Real-time Adaptive Control Research Articles

Real-time Electricity Pricing: TOU-MPC Based Energy Management for Commercial Buildings

An adaptive real-time electricity pricing and optimal control system based on energy management of buildings is presented. This is made in the framework of a smart-grid according to energy demand and time-of-use (TOU) electricity tariff in conjunction with a model predictive control (MPC). The developed model is considered as a multiple single input single output MPC system that acts at a different rate of TOU- electricity tariffs (off-peak, standard and peak). A smart selective timer switching system links one to the other. The performance of the model design is expressed through the simulation results of the adaptive TOU-MPC for commercial buildings.

Multi-Time Scale Control of Demand Flexibility in Smart Distribution Networks

This paper presents a multi-timescale control strategy to deploy electric vehicle (EV) demand flexibility for simultaneously providing power balancing, grid congestion management, and economic benefits to participating actors. First, an EV charging problem is investigated from consumer, aggregator, and distribution system operator’s perspectives. A hierarchical control architecture (HCA) comprising scheduling, coordinative, and adaptive layers is then designed to realize their coordinative goal. This is realized by integrating multi-time scale controls that work from a day-ahead scheduling up to real-time adaptive control. The performance of the developed method is investigated with high EV penetration in a typical residential distribution grid. The simulation results demonstrate that HCA efficiently utilizes demand flexibility stemming from EVs to solve grid unbalancing and congestions with simultaneous maximization of economic benefits to the participating actors. This is ensured by enabling EV participation in day-ahead, balancing, and regulation markets. For the given network configuration and pricing structure, HCA ensures the EV owners to get paid up to five times the cost they were paying without control.

ACTIVE HEAT DISSIPATION SYSTEM USING ADAPTIVE RECURRENT WAVELET NEURAL NETWORK CONTROL

This paper proposes a novel cooling control system with the intelligent active technique, which is based on the NI-PXI systems structure combined with the heat pipe cooling chips. In order to further solve the controlling problems of nonlinear heat transfer system, the proposed intelligent system involves the PID control, traditional control, and the adaptive recurrent wavelet neural network controller (ARWNNC) control techniques. The traditional control there exists the undesirable control chattering, The PID control the Response of Control cannot be processed immediately and the input voltage saturation phenomenon, the adaptive recurrent wavelet neural network controller, is employed to approximate the ideal controller, while the corresponding parameters are derived by the gradient steepest descent method, thus being provided with the adaptive real-time control ability.

Adaptive Mechanical Stabilization of a Free-Floating Fully Implantable Hearing Aid

An implantable acousto-mechanical transducer will yield a higher output if its mass can be virtually increased through the use of a secondary actuator. Current hearing aids and implants rely on feedback compensation to prevent instability (e.g. howling), usually in the form of a digital or analogue filter. We examine the effect of mechanically stabilizing a piezo-driven mechanical amplifier inserted into the incudostapedial joint gap. The aim of this study is to determine whether this is possible and discern the advantages and disadvantages of the design. We examine a 10:1 scale model of a prospective implantable hearing aid comprising one piezoelectric sensor and two independent piezoelectric actuators in a single-titanium housing. As expected, the maximum gain of the device is limited by feedback between sensor input and the output of the primary actuator. The secondary actuator is used to provide a counter force to the recoil of the primary output piezo. This adds a virtual mass to the device, effectively reducing feedback in the mechano-acoustic path. The compensation unit (CU) described here is driven by a real-time adaptive control algorithm. Using the above approach, we observe an added stable gain of >30 dB, and report a functional hearing gain of up to 40 dB. Physical and digital feedback compensation can be employed in parallel for best results. The experimental data is backed by computer simulations. These results compare favorably with previous studies of a two-piezo transducer with digital feedback control and show the potential for the transducer as a hearing aid for high-frequency hearing loss.

