Two-stage Control Research Articles

Lane-Change in Dense Traffic With Model Predictive Control and Neural Networks

This article presents an online smooth-path lane-change control framework. We focus on dense traffic where intervehicle space gaps are narrow, and cooperation with surrounding drivers is essential to achieve the lane-change maneuver. We propose a two-stage control framework that harmonizes model predictive control (MPC) with generative adversarial networks (GANs) by utilizing driving intentions to generate smooth lane-change maneuvers. To improve performance in practice, the system is augmented with an adaptive safety boundary and a Kalman filter to mitigate sensor noise. Simulation studies are investigated at different levels of traffic density and cooperativeness of other drivers. The simulation results support the effectiveness, driving comfort, and safety of the proposed method.

Event-Driven Energy-Efficient Driving Control in Urban Traffic for Connected Electric Vehicles

The traffic light in urban areas dominates the traffic flow, resulting in variation of energy consumption of the vehicles involved. To mitigate the impact of traffic bias on the energy efficiency of electric vehicles (EVs), this article proposes an event-driven energy-efficient driving control (EEDC) strategy based on a receding horizon two-stage control framework, which harnesses the Internet of Vehicles to incorporate the traffic light and preceding vehicle for adaption of different driving scenarios. At the core of the first stage are the vehicle driving event classification rules, which classified the urban traffic scenarios into four events. This article contributes to empirical solutions on the design of the traffic scenario classifier considering conflict goals, including driving efficiency and safety. In the second stage, the speed trajectory in each driving event is optimized using Pontryagin’s minimum principle to reduce vehicle energy consumption. A real-time solution for the energy-efficient driving problem is derived with the consideration of vehicle dynamics, control input, and speed limit constraints. Finally, extensive simulations and road tests are conducted to evaluate the effectiveness of the EEDC. The results show that the EEDC is excellent in energy efficiency improvement over two benchmark strategies in different traffic scenarios while satisfying the constraints in inter-vehicle driving safety and travel time. Moreover, the road tests demonstrate that the EEDC is capable of energy saving in real-world driving.

Design and Experiments of a Two-Stage Fuzzy Controller for the Off-Center Steer-by-Wire System of an Agricultural Mobile Robot

This paper focuses on the steering motion control of an in-wheel motor-drive robot. The influence of the pulse-width modulation (PWM) duty cycle on steering motion and the steering control method have not yet been proved. Thus, this study aimed to design a steering controller for the off-center steer-by-wire system of a robot. The influence of the PWM duty cycle on the steering motion under different conditions is firstly tested on a test bench. Based on the optimal duty cycles of different cases found in the test, a two-stage fuzzy controller of the duty cycle is designed for the steering system. The first stage of the controller is used to dynamically adjust the PWM duty cycle of the electromagnetic friction lock (EFL). The second stage is designed to realize the self-tuning of the fuzzy controller’s quantization factor and the scale factor. Through two-stage control, the motion of the in-wheel motor and the EFL can be coordinated to realize stable and rapid steering. Considering the robots’ primary application in field roads at present, road tests were ultimately conducted to verify the proposed method. The test results show that the angle response rate of the steering arm is elevated with the increase in the steering angle signal. The proposed controller can sensitively track the target angles with smaller overshoot, yaw rate and lateral acceleration, and better steering accuracy than the PID (proportional–integral–differential) controller under different working conditions.

Design of A Two-Stage Control Strategy of Vanadium Redox Flow Battery Energy Storage Systems for Grid Application

The low energy conversion efficiency of the vanadium redox flow battery (VRB) system poses a challenge to its practical applications in grid systems. The low efficiency is mainly due to the considerable overpotentials and parasitic losses in the VRB cells when supplying highly dynamic charging and discharging power for grid regulation. Apart from material and structural advancements, improvements in operating strategies are equally essential for achieving the expected high-performance VRB system, although an optimized solution has not been fully exploited in the existing studies. In this paper, a two-stage control strategy is thus developed based on a proposed and experimental validated multi-physics multi-time-scale electro-thermo-hydraulic VRB model. Specifically, in the first stage, the optimal flow rate of the VRB is obtained based on online optimization to reduce parasitic loss and enhance instantaneous system efficiency, and the result serves as the set point of a feedback flow rate controller. In the second stage, dual time scales are specifically considered. And the current and flow rate controllers are designed to meet the highly varying power demands for grid-connected applications. The effectiveness of the proposed control strategy is verified under a scenario to smooth wind power generation. Comparative studies show that compared to the prevailing approaches, higher efficiency can be achieved in tracking the theoretical optimal power profiles for online battery control.

