Model Adaptive Control Research Articles

Development of Vehicle Delay and Queue Length Models for Adaptive Traffic Control at Signalized Roundabout

Vehicle delay and queue length models are important indexes to optimize signal timing plan for a signalized roundabout. However, at present much attention is paid on unsignalized roundabouts. In this paper, we firstly analyzed the impacts of phasing schemes on vehicle movements and brought forward two typical phasing schemes. The loop detector layout plan was established to detect vehicle volumes of different streams in real time. Then under each phasing scheme, the models for average vehicle delay and queue length were developed respectively. Finally, case study was conducted to evaluate the models using field data collected from a real signalized roundabout. The results show that: all precision errors are smaller than 15% and average precision errors are smaller than 10%. The developed two types of models can satisfy the requirements of engineering.

Design of the time-gap-dependent robust headway control algorithm for ACC vehicles

A robust fuzzy headway control algorithm based on the constant time headway (CTH) model for adaptive cruise control (ACC) vehicles is developed. Considering the possible adjustment of the time gap (TG) by a driver after the activation of ACC, inter-vehicle kinematics is established by using a Takagi-Sugeno fuzzy model with the TG as the premise variable. A robust headway controller based on the model is designed by using the Lyapunov function approach. Control input saturation is considered in the design of the controller. Furthermore, the initial conditions of transitional manoeuvres from the velocity control mode to the distance control mode are computed on the basis of inter-vehicle kinematics. The designed algorithm is verified in various operating conditions. Passenger comfort can be improved by the designed algorithm under the precondition of driving safety in the tuning processes of the TG. And the individual preferences of various drivers on ACC are achieved and acceptability is eventually improved.

Research on Wind Power System with Adaptive Speed Control of MPPT Algorithm

MPPT (Maximum Power Point Tracking) is one of the most important technologies in the wind power system. It analyses the wind power system’s model. Based on properties of wind machine’s mechanical and electrical, around the rotor dynamics and the excitation winding voltage, this paper researches the automatic tracking algorithm of rotational speed. By dynamically changing winding voltage it controls the generator reaction torque, then the rotational speed is adjusted and it develops a new nonlinear adaptive transmission control model for wind power generator. This control model can adjust the excitation winding of the alternator voltage dynamically and make wind turbines to achieve the maximum power point tracking rapidly.

BTG-AC: Break-the-Glass Access Control Model for Medical Data in Wireless Sensor Networks.

Wireless sensor networks (WSNs) have recently attracted much interest in the research community because of their wide range of applications. An emerging application for WSNs involves their use in healthcare where they are generally termed wireless medical sensor networks. In a hospital, outfitting every patient with tiny, wearable, wireless vital sign sensors would allow doctors, nurses, and other caregivers to continuously monitor the state of their patients. In such a scenario, patients are expected to be treated in reasonable time, so an access control model is needed, which will provide both real-time access to comprehensive medical records and detect unauthorized access to sensitive data. In emergency situations, a doctor or nurse needs to access data immediately. The loss in data availability can result in further decline in the patient's condition or can even lead to death. Therefore, the availability of data is more important than any security concern in emergency situations. To address that research issue for medical data in WSNs, we propose the break-the-glass access control (BTG-AC) model that is a modified and redesigned version of the break-the-glass role-based access control (BTG-RBAC) model to address data availability issue and to detect the security policy violations from both authorized and unauthorized users. Several changes within the access control engine are made in BTG-RBAC in order to make the new BTG-AC to apply and fit in WSNs. This paper presents the detailed design and development of the BTG-AC model based on a healthcare scenario. The evaluation results show that the concepts of BTG, prevention and detection mechanism, and obligation provide more flexible access than other current access control models in WSNs. Additionally, we compare the BTG-AC model with an adaptive access control (A2C) model, which has similar properties, for further evaluation. Alongside with the comparison, the advantages and disadvantages of BTG-AC over current WSN access control models are presented.

Risk adaptive hybrid RFID access control system

AbstractDynamic environments pose a challenge for traditional access control models where permissions are granted or revoked merely based on predefined and static access policies making them incapable of dynamically adapting to changing conditions. Risk adaptive access control models have been gaining more attention in the research community as an alternative approach to overcome the limitations of traditional access control models. Radio Frequency Identification (RFID) is an emerging technology widely utilized in both physical and logical access control systems because of its contactless nature, low cost, high read/write speed and long distance operation. Serverless RFID system architecture offers better availability assurance and lower implementation cost, while access rights management is easier in server‐based architecture. In this study, we continue to build on our previous research on the privacy and security of RFID access control systems without a backend database in order to overcome its limitations. We propose a hybrid design for a risk adaptive RFID access control system; that is, dynamically alternating between two access control modes, online (server‐based) and offline (serverless), to adapt to the level of risk depending on rule‐based risk scenarios and current risk value. The proposed design combines features of both serverless and risk adaptive access control systems. Copyright © 2015 John Wiley & Sons, Ltd.