An adaptive traffic signal coordination optimization method based on vehicle-to-infrastructure communication

The increasing volume of traffic in cities has a significant effect on the road traffic congestions and the time it takes for road users to reach their destinations as well. Adaptive signal control system has shown powerful ability to effectively alleviate urban traffic congestions to achieve desirable objectives (e.g., minimization of delay). The real-time adaptive traffic signal control system can obtain traffic density information by taking advantage of vehicle-to-infrastructure (V2I) communication system to prepare efficient measurement statistics for traffic controllers. In this paper, we present a novel multi-agent based control method for an integrated network of adaptive traffic signal controllers under V2I communication environment which is characterized by the following features: flow model free, area coordinated, co-learning and suitable for parallel processing. Our method has been tested on a large-scale network covering 23 km$$^{2}$$2 with 22 intersections in the downtown area of Xiaogan, China. The results show the significant reductions in the average travel time per vehicle, the average delay per vehicle and the average queue length, when compared to the data in the traditional fixed-time method and actuated method.

A flexible robotic control experiment for teaching nonlinear adaptive control

Teaching a subject on system identification and adaptive control to Master’s students is a challenging task. In view of providing a platform to achieve the teaching-learning on adaptive control effectively, this paper proposes a real-time robotic adaptive control experiment. The robotic system consists of flexible links which is subjected to varied payloads. The link flexure of this robot and other parameters conditions are uncertain thus necessitating an adaptive control to handle the payload variations. A simple regressor-based adaptive control is designed by linear parametric representation of a highly nonlinear and coupled dynamics this robot with flexible links (flexible robot). To reinforce the student’s concept of design, development, and implementation of an adaptive control, experiments were undertaken in real-time for different payloads. Attempts have been made to make the students’ understanding more clear how an adaptive controller performs well for situations with varied parameters which is not possible to obtain by a fixed gain controller.

Robust Energy Management of a Hybrid Wind and Flywheel Energy Storage System Considering Flywheel Power Losses Minimization and Grid-Code Constraints

The maximum power and power ramp rate are important grid codes for integrating renewable energy resources in transmission systems. The power curtailment regulates the maximum power and ramp rate; however, adding an energy storage system (ESS) can time shift surplus wind energy instead of curtailing it. The flywheel energy storage system (FESS) has the advantages of high efficiency and long lifetime; however, it has non-negligible standby losses and its lifetime is reduced exponentially as the rotating speed increases. Considering such practical constraints, this work presents an energy management system (EMS) for a hybrid power system composed of a wind farm with a FESS. The FESS time shifts the surplus wind energy to respect the grid codes and reduce wind curtailment; meanwhile, the EMS aims at minimizing the FESS standby losses and boosting its lifetime using the predicted wind power data. The EMS is composed of two controllers. The first controller is a linear model predictive controller that defines the long-term FESS power set-point. The second controller is a real-time adaptive hysteresis controller that compensates for the wind-power prediction error. Comparative simulation studies and hardware-in-the-loop test results validate the effectiveness of the proposed EMS in reducing the FESS losses while respecting the grid integration code constraints.

Solution for a Five Link Industrial Robot Manipulator Inverse Kinematics Using Intelligent Prediction Response Method

Background: Robot kinematics suggests and interprets the relationship exists between the kinematic position connectivity and acceleration of each link. In any of the robot manipulator the kinematics solution may be forward kinematics or inverse kinematics. To determine the joint values for a provided desired end effector orientation and position, the inverse kinematics principle is applied. Inverse kinematics is the usage of kinematics equations of a robot to find out the joint parameters that gives a targeted position of the end-effector. Methods/Statistical Analysis: In this paper, inverse kinematic solution for a five joint robot involving intelligent prediction response method has been developed and the result will be analyzed based on the performance. The intelligent prediction response method will give the performance based result, which shows the five various angles of an industrial robot. The single variation in the joint angle will be analyzed for every joint angles. This method based inverse kinematics solution is much more useful in real-time adaptive robot control where shorter calculation times are required. Findings: The MATLAB 13.0 is used to find the solution for a set of joint parameters. The actual reading and MATLAB program was found acceptable level. Applications/Improvements: To solution provided for the inverse kinematics problem with number of joint angles using the intelligent prediction response method such as artificial neural network is used.