A Hybrid Two-Stage Control Solution for Six-Phase PMSM Motor With Improved Performance

This article proposes a hybrid two-stage control solution for a six-phase permanent magnet synchronous machine (PMSM) motor with enhanced control performance. The original direct torque control (DTC) and model predictive control (MPC) are artfully integrated together to alleviate the computational burden and the harmonic currents. The proposed method is implemented in two steps. First, the original DTC method is adopted to determine the orientation vector from six existing vectors. It is worthy to mention that the orientation vector is different from the commonly used reference vector calculated through the deadbeat control theory. Second, the prediction vectors located in the same sector with the orientation vector will be evaluated in the original MPC manner. In this way, the enumeration of all the prediction vectors is avoided and the computational burden is avoided. In addition, discrete duty ratios are assigned to define other two groups of virtual vectors to enhance the current quality. In this way, high-performance control of a six-phase motor can be achieved by simply combining the standard DTC and MPC control structure. Experimental results have been used to assess the goodness of the proposed method.

Impact of Spotted Hyena Optimized Cascade Controller in Load Frequency Control of Wave-Solar-Double Compensated Capacitive Energy Storage Based Interconnected Power System

The concept of automatic generation control has an immense role in providing quality power in an interconnected system. To obtain quality power by controlling the oscillations of frequency and tie-line power, a proper controller design is necessary. So, an innovative endeavor has been undertaken to enforce a two-stage controller with the amalgamation of a proportional-derivative with filter (PDN) (integer-order) and a fractional order integral-derivative (FOID), i.e., PDN(FOID). In an effort to acquire the controller’s gains and parameters, a bio-inspired meta-heuristic spotted hyena optimizer is applied. Various examinations manifest the excellence of PDN(FOID) over other controllers such as integral, proportional–integral, proportional–integral-derivative filter, and fractional order PID from perspectives concerning the diminished amount of peak anomaly oscillations, and the instant of settling for a three-area system. The system includes thermal–bio-diesel in area-1; a thermal–geothermal power plant in area-2; and a thermal–split-shaft gas turbine in area-3. It is also observed that the presence of renewable sources such as wave power plants and photovoltaics makes the system significantly better compared to the base system, when assessed individually or both together. Action in a combination of capacitive energy storage with duple compensation is also examined using the PDN(FOID) controller, which provides a noteworthy outcome in dynamic performance. Moreover, PDN(FOID) parameter values at a nominal condition are appropriate for the random patterns of disturbance needed for optimization.

Analysis and Simulation of Overmodulation Modes of a Three-Inverter Block of the Photovoltaic Installation

The purpose of this work is to modernize the control scheme of a transformer-based photovoltaic system with three inverters controlled by the modified algorithms of synchronous spacevector PWM in order to ensure linear regulation of the system in the overmodulation zone of the inverter block in the process of forming a symmetrical and synchronized (with the operating frequency of the system) voltage on the inverter-side windings of a power transformer. This goal is achieved by the fact that the process of two-stage control of the system in the zone of overmodulation of inverters is carried out on the basis of synchronized phase shifts between the control signals of inverters (a constant phase shift, as well as an additional adjustable phase shift between the inverter signals as a function of the duration of clock subintervals), with an appropriate modification of the algorithms of synchronous PWM of inverters due to the inclusion of two specialized correction factors in the basic functional dependencies. The most significant results of the work include the fact that in a system with a modified control scheme and modulation of the inverter block in the overmodulation zone, the resulting voltage on the inverter-side windings of the power transformer is characterized by quarterwave symmetry, and its spectrum contains no even-order harmonics, as well as subharmonics, including regimes of fluctuations in the operating frequency of the system connected to a three-phase network, thereby helping to reduce losses in the transformer windings and improve the efficiency of photovoltaic systems.