Intelligent decision making model for traffic congestion control based on fuzzy petri net

This research established an adaptive congestion control model using the knowledge representation and reasoning methods, proposed variable connection weights FPN knowledge representation and reasoning model, and put forward a matrix formal reasoning algorithm. The model comprised a variety of decision variables and diverse control rules, designed an intersection signal control mode with variable phase sequence by considering the upstream and downstream traffic flow, so as to guide and control saturated traffic flow quickly and efficiently. Finally, visualization simulation was used to confirm the validity of the model by VC + +.

Stochastic programming model for oversaturated intersection signal timing

In large cities, signalized intersections often become oversaturated in rush hours due to growing traffic demand. If not controlled properly, they may collectively result in serious congestion. How to schedule traffic signals for oversaturated intersections has thus received increasing interests in recent years. Among various factors that may influence control performance, uncertainty in traffic demand remains as an important one that needs to be further studied. In some recent works, e.g., Yin (2008) and Li (2011), robust optimization models have been utilized to address uncertainty in traffic demand and to design fixed-timed signal control for oversaturated intersections. In this paper, we propose a stochastic programming (SP) model to schedule adaptive signal timing plans that minimize the expected vehicle delay. Our numerical experiments show that the proposed SP model better describes the fluctuations of traffic flows and outperforms the deterministic linear programming (LP) model in total vehicle delay, total throughput, and vehicle queue lengths. Moreover, we compare the proposed SP model with the adaptive signal control model proposed in Lin et al. (2011) to provide insights on such improvements from green time utilization and queue balancing perspectives. Furthermore, we study the feasibility of the proposed SP model in practice, with an emphasis on the required sample sizes.

Modeling and Simulation of Adaptive Control for a Class of Nonlinear System

This paper investigates adaptive control for a class of nonlinear system with uncertain parameters and unknown disturbances. We propose method of improving performance of the controller via modeling and simulation analysis. The method is achieved under different simulation circumstances. It is shown that the global asymptotical stability of closed-loop system can be guaranteed by nonlinear adaptive robust design. The simulation results demonstrate the effectiveness of the presented method.

Intelligent decision making model for traffic congestion control based on fuzzy petri net

This research established an adaptive congestion control model using the knowledge representation and reasoning methods, proposed variable connection weights FPN knowledge representation and reason...

Person-Based Adaptive Priority Signal Control with Connected-Vehicle Information

The goal for transit signal priority (TSP) strategies is to improve the efficiency of urban transportation systems by promoting fast passage of system users. However, because conventional vehicle detection technologies require TSP strategies to be vehicle based, TSP may not lead to optimal results for person delay. This paper proposes a signal control model called PAPSCCI (person-based adaptive signal priority control with connected-vehicle information). First, by using vehicle speed and location information available from connected-vehicle technologies, the model explicitly computes individual vehicle delay. In this way the model avoids assumptions about vehicle arrivals, which often are inevitable in a delay calculation derived from a queuing model. Furthermore, in the model approach, the computation of delays for private vehicles is no different from that for public buses except in the priority level and unifies the two types of vehicles. With onboard passenger information, the PAPSCCI model computes person delay for every vehicle running through the intersection and offers a more accurate basis for person delay minimization. The performance of the PAPSCCI model is evaluated in a traffic simulation environment. Compared with the optimized timing from SYNCHRO, the PAPSCCI model produces 39%, 49%, and 30% decreases in bus passenger delays for one, two, and three conflicting bus routes, respectively. In addition, general automobiles experience about an 8% to 11% decrease in person delays, showing the potential of PAPSCCI as a general adaptive signal control model. Finally, a penetration rate study shows that the PAPSCCI model can consistently perform reasonably well even when only about 30% of vehicles are equipped with connected-vehicle technology.

