Real-time Control Algorithm Research Articles

A Forecasting-Based Control Algorithm for Improving Energy Managment in High Concentrator Photovoltaic Power Plant Integrated with Energy Storage Systems

The High Concentrator Photovoltaic (HCPV) technology, due to its high efficiency, is considered one of the most promising solutions for the exploitation of sun-irradiation-based Renewable Energy Sources (RES). Nevertheless, the HCPV production is strictly connected to the Direct Normal Irradiation (DNI) making this photovoltaic technology more sensible to cloudiness than traditional ones. In order to mitigate the power intermittence and improve production programmability, the integration between Energy Storage Systems (ESSs) and HCPV, resorting to forecasting algorithms, has been investigated. Specifically, a local weather forecasting algorithm has been used for estimating the daily time evolution of DNI, air Temperature (T), Wind Speed (WS), and Air Mass (AM). These data are subsequently processed by means of an accurate HCPV model for the estimation of one day-ahead daily power production profile. The processing of HCPV forecasted generation by means of a properly tuned filter-based algorithm allows one day-ahead the definition of power profiles of ESS and power plant respectively, considering also the ESS constraints and the characteristic of the implemented real-time control algorithm. The effectiveness of the proposed forecasting model and control algorithm is verified through a simulation study referring to the solar power plant constituted by HCPV and ESS installed in Ottana, Italy. The results highlight that the application of the proposed approach lessens the power fluctuation effect caused by HCPV generation preserving the batteries at the same time. The feasibility and advantages of the proposed approach are finally presented.

Research and Ground Verification of the Force Compliance Control Method for Space Station Manipulator

The space station manipulator does lots of tasks with contact force/torque on orbit. To ensure the safety of the space station and the manipulator, the contact force/torque of manipulator must be controlled. Based on analyzing typical tasks’ working flows and force control requirements, such as ORU (orbit replacement unit) changeout and dual arm collaborative payload transport, an impedance control method based on wrist 6 axis force/torque feedback is designed. For engineering implementation of the impedance control algorithm, the discretization method and impedance control parameters selection principle are also studied. To verify the compliance control algorithm, a ground experiment platform adopting industrial manipulators is developed. In order to eliminate the influence of gravity, a real-time gravity compensation algorithm is proposed. Then, the correctness of real-time gravity compensation and force compliance control algorithm is verified on the experiment platform. Finally, the ORU replacement and dual arm collaborative payload transport experiments are done. Experimental results show that the force compliance control method proposed in this paper can control the contact force and torque at the end of the manipulator when executing typical tasks.

Parallel MPC for Linear Systems With Input Constraints

This article is about a real-time model predictive control algorithm for large-scale, structured linear systems with polytopic control constraints. The proposed controller receives the current state measurement as an input, and computes a suboptimal control reaction by evaluating a finite number of piecewise affine functions that correspond to the explicit solution maps of small-scale parametric quadratic programming (QP) problems. We provide asymptotic stability guarantees, which can be verified offline. The feedback controller is computing approximations of the optimal input, because we are enforcing real-time requirements assuming that it is not possible to solve the given large-scale QP in the given amount of time. Here, a key contribution of this article is that we provide a bound on the suboptimality of the controller. The approach is illustrated by benchmark case studies.

Damping Mechanisms in Cable-Harnessed Structures for Space Applications: Analytical Modeling

Abstract Recent developments in the aerospace industry have driven focus toward accurately modeling the effects of the cables and electronic cords on space structures. In the past, researchers have modeled the mass and stiffness effects of these cables but primarily overlooked their damping effects through careful analytical model developments. The objective of the current work is to present analytical models for cable-harnessed structures that also include the damping effects in their vibration response. Obtaining simple, low-order and high-fidelity models are highly advantageous in designing robust vibration real-time control algorithms for structures. Additionally, the analytical models are useful tools in providing insight into and better understanding of the dynamics of space structures as they are often difficult to be tested prior to launch due to their large size and at best only a few components may be tested. Motivated by the space applications, this work considers beam structures wrapped with cables which are modeled using beam and string theory assumptions. Two different damping models namely Kelvin–Voigt and hysteretic damping are considered. The homogenization approach is used as a starting point for structures of periodic wrapping patterns. Using the variational principle, the governing partial differential equation for the transverse coordinate of vibrations is found for three cable patterns and the results are compared to those from the distributed transfer function method (DTFM). Finally, the effects of several structural parameters are studied on the overall system damping.