Dynamics Identification and Robust Control Performance Evaluation of Towing Rope under Rope Length Variation

Lately, tugboats are widely used to maneuver vessels by pushing or towing them where tugboats use rope. In order to correctly control the motion of tugboat and towed vessel, the dynamics of the towline would be well identified. In real application environment, the towing rope length changes and the towing load is not constant due to the various sizes of towed vessel. And there are many ropes made by many types of materials. It means that it is not easy to obtain rope dynamics, such that it is too difficult to satisfy the given control purpose by designing control system. Thus real time identification or adaptive control system design method may be a solution. However it is necessary to secure sufficient information about rope dynamics to obtain desirable control performance. In this paper, the authors try to have several rope dynamic models by changing the rope length to consider real application conditions. Among them, a representative model is selected and the others are considered as uncertain models which are considered in control system design. The authors design a robust control to cope with strong uncertain and nonlinear property included in the real plant. The designed control system based on robust control framework is evaluated by simulation.

수직다관절형 아암의 운동학적 모델링 및 관절공간 모션제어에 관한 연구

In this paper, we propose a new technique to the design and real-time control of an adaptive controller for robotic manipulator based on digital signal processors. The Texas Instruments DSPs(TMS320C80) chips are used in implementing real-time adaptive control algorithms to provide enhanced motion control performance for dual-arm robotic manipulators. In the proposed scheme, adaptation laws are derived from model reference adaptive control principle based on the improved Lyapunov second method. The proposed adaptive controller consists of an adaptive feed-forward and feedback controller and time-varying auxiliary controller elements. The proposed control scheme is simple in structure, fast in computation, and suitable for real-time control. Moreover, this scheme does not require any accurate dynamic modeling, nor values of manipulator parameters and payload. Performance of the proposed adaptive controller is illustrated by simulation and experimental results for a dual arm robot manipulator with eight joints. joint space and cartesian space.

A link partitioning approach for real-time control of queue spillbacks on congested arterials

ABSTRACTIn oversaturated urban traffic conditions when traffic demand exceeds capacity at signalised intersections, queues fail to clear during the allocated green times. Once a queue reaches the upstream intersection in an arterial, a queue spillback occurs that reduces the upstream link capacity. To mitigate the negative impacts of spillbacks, this article introduces a real-time adaptive traffic signal control method for global management of spillbacks along signalised arterials. The key idea of the proposed method is to implement a real-time partitioning of the arterial to detect critical cluster(s) of consecutive links with oversaturated traffic conditions. The partitioning approach enables to develop locally smaller-sized decentralised signal control strategies operating on the most upstream and downstream intersections of each cluster. Micro-simulation investigations on a real-world arterial site demonstrate the benefits of the proposed approach compared to an existing pre-timed signal control strategy and a classical decentralised green extension strategy. Utilising an advanced queue length detection method and specific focus on queue spillbacks prevention, the control strategy leads to significant reduction of congestion, and arterial total delay.

Forward search algorithm based on dynamic programming for real‐time adaptive traffic signal control

The scheduling of traffic signal at intersections is involved in an application of artificial intelligence system. This study presents a new forward search algorithm based on dynamic programming (FSDP) under a decision tree, and explores an efficient solution for real-time adaptive traffic signal control policy. Traffic signal control with cases of fixed phase sequence and variable phase sequence are both considered in the algorithm. Owing to the properties of forward research dynamic programming and the process optimisation of repeated or invalid traffic states the authors proposed, FSDP algorithm reduces the number of states and saves much computation time. Consequently, FSDP is certain to be an on-line algorithm through its application to a complicated traffic control problem. Moreover, the labelled position method is firstly proposed in the author's study to search the optimal policy after reaching the goal state. For practical operations, this new algorithm is extended by adding the rolling horizon approach, and some derived methods are compared with the optimal fixed-time control and adaptive control on the evaluation of traffic delay. Experimental results obtained by the simulations of symmetrical and asymmetrical traffic flow scenarios show that the FSDP method can perform quite well with high efficiency and good qualities in traffic control.