Optimal allocation and adaptive VAR control of PV-DG in distribution networks

The development of distributed generation (DG) has brought new challenges to power networks. One of them that catches extensive attention is the voltage regulation problem of distribution networks caused by DG. Optimal allocation of DG in distribution networks is another well-known problem being widely investigated. This paper proposes a new method for the optimal allocation of photovoltaic distributed generation (PV-DG) considering the non-dispatchable characteristics of PV units. An adaptive reactive power control model is introduced in PV-DG allocation as to balance the trade-off between the improvement of voltage quality and the minimization of power loss in a distribution network integrated with PV-DG units. The optimal allocation problem is formulated as a chance-constrained stochastic programming (CCSP) model for dealing with the randomness of solar power energy. A novel algorithm combining the multi-objective particle swarm optimization (MOPSO) with support vector machines (SVM) is proposed to find the Pareto front consisting of a set of possible solutions. The Pareto solutions are further evaluated using the weighted rank sum ratio (WRSR) method to help the decision-maker obtain the desired solution. Simulation results on a 33-bus radial distribution system show that the optimal allocation method can fully take into account the time-variant characteristics and probability distribution of PV-DG, and obtain the best allocation scheme.

Simulation Validation of Three Nonlinear Model-Based Controllers in the Adaptive Cruise Control System

In this paper, the simulation validation of the hierarchical two-loop Adaptive Cruise Control (ACC) system is considered as a candidate for the application in the Advanced Driver Assistance Systems (ADAS), which aims to ensure driving safety and comfort as well as to improve fuel efficiency. Three different nonlinear model-based approaches for the inner-loop controllers are investigated for this system: the conventional Proportional-Integral Gain Scheduling controller (PI + GS) and two other strategies based on the simplified modelling of the vehicle dynamics: Balance-Based Adaptive Controller (B-BAC) and Nonlinear Model Predictive Controller (NMPC). The performance of each considered ACC system is tested by simulation under the same realistic scenarios for distance tracking mode and switching mode. The comparative criteria include driving safety, comfort of the driver and passengers and the fuel-economy aspect defined as BSFC (Brake Specific Fuel Consumption) index. The simulation results demonstrate that all the considered control algorithms meet the safety and car-following requirements while they provide slightly different level of driving comfort and fuel consumption, depending on the traffic situation and operating mode.

Adaptive robust control of a high-response dual proportional solenoid valve with flow force compensation

This article focuses on the high-performance control of a high-response proportional solenoid valve which has two proportional solenoids. The valve is named high-response dual proportional solenoid valve (HDPSV). The HDPSV can achieve faster response than other high-response proportional solenoid valves with one proportional solenoid and has the potential of gaining stronger disturbance rejection due to the differential effect of two proportional solenoids. However, the expense for these advantages is that more complexity and more difficulties are involved in controlling the valve. In this article, the nonlinear dynamic model of the HDPSV is constructed first. The current–force gain of the proportional solenoid is modeled by a modified tanh() function to capture its nonlinear characteristics. An incremental differential allocation strategy is raised to coordinate the action of the two proportional solenoids driving the HDPSV. The model of flow force is built with parametric uncertainty and reconstructed to address its non-differentiability. To deal with the parametric uncertainties and uncertain nonlinearities appearing in the dynamic model of the HDPSV discontinuous projection-based adaptive robust control, which combines systematically backstepping adaptive control with deterministic robust control, is extended to synthesize HDPSV controller. Simulations and experiments are carried out to validate the effectiveness of the controller. Both the simulation and experimental results show that with the proposed adaptive robust controller, the flow forces are well compensated, while the HDPSV achieves disturbance rejection, high precision, fast transient response and broad bandwidth.

Design and Control of 3-DoF Spherical Parallel Mechanism Robot Eyes Inspired by the Binocular Vestibule-ocular Reflex

With the wide application of robot in unstructured environment, cognizing and understanding environmental information accurately is the key for robots to complete the task. Thus, visual stability problems in these robot control systems have become important design issues. In this paper, to solve the problem of vision instability caused by attitude variation of the robot working under bumpy environment, a bionic eyes system of active compensation for robot visual error is proposed inspired by the binocular VOR. The bionic vision system is developed based on features of oculomotor behaviors and bionic control algorithm of the binocular VOR. According to the behaviors features of eye movement, the bionic eye mechanism is designed by using a 3-DoF spherical parallel mechanism(SPM). Meanwhile, to improve the performance, the mechanism parameters of SPM are optimized by utilizing the worst-case performance index as the optimizing target in required workspace. An adaptive control model of the binocular VOR is established on neural control mechanisms of eye movement, and the model is used as the bionic control algorithm to drive and control the eye mechanism. The results indicate that the model can actively compensate the visual errors from postural changes of robot. Physical robot experiments also show that the system is robust even with bumpy environment.