Real-Time Control of Plug-in Electric Vehicles for Congestion Management of Radial LV Networks: A Comparison of Implementations

The global proliferation of plug-in electric vehicles (PEVs) poses a major challenge for current and future distribution systems. If uncoordinated, their charging process may cause congestion on both network transformers and feeders, resulting in overheating, deterioration, protection triggering and eventual risk of failure, seriously compromising the stability and reliability of the grid. To mitigate such impacts and increase their hosting capacity in radial distribution systems, the present study compares the levels of effectiveness and performances of three alternative centralized thermal management formulations for a real-time agent-based charge control algorithm that aims to minimize the total impact upon car owners. A linear formulation and a convex formulation of the optimization problem are presented and solved respectively by means of integer linear programming and a genetic algorithm. The obtained results are then compared, in terms of their total impact on the end-users and overall performance, with those of the current heuristic implementation of the algorithm. All implementations were tested using a simulation environment considering multiple vehicle penetration and base load levels, and equipment modeled after commercially available charging stations and vehicles. Results show how faster resolution times are achieved by the heuristic implementation, but no significant differences between formulations exist in terms of network management and end-user impact. Every vehicle reached its maximum charge level while all thermal impacts were mitigated for all considered scenarios. The most demanding scenario showcased over a 30% reduction in the peak load for all thermal variants.

Automated Vehicle Control at Freeway Lane-drops: a Deep Reinforcement Learning Approach

This study develops an optimal, real-time and adaptive control algorithm for helping a Connected and Automated Vehicle (CAV), navigate a freeway lane-drop site (e.g. work zones). The proposed traffic control strategy is based on the Deep Q-Network (DQN) Reinforcement Learning (RL) algorithm, and is designed to determine the driving speed and lane-change maneuvers that would enable the CAV to go through the bottleneck, with the least amount of delay. The DQN RL agent was trained using the microscopic traffic simulator VISSIM, where the learning focused on how the CAV may be able to optimally maneuver the lane drop site while driving as close as possible to the freeway speed limit. VISSIM was also used to compare the performance of the DQN-controlled AV, as opposed to a human-driven vehicle with no intelligent control, in terms of the driving speed or travel time needed to traverse the lane drop site, under a congested, real life-like traffic scenario. The research findings demonstrate the promise of DQN RL in allowing the CAV to intelligently, and optimally navigate, through the lane drop site. Specifically, for the scenario for which the agent was trained, the reduction in the CAV travel time was around 96 percent, compared to the base case. The robustness of the RL agent was further tested on various scenarios different from the training case. For those cases, the mean and standard deviation of the reductions in the travel of the DQN-controlled CAV travel times, compared to the base case, were around 31% and 61%, respectively.

Novel Implementation of Multiple Automated Ground Vehicles Traffic Real Time Control Algorithm for Warehouse Operations: Djikstra Approach

Novel Implementation of Multiple Automated Ground Vehicles Traffic Real Time Control Algorithm for Warehouse Operations: Djikstra Approach

Design of Ship Visual Communication Teleoperation System Based on Virtual Reality

Ji, Y., 2020. Design of ship visual communication teleoperation system based on virtual reality. In: Yang, Y.; Mi, C.; Zhao, L., and Lam, S. (eds.), Global Topics and New Trends in Coastal Research: Port, Coastal and Ocean Engineering. Journal of Coastal Research, Special Issue No. 103, pp. 975–978. Coconut Creek (Florida), ISSN 0749-0208.In view of slow response, small number of users' access and inaccurate anomaly warning of traditional ship remote operation system, this paper designs a ship visual communication remote operation system based on virtual reality. Ceator software is used to build the three-dimensional model of the ship control system. Real-time operation control algorithm is adopted to drive the control program, introducing Vega to develop the ship visual communication remote operation system. Eclipse is used to build the system operation environment. The proposed ship visual communication remote operation system has faster response, larger number of users access, and more accurate anomaly warning is, which makes up for the shortcomings of the traditional system.

