State Machine Control Research Articles

Online Multi-Level Energy Management Strategy Based on Rule-Based and Optimization-Based Approaches for Fuel Cell Hybrid Electric Vehicles

This paper introduces an online multi-level energy management strategy (EMS) based on proposed rule-based and optimization-based approaches for fuel hybrid electric vehicles, including fuel cell, battery, and ultracapacitor systems. Our approach combines equivalent consumption minimization, state machine control, operational mode control, and fuzzy logic control methods. The proposed multi-level EMS reduces fuel consumption, enhances fuel cell operating time in a high-efficiency range, reduces battery power fluctuations, and improves maintaining the battery state of charge (SOC). The proposed EMS is compared with the optimized-fuzzy logic control (Opt-FLC) method. The results show a reduction in fuel consumption, battery power fluctuations, and the SOC difference between the start and end of the driving cycle, compared to Opt-FLC. Hence, fuel economy improvement and lifetime enhancement of hybrid energy storage system are the significant outcomes of new proposed multi-level EMS.

Implementation of self-adaptive Harris Hawks Optimization-based energy management scheme of fuel cell-based electric power system

This paper presents a hybrid Fuel Cell-based Power System (FCPS) consisting of fuel cell and hybrid Energy Storage Systems (ESSs), including a battery with high energy density and supercapacitor with high power density to overcome the sudden load demand change and improving the reliability of the delivered power. Any hybrid power system needs Energy Management Strategies (EMS) to balance the power between the different energy sources. In this paper, a comparative analysis of three energy management strategies, including the state machine control method, the classical PI control method and equivalent consumption minimization strategy (ECMS) is performed. The paper's main objective is enhancing the DC-bus voltage profile of a hybrid fuel cell/battery/supercapacitor power system equipped with the developed under-mentioned EMS by using a hybrid modified optimization technique that combines Harris Hawks optimization (HHO) and Sine Cosine Algorithm (SCA). The new hybrid HHO-SCA is employed to determine the optimal control parameters of the DC-bus voltage controller, which significantly assists in enhancing the DC-bus voltage profile as well as the performance of the applicable ESS in terms of improving efficiency and SoC. The effectiveness of the suggested control schemes is simulated using MATLAB/SIMULINK software. The simulation results confirmed that the proposed HHO-SCA is superior and efficient in improving the DC-bus voltage.

Minimum hydrogen consumption based control strategy of fuel cell/PV/battery/supercapacitor hybrid system using recent approach based parasitism-predation algorithm

In hybrid renewable energy sources containing different storage devices like fuel cells, batteries, and supercapacitors, minimizing the hydrogen consumption is the main target for economic aspects and operation enhancement. External energy maximization strategy (EEMS) is the most popular energy management strategy used with hybrid renewable energy sources. However, gradient-based method is employed in EEMS which has low convergence, moreover it doesn’t guarantee the optimum solution. Therefore, this paper proposes for first time an energy management strategy based on recent metaheuristic optimizer of parasitism-predation algorithm employed in hybrid source comprises photovoltaic/fuel cell/battery/supercapacitor for supplying aircraft in emergency state during landing. The main target is hydrogen consumption minimization, this helps in enhancing the power durability to the aircraft in case of curtailment of the main power source. The selection of parasitism-predation algorithm (PPA) is due to requirement of less parameters defined by the user and its high convergence ability. The proposed strategy is compared to other conventional and programmed approaches of state machine control, water cycle algorithm, dynamic differential annealed optimization, spotted hyena optimizer, EEMS, marine predator algorithm, and mayfly optimization algorithm. The obtained results confirmed the superiority of the proposed method achieving efficiency of 95.34% and minimum hydrogen consumption of 15.7559 gm.

Energy management and optimization of PEMFC/battery mobile robot based on hybrid rule strategy and AMPSO

Due to the flexibility of seam tracking robot in special environment, independent power supply with different characteristics is adopted. Power allocation of hybrid power system composed of proton exchange membrane fuel cell and lithium battery is mainly researched. According to the requirements of economy and safety, hybrid rule fuzzy state machine control is designed to realize the optimal operation and fast response of power supply system. On the basis of energy allocation, further reduction of hydrogen consumption and power fluctuation is considered, which improves the power supply scheme. The sensitivity analysis method is used to screen the optimization variables, which reduces the complexity of the strategy; in addition, an adaptive mutation particle swarm optimization is studied to optimize the strategy, which combines the mutation idea, adaptively changes the weight and learning factor, and realizes the minimization of power fluctuation and equivalent hydrogen consumption. The results show that the stability of fuel cell and the rationality of lithium battery charging and discharging are greatly improved, and the fuel economy and service life of hybrid power system are improved.