Adaptive-Loss minimization control for speed sensorless induction machines

This paper presents a new online loss-minimization algorithm in the adaptive control model for speed sensorless based on the Direct Rotor Flux Oriented Control (DRFOC) with optimum flux in the condition of do variable stator resistance. The paper also presents a new approach to develop a reference and adaptive models to estimate the speed and to observe stator resistance change. Simulation results in Matlab Simulink showed good adaptability of the presented model controls even when the stator resistance changes to up 200% of rated. In addition, using the proposed algorithm allows reducing losses and improving efficiency of proposed control model in comparison with DRFOC traditional model for load range 0-70% of the rated. Where the difference of efficient can reach 42%, and the difference in loss to 85%.

Implementation Analysis of State Space Modelling and Control of Nonlinear Process Using PID Algorithm in MATLAB and PROTEUS Environment

The scope of this paper is to look beyond linear solutions and discuss briefly about the recent developments in nonlinear system control & controller tuning methods. An adaptive cruise control model is taken as case study to illustrate the practicality of implementation in the simulation environment in terms of modelling and control of non-linear process. Effort is made to analyse the nonlinear system control in MATLAB and PROTEUS environment.

An Adaptive Error Event Control Model for Smart Festival Management System running on Situation-Awareness Environments

This paper proposes a new adaptive error event control model for smart festival management running on situation-awareness environments by analyzing the window and attributes of the object error event. This paper presents the design of the adaptive error event control model for sharing control, which is running on RCSM (Reconfigurable Context Sensitive Middleware) for smart festival management. RCSM provides standardized communication protocols to interoperate an application with others under dynamically changing situations for the smart festival management.

Constrained Nonlinear Model Predictive Control of a Polymerization Process via Evolutionary Optimization

In this work, a nonlinear model predictive controller is developed for a batch polymerization process. The physical model of the process is parameterized along a desired trajectory resulting in a trajectory linearized piecewise model (a multiple linear model bank) and the parameters are identified for an experimental polymerization reactor. Then, a multiple model adaptive predictive controller is designed for thermal trajectory tracking of the MMA polymerization. The input control signal to the process is constrained by the maximum thermal power provided by the heaters. The constrained optimization in the model predictive controller is solved via genetic algorithms to minimize a DMC cost function in each sampling interval.

A Reconfiguration Control Scheme for a Quadrotor Helicopter via Combined Multiple Models

In this paper, an optimal reconfiguration control scheme is proposed for a quadrotor helicopter with actuator faults via adaptive control and combined multiple models. The combined models set contains several fixed models, an adaptive model and a reinitialized adaptive model. The fixed models and the adaptive model can describe the failure system under different fault conditions. Moreover, the proposed reinitialized adaptive model refers to the closest model of the current system and can improve the speed of convergence effectively. In addition, the reference model is designed in consideration of an optimal control performance index and the principle of the minimum cost to achieve perfect tracking performance. Finally, some simulation results demonstrate the effectiveness of the proposed reconfiguration control scheme for faulty cases.

Online Coordination of Signals for Heterogeneous Traffic Using Stop Line Detection

Real time control of heterogeneous traffic is always a challenge for efficient and effective traffic management. The solution becomes more complex when the heterogeneity is augmented with limited lane discipline. Adaptive traffic signal controllers offer better signal time management especially when the traffic is not saturated. Traditional methods of upstream vehicle detection (UTOPIA, PRODYN, SCOOT, OPAC, and RHODES) work well with disciplined traffic. However, this approach has several limitations when the drivers do not adhere to their respective lanes. To address this, a traffic adaptive control model which uses stop-line detector information is proposed in this paper. In a corridor of closely spaced controlled intersection, vehicular movement can be improved largely by synchronizing the signals between intersections. Further, efficiency of the synchronization can be enhanced by operating the corridor at an optimum cycle. The model aims at real-time allocation of optimum cycle time through actor-critic reinforcement learning. This approach has the ability to learn relationships between control action such as cycle time and their effect on the vehicle queuing while pursuing a goal of maximizing intersection throughput. When this model is applied to large scale problem such as multiple phasing, will generate large state-space which limits application of conventional reinforcement learning techniques. To address this, actor- critic reinforcement learning with function approximation technique is used. The performance of the model is tested on a typical four phase four intersections arterial with variable flow is simulated using a traffic micro-simulator (VISSIM) and interfaced with the proposed model using dynamic link library (DLL). The model performance is compared with the traditional fully-actuated system. The results using this approach shows significant improvement over traditional control in the coordinated direction, especially for the traffic with demand transition.