Reduce Bus Bunching with a Real-Time Speed Control Algorithm Considering Heterogeneous Roadway Conditions and Intersection Delays

AbstractThis paper investigates bus bunching issues encountered with a single bus line. A real-time speed control model was proposed with the objective of minimizing variations in bus headway. Thre...

DC Microgrid System Modeling and Simulation Based on a Specific Algorithm for Grid-Connected and Islanded Modes with Real-Time Demand-Side Management Optimization

This paper presents an algorithm considering both power control and power management for a full direct current (DC) microgrid, which combines grid-connected and islanded operational modes, with real-time demand-side management optimization. The full microgrid is a hybrid dynamic system model consisting of two interacting parts: continuous-time dynamics and discrete-event dynamics. Such a full microgrid consists of photovoltaic sources, a DC load, battery storage systems, supercapacitor storage, a diesel generator, and a public grid connection, all connected on a DC common bus. This full microgrid is more reliable than a microgrid with only renewable sources or with only traditional energy sources, considering the power constraints imposed by the public grid as well as the sluggish dynamic of the diesel generator, self-discharging characteristic of the supercapacitor, and load shedding optimization. Meanwhile, this algorithm can automatically switch between grid-connected and islanded operational modes to optimize the power of the load shedding, take advantage of renewable energy, and keep the power balance in the full DC microgrid. The results under MATLAB/Simulink verify that the real-time control algorithm can maintain the power balance in real-time for the whole day and satisfy the power management strategy.

Real-Time Optimal Trajectory Generation and Control of a Multi-Rotor With a Suspended Load for Obstacle Avoidance

This letter presents real-time optimization algorithms on trajectory generation and control for a multi-rotor with a suspended load. Since the load is suspended through a cable without any actuator, movement of the load must be controlled via maneuvers of the multi-rotor, which brings about difficulties in operating this system. Additionally, the highly nonlinear dynamics of the system exacerbates the difficulties. While trajectory generation and control are essential for safety, energy efficiency, and stability, the aforementioned characteristics of the system add challenges. With this in mind, the authors propose real-time path planning and optimal control algorithms for collision-free trajectory generation and trajectory tracking. For the dynamics, simplified dynamic models of the system are proposed by considering time delay in attitude control of the multi-rotor. For collision avoidance, the vehicle, cable, and load are considered as ellipsoids with different sizes and shapes, and collision-free constraints are expressed in an efficient and nonconservative way. The augmented Lagrangian method is applied to solve the nonlinear optimization problem with the nonlinear constraints in real-time. For control of the system, model predictive control with a sequential linear quadratic solver is used. Several simulations and experiments are conducted to validate the proposed algorithm.

A compact neuromorphic architecture with dynamic routing to efficiently simulate the FXECAP-L algorithm for real-time active noise control