Online extremum seeking-based optimized energy management strategy for hybrid electric tram considering fuel cell degradation

In order to realize optimal power distribution between proton exchange membrane fuel cell and supercapacitor in hybrid electric tram, an online extremum seeking-based optimized energy management strategy is proposed in this work. Considering that the fuel cell is a complex nonlinear system, its performance will vary as the external parameters change, so it is necessary to consider the performance state of stack. An online extremum seeking algorithm is investigated in this work to seek the maximum power and maximum efficiency points by searching the variation in fuel cell performance. Besides, this work also updates its “safe operating zone” based on the results of the online extremum seeking. This process is achieved by the adaptive recursive least square algorithm. Furthermore, in order to limit the power dynamic of fuel cell, the degradation of the stack is considered in this study. To guarantee the stable and continued operation of the electric tram, the state of charge fluctuation range of supercapacitor is also limited. The effectiveness of the presented method is successfully verified under scaled-down operating condition of hybrid electric tram on the reduced-scale test platform. The proposed method is also compared with state machine control and equivalent consumption minimization strategy to further demonstrate that it has advantages in hydrogen consumption, state of charge fluctuation, efficiency, and fuel cell output power dynamics.

Design and FPGA implementation of multi-wing chaotic switched systems based on a quadratic transformation* *Project supported by the National Natural Science Foundation of China (Grant Nos. 61973199 and 61973200) and the Taishan Scholar Project of Shandong Province of China.

Based on a quadratic transformation and a switching function, a novel multi-wing chaotic switched system is proposed. First, a 4-wing chaotic system is constructed from a 2-wing chaotic system on the basis of a quadratic transformation. Then, a switching function is designed and by adjusting the switching function, the number and the distribution of the saddle-focus equilibrium points of the switched system can be regulated. Thus, a set of chaotic switched systems, which can produce 6-to-8-12-16-wing attractors, are generated. The Lyapunov exponent spectra, bifurcation diagrams, and Poincaré maps are given to verify the existence of the chaotic attractors. Besides, the digital circuit of the multi-wing chaotic switched system is designed by using the Verilog HDL fixed-point algorithm and the state machine control. Finally, the multi-wing chaotic attractors are demonstrated via FPGA platform. The experimental results show that the number of the wings of the chaotic attractors can be expanded more effectively with the combination of the quadratic transformation and the switching function methods.

Energy management of fuel cell electric vehicles based on working condition identification of energy storage systems, vehicle driving performance, and dynamic power factor

Abstract Energy management strategy is one of the main challenges in the development of fuel cell electric vehicles equipped with various energy storage systems. The energy management strategy should be able to provide the power demand of the vehicle in different driving conditions, minimize equivalent fuel consumption of fuel cell, and improve the total efficiency of energy storage systems. This paper presents a new bi-level online energy management for a battery-based fuel cell electric vehicle based on operational mode control, state machine control, equivalent consumption minimization, and dynamic power factor strategies. The purpose of this paper is the identification of vehicle driving conditions, determination of hydrogen fuel value based on fuel cell output power, classification of battery state of charge based on battery combined efficiency, and optimal power distribution of energy storage systems. This strategy works such that the battery charging is done at the optimum point of the fuel cell in order to increase the total efficiency of energy storage systems and satisfy subsequent maximum instantaneous power in different driving conditions. The proposed bi-level energy management strategy obtains significant simulation results, including a reduction in fuel consumption, reduction in power fluctuations, and increment in the energy efficiency of energy storage systems compared to other energy management strategies.

Enhancing the operation of fuel cell-photovoltaic-battery-supercapacitor renewable system through a hybrid energy management strategy

