladle-type automatic pouring machine. In the experimental results, the mean absolute error between the outflow liquid’s weight and the reference weight was improved from 0.1346 at the first pouring to 0.0498 at the fifth pouring. Moreover, the model parameters were identified within 4 s. Therefore, it enables updating the controller’s parameters within each pouring motion interval by the proposed approach.

In the present paper, a model-based controller of engine torque and engine-out Nitrogen oxide (NOx) emissions, which was previously developed and tested by means of offline simulations, has been validated on a FPT F1C 3.0 L diesel engine by means of rapid prototyping. With reference to the previous version, a new NOx model has been implemented to improve robustness in terms of NOx prediction. The experimental tests have confirmed the basic functionality of the controller in transient conditions, over different load ramps at fixed engine speeds, over which the average RMSE (Root Mean Square Error) values for the control of NOx emissions were of the order of 55–90 ppm, while the average RMSE values for the control of brake mean effective pressure (BMEP) were of the order of 0.25–0.39 bar. However, the test results also highlighted the need for further improvements, especially concerning the effect of the engine thermal state on the NOx emissions in transient operation. Moreover, several aspects, such as the check of the computational time, the impact of the controller on other pollutant emissions, or on the long-term engine operations, will have to be evaluated in future studies in view of the controller implementation on the engine control unit.

The motivation for this paper comes from a real need to have an overview of the challenges of modelling and control for very demanding systems, such as wind turbine systems, which require reliability, availability, maintainability, and safety over power conversion efficiency. These issues have begun to stimulate research and development in the wide control community particularly for these installations that need a high degree of “sustainability”. Note that this represents a key point for offshore wind turbines, since they are characterised by expensive and/or safety critical maintenance work. In this case, a clear conflict exists between ensuring a high degree of availability and reducing maintenance times, which affect the final energy cost. On the other hand, wind turbines have highly nonlinear dynamics, with a stochastic and uncontrollable driving force as input in the form of wind speed, thus representing an interesting challenge also from the modelling point of view. Suitable control methods can provide a sustainable optimisation of the energy conversion efficiency over wider than normally expected working conditions. Moreover, a proper mathematical description of the wind turbine system should be able to capture the complete behaviour of the process under monitoring, thus providing an important impact on the control design itself. In this way, the control scheme could guarantee prescribed performance, whilst also giving a degree of “tolerance” to possible deviation of characteristic properties or system parameters from standard conditions, if properly included in the wind turbine model itself. The most important developments in advanced controllers for wind turbines are also briefly referenced, and open problems in the areas of modelling of wind turbines are finally outlined.

Continuous manufacturing (CM) is a promising strategy to achieve various benefits in the context of quality, flexibility, safety and cost in pharmaceutical production. One of the main technical challenges of CM is that the process needs to handle transient conditions such as the start-up phase before state of control operation is reached, which can potentially cause out-of-specification (OOS) material. In this context, the presented paper aims to demonstrate that suitable process control strategies during start-up of a continuous granulation and drying operation can limit or even avoid OOS material production and hence can ensure that the provided benefits of CM are not compromised by poor production yields. In detail, heat-up of the drying chamber prior the start of production can lead to thermal energy being stored inside of the stainless-steel housing, acting as an energy buffer that is known to cause over-dried granules in the first few minutes of the drying process. To compensate this issue, an automatic ramping procedure of dryer rotation speed (and hence drying time) was introduced into the plant’s process control system, which counteracts the excessive drying capacity during start-up. As a result, dry granules exiting the dryer complied with the targeted intermediate critical quality attribute loss-on-drying (LOD) from the very beginning of production.

Lightweight, compliant actuators are particularly desirable in robotic systems intended for interaction with humans. Pneumatic artificial muscles (PAMs) exhibit these characteristics and are capable of higher specific work than comparably-sized hydraulic actuators and electric motors. The objective of this work is to develop a control algorithm that can smoothly and accurately track the desired motions of a manipulator actuated by pneumatic artificial muscles. The manipulator is intended for lifting humans in nursing assistance or casualty extraction scenarios; hence, the control strategy must be capable of responding to large variations in payload over a large range of motion. The present work first investigates the feasibility of two output feedback controllers (proportional-integral-derivative and fuzzy logic), but due to the limitations of pure output feedback control, a model-based feedforward controller is developed and combined with output feedback to achieve improved closed-loop performance. The model upon which the controller is based incorporates the internal airflow dynamics, the physical parameters of the pneumatic muscles and the manipulator dynamics. Simulations were performed in order to validate the control algorithms, guide controller design and predict optimal gains. Using real-time interface software and hardware, the controllers were implemented and experimentally tested on the manipulator, demonstrating the improved capability.

In this work, we present and test an approach based on an inverse model applicable to the control of induction heat treatments. The inverse model is comprised of a simplified analytical forward model trained with experiments to predict and control the temperature of a location in a cylindrical sample starting from any initial temperature. We solve the coupled nonlinear electromagnetic-thermal problem, which contains a temperature dependent parameter α to correct the electromagnetic field on the surface of a cylinder, and as a result effectively the modeled temperature elsewhere in the sample. A calibrated model to the measurement data applied with the process information such as the operating power level, current, frequency, and temperature provides the basic ingredients to construct an inverse model toolbox, which finally enables us to conduct experiments with more specific goals. The input set values of the power supply, i.e., the power levels in the test rig control system, are determined within an iterative framework to reach specific target temperatures in prescribed times. We verify the concept on an induction heating test rig and provide two examples to illustrate the approach. The advantages of the method lie in its simplicity, computationally cost effectiveness and independence of a prior knowledge of the internal structure of power supplies.

The new electric power generation scenario, characterized by growing variability due to the greater presence of renewable energy sources (RES), requires more restrictive dynamic requirements for conventional power generators. Among traditional power generators, gas turbines (GTs) can regulate the output electric power faster than any other type of plant; therefore, they are of considerable interest in this context. In particular, the dynamic performance of a GT, being a highly nonlinear and complex system, strongly depends on the applied control system. Proportional−integral−derivative (PID) controllers are the current standard for GT control. However, since such controllers have limitations for various reasons, a model predictive control (MPC) was designed in this study to enhance GT performance in terms of dynamic behavior and robustness to model uncertainties. A comparison with traditional PID-based controllers and alternative model-based control approaches (feedback linearization control) found in the literature demonstrated the effectiveness of the proposed approach.

An advanced model-based control method for the integrated fuel processing and a fuel cell system consisting of ethanol reforming, hydrogen purification, and a proton exchange membrane fuel cell is presented. For process identification, a physical model of the process chain was constructed. Subsequently, the simulated process was approximated with data-driven control models. Based on these control models, a hierarchical control framework consisting of model predictive controller and a global optimization algorithm was introduced. The performance of the new control method was evaluated with simulations. Results indicate that the new optimization concept enables resource efficient and fast control of the studied energy conversion process. Fast and efficient fuel cell process could then provide sustainable power source for autonomous and mobile applications in the future.