Adaptive traffic signal control in multiple intersections network

Traffic in urban areas is generally regulated by traffic lights, which represent a traffic flow bottleneck if they are not effectively configured. The dynamic nature of traffic flows makes reliable prediction possible only over a limited time horizon, and hence necessitates continual re-computation solutions. As intersections network size increases, the exponential growth in joint signal timing and traffic states exhibit a computational barrier to determining effective traffic lights timing. So, real-time traffic signal control optimization becomes a challenging problem. The present work investigates the issue of adaptive traffic control using real-time traffic data in multiple intersections. Each network intersection is controlled by an autonomous agent operating with a view of incoming traffic. The generated model presents ability to identifying dynamic changes in traffic flow conditions as well as adjusting green lights sequences and offering a good coordination between each intersections neighboring. The main contribution of this model is a real-time adaptive control of the traffic lights, according to discontinuity and heterogeneity of traffic flow. Simulation results confirm the system effectiveness compared to fixed time control, actuated time control and green wave method. And that by mitigating the congestion in terms of maximum throughput traffic flow, minimum waiting time and stops while maintaining fairness among all the network traffic lights.

Energy Management Design in Hybrid Electric Vehicles: A Novel Optimality and Stability Framework

This paper addresses the problem of finding a closed-form optimal solution for the energy management problem in charge-sustaining hybrid electric vehicles (HEVs), and proposes, for the first time, a generalized stability and optimality framework for this type of problem. The energy management problem, which by its very nature is a finite-time horizon control problem, is reformulated as a nonlinear–nonquadratic infinite-time optimization problem, leading to a family of state-feedback control laws that provide optimality with respect to an infinite time horizon performance objective, while guaranteeing asymptotic stability. The stability problem in charge-sustaining HEVs is formulated to allow the design of analytical solutions using a Lyapunov-based argument. The proposed control law is implemented on a pre-transmission parallel hybrid heavy-duty vehicle and the performance of the closed-loop system is shown in simulation and compared with the benchmark solution provided by Pontryagin’s minimum principle (PMP) and the real-time adaptive controller adaptive-PMP. Results show low sensitivity to the control parameter, low-calibration effort, and reduction of computational effort, while maintaining close-to-the-optimum performance. Hardware-in-the-loop simulations were conducted to validate and verify the new strategy in a real-time simulation setup.

A real-time adaptive signal control in a connected vehicle environment

The state of the practice traffic signal control strategies mainly rely on infrastructure based vehicle detector data as the input for the control logic. The infrastructure based detectors are generally point detectors which cannot directly provide measurement of vehicle location and speed. With the advances in wireless communication technology, vehicles are able to communicate with each other and with the infrastructure in the emerging connected vehicle system. Data collected from connected vehicles provides a much more complete picture of the traffic states near an intersection and can be utilized for signal control. This paper presents a real-time adaptive signal phase allocation algorithm using connected vehicle data. The proposed algorithm optimizes the phase sequence and duration by solving a two-level optimization problem. Two objective functions are considered: minimization of total vehicle delay and minimization of queue length. Due to the low penetration rate of the connected vehicles, an algorithm that estimates the states of unequipped vehicle based on connected vehicle data is developed to construct a complete arrival table for the phase allocation algorithm. A real-world intersection is modeled in VISSIM to validate the algorithms. Results with a variety of connected vehicle market penetration rates and demand levels are compared to well-tuned fully actuated control. In general, the proposed control algorithm outperforms actuated control by reducing total delay by as much as 16.33% in a high penetration rate case and similar delay in a low penetration rate case. Different objective functions result in different behaviors of signal timing. The minimization of total vehicle delay usually generates lower total vehicle delay, while minimization of queue length serves all phases in a more balanced way.

Network Traffic Control in Cyber-Transportation Systems Accounting for User-Level Fairness

This research identifies the advantage of connected vehicle environment in cyber-transportation systems (CTS) and proposes control strategies that can yield benefits from system and user perspectives. Particularly, the focus is to minimize the total stopped delay at signalized intersections for the entire trip of a user. Further, we define and assess the notion of fairness from the user perspective. We develop and implement a vehicle-to-infrastructure (V2I)-based signal controller within the framework of CTS. The test results from a network containing 15 signalized intersections show that the proposed fair queuing (FQ) algorithm performs better than the fixed control and real-time adaptive control in terms of both efficiency (system travel time) and fairness (minimizing the difference between maximum and mean intersection delay) across users.