It is necessary to have an energy management system based on one or more control strategies to sense, monitor, and control the behavior of the hybrid energy sources. In renewable hybrid power systems containing fuel cells and batteries, the hydrogen consumption reduction and battery state of charge (SOC) utilizing are the main objectives. These parameters are essential to get the maximum befits of cost reduction as well as battery and hydrogen storage lifetime increasing. In this paper, a novel hybrid energy management system (HEMS) was designed to achieve these objectives. A renewable hybrid power system combines: PV, PEMFC, SC, and Battery was designed to supply a predetermined load with its needed power. This (REHPS) depends on the PV power as a master source during the daylight. It uses the FC to support as a secondary source in the night or shading time. The battery is helping the FC when the load power is high. The supercapacitor (SC) is working at the load transient or load fast change. The proposed energy management system uses fuzzy logic and frequency decoupling and state machine control strategies working together as a hybrid strategy where the switching over between both strategies done automatically based on predetermined values to obtain the minimum value of hydrogen consumption and the maximum value of SOC at the same time. The proposed HEMS achieves 19.6% Hydrogen consumption saving and 5.4% increase in SOC value compared to the results of the same two strategies when working as a stand-alone. The load is designed to show a surplus power when the PV power is at its maximum value. This surplus power is used to charge the battery. To validate the system, the results were compared with the results of each strategy if working separately. The comparison confirms the achievement of the hybrid energy management system goal.

Center-Articulated Hydrostatic Cotton Harvesting Rover Using Visual-Servoing Control and a Finite State Machine

Multiple small rovers can repeatedly pick cotton as bolls begin to open until the end of the season. Several of these rovers can move between rows of cotton, and when bolls are detected, use a manipulator to pick the bolls. To develop such a multi-agent cotton-harvesting system, each cotton-harvesting rover would need to accomplish three motions: the rover must move forward/backward, turn left/right, and the robotic manipulator must move to harvest cotton bolls. Controlling these actions can involve several complex states and transitions. However, using the robot operating system (ROS)-independent finite state machine (SMACH), adaptive and optimal control can be achieved. SMACH provides task level capability for deploying multiple tasks to the rover and manipulator. In this study, a center-articulated hydrostatic cotton-harvesting rover, using a stereo camera to locate end-effector and pick cotton bolls, was developed. The robot harvested the bolls by using a 2D manipulator that moves linearly horizontally and vertically perpendicular to the direction of the rover’s movement. We demonstrate preliminary results in an environment simulating direct sunlight, as well as in an actual cotton field. This study contributes to cotton engineering by presenting a robotic system that operates in the real field. The designed robot demonstrates that it is possible to use a Cartesian manipulator for the robotic harvesting of cotton; however, to reach commercial viability, the speed of harvest and successful removal of bolls (Action Success Ratio (ASR)) must be improved.

A Coordinated Control Strategy for a Diesel-Electric Tugboat System for Improved Fuel Economy

Recent growth in seaborne trade has increased the tugboat operation cycle to maneuver incoming marine vessels at port. To meet the tugboat load demand for maneuvering, multiple power sources are being employed on-board. This necessitates a coordinated control strategy for the effective operation of these multiple power sources. To design an effective coordinated control strategy, this article initially estimates multiple power sources capacities (doubly fed induction machine (DFIM) and battery storage system) for a diesel-electric tugboat system based on a real-time tugboat operation cycle obtained from Visakhapatnam port, India. With respect to these multiple power sources, a coordinated control strategy for a better fuel efficiency is proposed based on a state machine control algorithm. The proposed control strategy operates a variable speed diesel-electric generator (diesel engine coupled DFIM) at a minimum specific fuel consumption value and battery based on its state of charge. To show the efficacy of the proposed variable speed diesel-electric system, fuel consumption is computed, which resulted in 26.42% of saving in comparison with a traditionally used diesel-mechanical tugboat. To evaluate the real-time system (2 MW DFIM + 150 kWh battery), MATLAB/Simulink tool is used and an experimental demonstration is performed on a laboratory prototype.

Enhanced appearance-based finger detection and tracking using finite state machine control

Real-time finger detection and tracking systems have been growing rapidly in the past decade. Among those methods, Appearance-based and Model-based methods have produced excellent results. However, the occlusion issue is one of the main challenges in this field. In this study, we address this issue by considering the repeating-finite gestures of a guitar-strumming or a hand puppet and, represent using a Finite State Machine model. Also we proposed a novel finger pose tracking system using FSM Model combining with the appearance-based method.. The proposed system consists of two parts: FSM-FT builder creates the FSM hand, and the FSM-FT runner controls the FSM-FT system. Empirically, we conducted an experimental study involving one sample repeating hand gesture and our approach achieved a significance recognition rate of 82% in the testing phase.