Two-stage control for transfer synchronisation and regularity of subsequent bus line service

Transfer synchronisation aims at reducing the waiting time of transferring passengers to achieve a seamless transfer. Taking control at the operation level is widely regarded as an effective method to make real-time response to transfer synchronisation. However, the irregularity of bus line service after transfer may be caused due to the real-time control before the transfer behaviour. In this paper, we propose a two-stage speed control strategy to improve the transfer efficiency and the regularity of bus service after transfer simultaneously. The influence of speed control is estimated by constructing dynamic rolling horizon. Considering the service performances of both the transfer node and the next stop, we develop a multi-objective optimisation model. Experimental results show that compared with the one-stage control, the two-stage control can significantly reduce the deviation of headway after transfer while reducing the transfer time.

Voting, contagion and the trade-off between public health and political rights: Quasi-experimental evidence from the Italian 2020 polls

Natural disasters raise challenging trade-offs between public health safety and inalienable rights like the active involvement in political choices through voting. We exploit a quasi-experimental setting provided by multiple ballots across regions and municipalities during the Italian 2020 elections to estimate the effect of voters’ turnout on the spread of COVID-19. By employing an event-study design with a two-stage Control Function strategy, we find that post-poll new COVID infections increased by an average of 1.1% for each additional percentage point of turnout. Based on these estimates and real political events, we also show through a simulation that in-person voting during a high-infection regime may have a large impact on public health outcomes, more than doubling new infections, deaths and hospitalizations. These findings suggest that policy-makers’ responses to natural disasters should be flexible and contingent to the emergency severity, in order to minimize social costs for citizens.

Discussion on Working Principle and Fault Treatment of Two-stage Control Valve for Train Loading

Discussion on Working Principle and Fault Treatment of Two-stage Control Valve for Train Loading

Dynamic Changes in Microbial Communities and Physicochemical Characteristics During Fermentation of Non-post Fermented Shuidouchi.

Non-post fermented Shuidouchi is a Chinese spontaneously fermented soybean food with multifunctionality in human health. The functionality and safety of this plant-based food will be affected by the microorganisms during fermentation. In this study, microbial diversity was investigated using culture-dependent and culture-independent methods. The functional metabolites such as polyamines and alkylpyrazines were also determined at different time points during fermentation. We found that Bacillus was the most dominant microbe throughout the fermentation process, while the temperature was the most important influencing factor. During fermentation, the microbial diversity increased at a moderate temperature and decreased at a high temperature (52°C). High temperature caused the prosperity of the spore-producing bacteria such as Bacillus (more than 90% relative abundance in bacteria) and Aneurinibacillus (2% or so relative abundance in bacteria), and the inhibition of fungi. Furthermore, it was found by correlation analysis that the relative abundances of Bacillus and Aneurinibacillus were positively correlated with the relative content of amino acid metabolism pathway and the content of most alkylpyrazines and biogenic amines. Meanwhile, the relative abundances of many non-dominant bacteria were negatively correlated with the content of biogenic amines and positively correlated with the relative content of carbohydrate metabolism pathway. These effects were helpful to control the biogenic amine contents under the safety limits, increasing the alkylpyrazine type and product functionality. A two-stage temperature control strategy—a moderate temperature (35–42°C) first, then a high temperature (52°C)—was concluded from the spontaneous fermentation of non-post fermented Shuidouchi. This strategy could improve the safety of product by inhibiting or sterilizing the thermolabile microbes. The non-post fermented Shuidouchi product is rich in functional compounds such as polyamines and alkylpyrazines.

Energy-Based Analysis of String Stability in Vehicle Platoons

String stability is a vital property of vehicle platoons which ensures that the system states are not amplified downstream along the string of vehicles. In this paper, a passivity-based approach to stability analysis of vehicle platoons is presented. This is motivated by a case study of an adaptive cruise-controlled platoon. We link the string stability of the platoon to the output strict passivity of each subsystem (vehicle) in the string. We show that a sufficient condition for the platoon to be string stable is that the output strict passivity index of each vehicle in the string be lower bounded by one. For a linear time-invariant (LTI) system, this means the Nyquist plot of the transfer function of each subsystem is within the unit circle. Modeling the platoon as a cascade of LTI subsystems, we present a visual tool to assess string stability. By constraining the output strict passivity index of each subsystem in a string to unity through a two-stage control strategy, the condition for string stability is met. The proposed string stability control strategy, accounting for uncertainties and delays in the drive and sensory systems, is validated using numerical simulations.

Intelligent Control Method for Loss Distribution Balance of High-Power Photovoltaic Grid-Connected Inverters.