Material removal with constant depth in CNC laser milling based on adaptive control of laser fluence

Computer numerical control CNC laser milling is an innovative technology to produce precision parts in micron and even nanometer scale. However, due to the restriction of dynamic performance of the machine tool, the preset feed rate cannot be reached immediately, and the actual feed rate is unpredictable when the part with rapidly varied geometric feature is machined. At this moment, the incident laser energy is still the default value, so the feed rate below the target value will cause excessive accumulation of laser fluence into the workpiece surface, eventually leading to an inhomogeneous milling depth and developing the excessive heat affect zone (HAZ). Aiming at the problem in CNC laser milling of the parts with rapidly varied geometric feature in this study, a method of real-time adaptive control of laser fluence according to actual feed rate was proposed. Firstly, a prediction model of laser milling depth was established. Then, based on the model and small amount of preliminary trials, laser fluence required at arbitrary feed rate was obtained to realize the material removal with constant depth. Furthermore, the adaptive control technology of laser fluence on the basis of actual feed rate was investigated. By monitoring the actual feed rate of the machine tool and regulating the laser fluence with it, the machining depth of the parts with rapidly varied geometric feature by CNC laser milling can remain constant. CNC laser milling experiments conducted in this paper demonstrate the feasibility and veracity of the adaptive control strategy.

Using IDS fitted Q to develop a real-time adaptive controller for dynamic resource provisioning in Cloud's virtualized environment

Reinforcement learning (RL) is a powerful solution to adaptive control when no explicit model exists for the system being controlled. To handle uncertainty along with the lack of explicit model for the Cloud's resource management systems, this paper utilizes continuous RL in order to provide an intelligent control scheme for dynamic resource provisioning in the spot market of the Cloud's computational resources. On the other hand, the spot market of computational resources inside Cloud is a real-time environment in which, from the RL point of view, the control task of dynamic resource provisioning requires defining continuous domains for (state, action) pairs. Commonly, function approximation is used in RL controllers to overcome continuous requirements of (state, action) pair remembrance and to provide estimates for unseen statuses. However, due to the computational complexities of approximation techniques like neural networks, RL is almost impractical for real-time applications. Thus, in this paper, Ink Drop Spread (IDS) modeling method, which is a solution to system modeling without dealing with heavy computational complexities, is used as the basis to develop an adaptive controller for dynamic resource provisioning in Cloud's virtualized environment. The performance of the proposed control mechanism is evaluated through measurement of job rejection rate and capacity waste. The results show that at the end of the training episodes, in 90 days, the controller learns to reduce job rejection rate down to 0% while capacity waste is optimized down to 11.9%.

A Switching Rollover Controller Coupled With Closed-Loop Adaptive Vehicle Parameter Identification

This paper presents a real-time adaptive switching controller to mitigate rollover accidents without reducing the performance of the vehicle. The proposed controller relies on adaptive identification of vehicle lateral and vertical dynamics parameters, including the center-of-gravity height that has a major role in rollover. Least squares and Kalman filtering techniques are employed to propose two novel identification algorithms that are robust against speed variations, which can be further coupled effectively with a switching rollover controller while parameter identification is in progress. Extensive simulations are carried out to demonstrate the superior performance of the proposed method.

Real-time robust adaptive control of robots subjected to actuator voltage constraint

This paper is concerned with the problem of design and implementation of a robust adaptive control strategy for electrically driven robots while considering to the constraints on the actuator voltage input. The proposed approach provides a flexible design framework and stable to deal with robustness compared with many other adaptive controllers, such as halting/slowing adaption techniques and adaptively adjusting command signal, which are proposed for robotic applications. The control design procedure is based on a new form of universal approximation theory and using Stone–Weierstrass theorem, to avoid saturation besides being robust against both structured and unstructured uncertainties associated with external disturbances and actuated manipulator dynamics. Moreover, the proposed approach eliminates problems arising from classic adaptive feedforward control scheme. The analytical studies as well as experimental results produced using MATLAB/SIMULINK external mode control on a two degree of freedom electrically driven robot demonstrate high performance of the proposed control schemes.