Enhanced Intelligent Energy Management System for a Renewable Energy-Based AC Microgrid

This paper proposes an enhanced energy management system (EEMS) for a residential AC microgrid. The renewable energy-based AC microgrid with hybrid energy storage is broken down into three distinct parts: a photovoltaic (PV) array as a green energy source, a battery (BT) and a supercapacitor (SC) as a hybrid energy storage system (HESS), and apartments and electric vehicles, given that the system is for residential areas. The developed EEMS ensures the optimal use of the PV arrays’ production, aiming to decrease electricity bills while reducing fast power changes in the battery, which increases the reliability of the system, since the battery undergoes fewer charging/discharging cycles. The proposed EEMS is a hybrid control strategy, which is composed of two stages: a state machine (SM) control to ensure the optimal operation of the battery, and an operating mode (OM) for the best operation of the SC. The obtained results show that the EEMS successfully involves SC during fast load and PV generation changes by decreasing the number of BT charging/discharging cycles, which significantly increases the system’s life span. Moreover, power loss is decreased during passing clouds phases by decreasing the power error between the extracted power by the sources and the required equivalent; the improvement in efficiency reaches 9.5%.

High throughput and low area architectures of secure IoT algorithm for medical image encryption

The favor that lightweight encryption algorithms have gained over the recent years regarding data and image security in a resource constrained environment of the smart device sort in Internet of Things (IoT) is undeniable. Secure IoT (SIT) is a popular encryption algorithm that tends to consume less hardware resources due to simple operation, which serves a welcome advantage, since it is precisely the reduction of area and power consumption. In this paper, high-speed and low-area architectures are proposed for SIT algorithm under resource constrained applications. The proposed pipelined architecture is useful in high frequency applications, and proposed serial architecture is useful for low area requirement, leads to reduce the cost incurred on hardware. The proposed design can support four different block sizes of an input medical ultrasonic image, namely 32 × 32, 64 × 64, 128 × 128 and 256 × 256 along with Finite State Machine (FSM) controller. The dynamic size of block selection technique saves significant amount of clock cycles during image encryption. The proposed designs have implemented in FPGA XC5VLX50T-3ff1136 device and achieved maximum operating frequency of 287.51 MHz in pipelined architecture and number of slices 46 in serial architecture. PRESENT is an efficient lightweight block cipher having 73 slices in serial architecture whereas proposed serial architecture provides 58.69% improvement in area. LEA is a popular Feistel block cipher generates maximum frequency of 217 MHz in pipelined architecture, but at the same time proposed pipelined architecture provides 32.25% enhancement in speed.

Deep-Learning-Based Indoor Human Following of Mobile Robot Using Color Feature.

Human following is one of the fundamental functions in human–robot interaction for mobile robots. This paper shows a novel framework with state-machine control in which the robot tracks the target person in occlusion and illumination changes, as well as navigates with obstacle avoidance while following the target to the destination. People are detected and tracked using a deep learning algorithm, called Single Shot MultiBox Detector, and the target person is identified by extracting the color feature using the hue-saturation-value histogram. The robot follows the target safely to the destination using a simultaneous localization and mapping algorithm with the LIDAR sensor for obstacle avoidance. We performed intensive experiments on our human following approach in an indoor environment with multiple people and moderate illumination changes. Experimental results indicated that the robot followed the target well to the destination, showing the effectiveness and practicability of our proposed system in the given environment.

Gaussian Mixture Models for Control of Quasi-Passive Spinal Exoskeletons

Research and development of active and passive exoskeletons for preventing work related injuries has steadily increased in the last decade. Recently, new types of quasi-passive designs have been emerging. These exoskeletons use passive viscoelastic elements, such as springs and dampers, to provide support to the user, while using small actuators only to change the level of support or to disengage the passive elements. Control of such devices is still largely unexplored, especially the algorithms that predict the movement of the user, to take maximum advantage of the passive viscoelastic elements. To address this issue, we developed a new control scheme consisting of Gaussian mixture models (GMM) in combination with a state machine controller to identify and classify the movement of the user as early as possible and thus provide a timely control output for the quasi-passive spinal exoskeleton. In a leave-one-out cross-validation procedure, the overall accuracy for providing support to the user was % (mean ± s.d.) with a sensitivity and specificity of % and % respectively. The results of this study indicate that our approach is a promising tool for the control of quasi-passive spinal exoskeletons.