In this work, we introduce, for the first time, the design of a compact neuromorphic architecture to efficiently support a filtered-x error-coded affine projection-like (FXECAP-L) algorithm that is based on affine projection (AP) algorithms for active noise cancellation (ANC) in an acoustic duct. To date, few practical ANC implementations have used AP algorithms because of their high computational complexity, despite providing fast convergence speeds. One of the main factors that increases their computational complexity is linked to the dimensions of the matrix used in the AP algorithm’s computations. Evidently, the largest dimensions of the matrix increase the convergence speed of the AP algorithms by paying a penalty in terms of area consumption. However, convergence speed is crucial in ANC applications since this factor determines the speed at which the noise is canceled. Recently, an FXECAP-L algorithm with evolving order has been proposed to dynamically reduce the dimensions of the matrix by maintaining the convergence speed of AP algorithms. Here, we propose a compact neuromorphic architecture with a dynamic routing mechanism to efficiently implement the evolutionary method of the FXECAP-L algorithm by creating a virtual matrix, whose dimensions can be modified over the filter processing. In this way, we avoid spending a large amount of memory to save the largest matrix elements. In addition, the inclusion of the dynamic routing mechanism in the proposed neuromorphic architecture has allowed us to guarantee low area consumption since the neuromorphic architecture is capable of simulating different adaptive structures without modifying its structure. Here, the neuromorphic architecture has been configured as the system identification and ANC controller for practical noise cancellation in an acoustic duct. Our results have demonstrated that the combination of the properties of the FXECAP-L algorithm and the implementation techniques generate a versatile signal processing development tool that can be used in practical real-time ANC applications.

Stability and Robustness of Real-Time Pressure Control in Water Distribution Systems

AbstractThis paper deals with the fundamental requirement of stability of real-time control algorithms for water distribution systems. Loss of stability may in fact generate strong pressure waves t...

Onboard reasoning and other applications of the logic-based approach to the moving objects intelligent control

This article provides the theoretical background and practical case studies of the application of reasoning and other logic-based approaches to the moving objects control. Modern moving objects, both manned and unmanned, utilise computers as their 'onboard brain'. Since planes, spacecraft, cars, trucks and trains must demonstrate flexible and safe behaviour in various situations, it seems prospective to use intelligent control means instead of rigid control logic dispersed in a program source code. This article is concerned with the possible implementation of onboard intelligence. In contrast to the popular use of neural networks, the logic-based approach is based on clear and exact control rules with strict responsibility. Thus, formal specification and verification methods can be utilised. The article describes the real-time control algorithm logic (RTCAL) for the above-mentioned purposes. We also present case studies of reasoning at the design and operation stages for providing the fault tolerant control of a spacecraft.

A heuristic method to optimize generic signal phasing and timing plans at signalized intersections using Connected Vehicle technology

This paper develops a flexible, real-time traffic signal control algorithm to optimize both phase durations and phase sequences at four-approach intersections with conflicting left-turns, based on information obtained from connected vehicles. Vehicle-to-infrastructure communications are assumed to provide the location of all connected vehicles near the signalized intersection at regular time intervals and this information is used to identify the presence of non-connected vehicles that are stopped at the intersection. All detected and identified vehicles are used to identify naturally occurring platoons in the traffic stream. The signal control algorithm then selects the optimal sequence that these platoons should discharge through the intersection to minimize average delay of all identified vehicles. Since all possible departure sequences for platoons of vehicles are considered, the problem is computationally difficult. Hence, several heuristic methods are proposed to determine optimal platoon departure sequences. These heuristics include an intelligent tree search and multiple types of genetic algorithms, including a newly developed genetic algorithm that perserves phase order sequence that is vital to this problem. Comparisons between these heuristics and the global optimal solution suggest that the heuristics are able to provide similar operational performance with significant reductions in total computation time required, such that the algorithm can be applied in real-time. In general, the intelligent tree search appears to outperform the genetic algorithm approaches in terms of operational performance but has computational requirements that increase exponentially with the number of platoons identified at the intersection. Meanwhile, the genetic algorithm methods tend to be more scalable but slightly less efficient. Overall, the results are promising for the application of the proposed flexible signal control algorithm at real intersections.