Aiming at the problem that the loss distribution balance control effect of high-power photovoltaic grid-connected inverter is poor due to the complex loss factors, this paper proposes a loss distribution analysis and balance intelligent control method for high-power photovoltaic grid-connected inverter. The topology structure of high-power photovoltaic grid-connected inverter is constructed and the overall control scheme is designed. The loss of inductance, resonant frequency, harmonic attenuation, and damping resistance in the circuit of PV grid-connected inverter is analyzed, respectively. On this basis, a two-stage loss control model of high-power PV grid-connected inverter is constructed, and the generalized linear decision rule is adopted to schedule PV power supply and fluctuating load. A two-stage loss control model for high-power photovoltaic grid-connected inverter was established and the optimal loss control value was obtained. Experimental results show that the proposed method can accurately suppress voltage and current losses of grid-connected photovoltaic inverters and has strong resonance resistance and robustness.

Energy Management Strategy of a Reconfigurable Grid-Tied Hybrid AC/DC Microgrid for Commercial Building Applications

This paper proposes an energy management strategy (EMS) of a reconfigurable grid-tied hybrid ac/dc microgrid (HMG) architecture for commercial building (CB) applications. This HMG architecture consists of a multi-mode configuration (MMC) with renewable-based distributed generators (DGs), energy storages (ESs), and genset. The main advantage of this architecture is its capability to reconfigure its structure based on the predicted building load power (BLP) and renewable-based DGs power. However, minimizing the building electricity cost (BEC) and maximizing the reliability index (RI) of a reconfigurable grid-tied HMG architecture is a challenging task due to the stochastic behaviors of BLP and renewable-based DGs power. In this context, the BLP and renewable-based DGs behaviors are modeled using an artificial neural network approach to predict future time slot values. Then, system level models are developed for HMG energy sources and grid-tied converters/switches. Furthermore, BEC and RI models are developed based on the dynamic pricing of grid/HMG electricity cost and supplied energy. Then, an EMS is proposed for the developed reconfigurable grid-tied HMG architecture, which consists of a two-stage control strategy, i.e., stage I and stage II. Stage I control strategy minimizes BEC and maximizes RI using multi-objective particle swarm optimization (MOPSO) and MMC. Stage II control strategy generates control signals for HMG energy sources, converters, and grid-tied converters/switches based on stage I reference signals. Historical data is used to demonstrate the effectiveness of the proposed EMS for a reconfigurable grid-tied HMG architecture. Through numerical simulation studies, it is shown that the proposed EMS is capable of reducing BEC and increasing RI by concurrently enabling MMC of a reconfigurable grid-tied HMG architecture.

On Distribution and Average Run Length of a Two-Stage Control Process

On Distribution and Average Run Length of a Two-Stage Control Process

A novel dynamic two-stage controller of battery energy storage system for maximum demand reductions

Demand response with battery energy storage systems (BESS) provides the most flexible peak reduction solution for different markets. One of the major challenges is the optimization of the demand threshold that controls the charging and discharging powers of BESS. To increase its tolerance to day-ahead prediction errors, state-of-art controllers utilize rigid parameters that are determined from long-term historical data. However, long-term historical data may be unavailable at implementation, and rigid parameters cause them unable to adapt to evolving load patterns. To tackle this issue, this article proposes a novel dynamic two-stage maximum demand reduction controller using BESS that incorporates 1-h-ahead load profiles to refine the threshold found based on day-ahead load profile and prevent peak reduction failure if necessary. The dynamic controller needs no rigid parameters and can begin its daily peak reduction with just 30 days of historical data. Compared to the conventional fixed threshold, single-stage, and fuzzy controllers, the proposed two-stage controller achieves up to 6.82% and 306.23% higher in average maximum demand reduction and total maximum demand charge savings, respectively, on two different datasets. The proposed controller also achieves a 0% peak demand reduction failure rate in both datasets, demonstrating its peak demand reduction prevention capability.