Design and Virtex-7-Based Implementation of Video Chaotic Secure Communications

In this paper, a Virtex-7-based video chaotic secure communication scheme is investigated. First, the network sending and receiving controller Intellectual Property (IP) cores are designed. Next, the chaotic encryption and decryption IP cores are implemented using fixed-point algorithm, pipeline operation, and state machine control. Thus, video capturing, video displaying, network sending, network receiving, chaotic encrypting, and chaotic decrypting can be achieved via IP core integration design. An improved 7D chaotic stream cipher algorithm for resisting divide-and-conquer attack is then designed and realized on a Virtex-7 high-end FPGA platform. Hardware experimental results are also given to verify the feasibility of the scheme.

P.543 Working memory of children: informatization of assessment task

This paper presents a control approach for bounding gait of quadruped robots by applying the concept of Virtual Constraints (VCs). A VC is a relative motion relation between two related joints imposed to the robots in terms of a specified gait, which can drive the robot to run with desired gait. To determine VCs for highly dynamic bounding gait, the limit cycle motions of the passive dynamic model of bounding gait are analyzed. The leg length and hip/shoulder angle trajectories corresponding to the limit cycles are parameterized by leg angles using 4 th-order polynomials. In order to track the calculated periodic motions, the polynomials are imposed on the robot as virtual motion constraints by a high-level state machine controller. A bounding speed feedback strategy is introduced to stabilize the robot running speed and enhance the stability. The control approach was applied to a newly designed lightweight bioinspired quadruped robot, AgiDog. The experimental results demonstrate that the robot can bound at a frequency up to 5 Hz and bound at a maximum speed of 1.2 m·s−1 in sagittal plane with a Froude number approximating to 1.

A Robot with Decoupled Mechanical Structure and Adapted State Machine Control for Both Ground and Staircase Situations

The rapid development of e-commerce makes the last-mile problem more and more prominent. To meet the requirements of such a scenario, this paper proposes a robot with a decoupled mechanical structure. Such a hexapod robot is divided into a chassis frame with a pair of auxiliary wheel-legs for horizontal movement, a tetrapod for vertical movement, and a slide-and-rail mechanism to connect the former two parts. Such a structure is simple and, thus, cost-efficient; by managing the horizontal and vertical movements, such a robot can keep the balance and safety of the load in the various operation environments, such as roads, staircases, and even indoor ones. Meanwhile, a state-machine-based controller, which adapts well to the unique structure of the proposed robot, is also proposed, simplifying the sequential control of the robot. A prototype robot with 30-kg load ability was built, and the experimental results prove the validity of the design.

An optimized energy management strategy for fuel cell hybrid power system based on maximum efficiency range identification

This study proposes an optimized energy management strategy (EMS) based on maximum efficiency range (MER) identification for a fuel cell/battery hybrid sightseeing car. And the aim of this study is to optimize hydrogen consumption of hybrid system and make sure that the power distribution between the fuel cell (FC) system and battery is optimal. FC system has the MER and is also a strongly coupled system. The MER of FC system will move with the change of operating conditions, and consequently, a parameter identification technique is needed to estimate the boundary powers of MER. This goal is achieved in this paper by using a forgetting factor recursive least square (FFRLS) online identification approach. Then the sequential quadratic programming (SQP) algorithm is used to solve the majorization problem of equivalent consumption minimum strategy (ECMS) so that the FC system operates as much as possible in the MER, while ensuring that the battery state of charge (SOC) fluctuates within the limited range. This helps to improve the efficiency, performance, and durability of the FC system and reduce the equivalent hydrogen consumption of the battery. A reduce-scale test platform is designed to verify the feasibility of the proposed optimized ECMS (OECMS). In addition, the conventional ECMS and rule-based state machine control (SMC) strategy are utilized in this paper to highlight the advantages of the proposed strategy. The experiment results show that the proposed OECMS helps to improve FC performance and optimize system hydrogen consumption.

An Effective Energy Management Strategy Based on Mine-Blast Optimization Technique Applied to Hybrid PEMFC/Supercapacitor/Batteries System

An effective energy management strategy based on the mine-blast optimization (MBA) technique was proposed in this paper to optimally manage the energy in a hybrid power system. The hybrid system was composed of fuel cells, batteries, and supercapacitors. Such system was employed to supply highly fluctuated load. The results of the proposed strategy were compared with previously employed strategies such as fuzzy logic control (FLC), state machine control strategy (SMCS), and equivalent fuel consumption minimization strategy (ECMS). The comparison was carried out in terms of the hydrogen fuel economy and the overall efficiency as the key factors. The resulting responses of the proposed MBA-based management strategy indicate that its performance is the best among the other strategies of SMCS, FLC, and ECMS in both the hydrogen fuel economy and overall efficiency.