Layered stochastic approach for residential demand response based on real‐time pricing and incentive mechanism

This paper proposes a layered stochastic optimization approach for residential demand response (DR) under real-time pricing (RTP) and an incentive-based mechanism, which contains three steps. In the first layer, an independent system operator (ISO) announces day-ahead RTP to a residential load aggregator (RLA). The RLA predicts individual household loads (step 1) and aggregates the loads to minimize electrical cost (step 2). In the second layer, the RLA announces incentives to homes, and home energy management systems (EMS) control the loads to maximize the reward in real-time (step 3). In Step 1, probability based individual load prediction models are developed. In Step 2, a stochastic optimization model is developed to aggregate controllable loads of residential consumers. In Step 3, an incentive-based mechanism is proposed, based on which, a real-time load control model for individual homes is developed to benefit the RLA and homeowners. A highly efficient real-time control algorithm for home EMS is developed. The case studies show that, with 10% controllable energy integration, the peak demand is reduced by 17.5% and the energy cost of the controllable loads is reduced by 28%. The proposed mechanism can effectively aggregate many individual residential controllable loads to participate in an electricity market.

Real-Time Control Algorithm for Enhancing Operation of Network of Stormwater Management Facilities

AbstractThe deployment of real-time control (RTC) technology has improved the performance of stormwater management facilities. However, most of the current research focuses on individual pilot site...

GoRoSoBo simplified: an accurate feedback control algorithm in real time for irrigation canals

AbstractAn irrigation canal is a hydraulic system whose main objective is to convey water from a source (dam, river) to different users. Such systems can be very large (several tens or hundreds of kilometers), characterized by time delays and non-linear dynamics, strong unknown perturbations and interactions among subsystems. In order to fulfill the requirements of canal users, the water manager must control all water deliveries during the irrigation cycle (or irrigation program) calculating the gate positions of the canal according to the water demands in real time. Initially, our overall control diagram in real time is mainly represented by two algorithms, the canal survey estimation algorithm (this algorithm estimates the water level and velocity along the irrigation canal during a past time horizon) and GoRoSoBo algorithm (feedback control algorithm operating in real time). Regarding long canals with several gates and pumps operating in a short period of time for a long predictive horizon, the initial version of GoRoSoBo algorithms would spend too much time calculating the canal gate position in real time. This is the reason why we have upgraded the code of the GoRoSoBo algorithm, saving in computational time around 85%, in order to operate in real time.

Design of Optimal Power Point Tracking Controller Using Forecasted Photovoltaic Power and Demand

With the advent of grid-connected photovoltaic systems for energy generation, new technologies must be created that maintain a continuous and stable balance between supply and demand of generated electricity. Consequently, accurate prediction of solar energy generation and consumption is required. Solar energy generation and electric power demand are both stochastic and nonstationary in nature and often incongruous. The imbalance between demand and supply can be costly and leads to long-term ineffectiveness of power generation and distribution. The aim of this work is to propose methods for maintaining demand-supply balance in PV power generation and distribution systems. To achieve this, we build and combine three different tools: 1) a predictive model for forecasting solar energy generation, 2) a predictive model for demand prediction, and 3) a real-time control algorithm that uses the outputs of prediction models and adjusts the output voltage of PV system to maintain demand-supply balance. Our prediction models are based on time-series forecasting tools and artificial neural networks. The control algorithm is called optimal power point tracking (OPPT) and is based on the perturb and observe algorithm. We evaluate the performance of the combined prediction-controller system using real-world data.

Automatic Control of the Middle Route Project for South-to-North Water Transfer Based on Linear Model Predictive Control Algorithm

The application of automatic control to irrigation canals is an important means of improving the efficiency of water delivery. The Middle Route Project (MRP) for South-to-North Water Transfer, the largest water transfer project in China, is currently under manual control. Given the complexity of the MRP, there is an urgent need to adopt some form of automatic control. This paper describes the application of model predictive control (MPC), a popular real time control algorithm particularly suited to the automatic control of multi-pool irrigation water delivery systems, to the MRP using a linear control model. This control system is tested in part of the MRP by means of numerical simulations. The results show that the control system can deal with both known and unknown disturbances, albeit with a degree of resonance in some short pools. However, it takes a long time for the MRP to reach a stable state under the MPC system and the calculation time for the whole MRP network would be too long to satisfy the requirements of real-time control. Suggestions are presented for the construction of an automatic control system for the MRP.