Predictive energy-efficient driving strategy design of connected electric vehicle among multiple signalized intersections

Signalized intersections dominate traffic flow in urban areas, resulting in increased energy consumption and travel delay for the vehicles involved. To mitigate the negative effect of traffic lights on eco-driving control of electric vehicles, a multi-intersections-based eco-approach and departure strategy (M-EAD) is proposed to improve vehicle energy efficiency, traffic throughput, and battery life, while maintaining acceptable driving comfort. M-EAD is a two-stage control scheme that includes efficient green signal window planning and speed trajectory optimization. In the upper stage, the traffic light green signal window planning is formulated as a shortest path problem, which is solved using an A* algorithm for travel delay reduction. In the lower stage, the speed optimization problem is solved by resorting to a receding horizon framework, in which the energy consumption and battery life losses are minimized using an iterative dynamic programming algorithm. Finally, Monte Carlo simulation with randomized traffic signal parameters is conducted to evaluate the performance of the proposed M-EAD strategy. The results show the various advancements of the proposed M-EAD strategy over two benchmark methods, constant speed and isolated-intersection-based eco-approach and departure strategies in terms of energy efficiency, travel time, and battery life in stochastic traffic scenarios. In addition, the performance of M-EAD on actual road conditions is validated by on-road vehicle test.

Cascade adaptive optics: contrast performance analysis of a two-stage controller by numerical simulations

The contrast performance of current extreme adaptive optics (XAO) systems can be improved by adding a second AO correction stage featuring its own wavefront sensor (WFS), deformable mirror (DM), and real-time controller. We develop a dynamical model for such a cascade adaptive optics (CAO) system with two stages each controlled by a standard integrator and study its control properties. We study how such a configuration can improve an existing system without modifying the first stage. We analyze the CAO architecture in general and show how part of the disturbance is transferred from low to high temporal frequencies with a nefarious effect of the second stage integrator overshoot and suggest possible ways to mitigate this. We also carry out numerical simulations of the particular case of a first stage AO using a Shack–Hartmann WFS and a second stage AO with a smaller DM running at a higher framerate to reduce temporal error. In this case, we demonstrate that the second stage improves imaging contrast by one order of magnitude and shortens the decorrelation time of atmospheric turbulence speckles by even a greater factor. The results show that CAO presents a promising and relatively simple way to upgrade some existing XAO systems and achieve improved imaging contrasts fostering a large number of science case including the direct imaging of exoplanets.

Efficiency and Reliability Analysis of Self-Adaptive Two-Stage Fuzzy Control System in Complex Traffic Environment

In this paper, a self-adaptive, two-stage fuzzy controller is established to realize the real-time online optimization of traffic signal timing plan, which takes multimodels of transportation as the research object to analyze the reliability of the control system at the isolated urban intersection. In this system, the first stage calculates traffic urgency degree for all red phases and selects the red phase with maximum traffic urgency degree as the next green phase. The second stage determines whether to extend or terminate the current signal phase. Aiming at the problems of the parameters of membership functions empirical settings and insufficient response to the real-time fluctuation in traffic flow, the controller introduces an improved hybrid genetic algorithm to solve it and enable the controller to self-learn. Finally, a microsimulation platform is constructed based on the VISSIM and Python language to evaluate the efficiency and reliability of the controller under complex actual traffic conditions. Results showed that the average delay time per vehicle is reduced by 14.59%, while the average number of stops per vehicle is reduced by 0.71% compared with the traditional control method. Results indicate that the traffic signal timing plan generated by the controller can efficiently improve the intersection traffic capacity and has good efficiency and reliability under the condition of medium saturation and unsteady flow.

Pre-Event Two-Stage Proactive Control for Enhanced Distribution System Resiliency

Recent advancements in prediction technologies have motivated the power distribution utilities to actively utilize forecasts to minimize the impact of high-impact low frequency (HILF) events. Accurate forecasting coupled with the past operational experiences potentially enables pre-event control, aiming to minimize the impact on the system in a network with finitely large numbers of automatic and manually operated control devices. Impacts to the critical loads (CLs) are minimized when the network is appropriately reconfigured before the event strikes. However, predictions are not perfectly accurate, and given limited accuracy with ever-changing weather forecasts, control actions are required to be constantly updated until the event has passed through. Once the events are predicted with high enough confidence and the necessary reconfiguration strategy has been identified, the manually operable switches must be configured significantly ahead of event landfall to ensure the operational crews’ safe return from potentially hazardous zones. Given limited resource availability and requisite manual switching operations, some loads would also have to be deenergized, which could be alleviated if those loads remain connected through remotely operable switches until minutes before the event makes the landfall. The proposed two-stage control framework with a necessary mathematical formulation facilitates the same. Furthermore, the proposed framework facilitates continuous corrective action based on available lead time. The developed proactive control framework is demonstrated using a modified IEEE 123-Node system model and a real-world isolated μ-grid based on the Cordova Electric Cooperative system with superior performance results.