Design Of Adaptive Control System Research Articles

Adaptive Neuro-Fuzzy Predictor-Based Control for Cooperative Adaptive Cruise Control System

In this paper, an adaptive neuro-fuzzy predictor-based control (ANFPC) approach with integrated automotive radar and vehicle-to-vehicle (V2V) communication for the design of the cooperative adaptive cruise control (CACC) system is presented, which concerns not only the safety and riding comfort of a vehicle but also enhances its fuel efficiency. This paper consists of two main parts: preceding vehicle state estimation and following vehicle controller. First, the prospective information of the preceding vehicle, such as position, velocity, and acceleration, can be derived through radar sensor, and the control force of the preceding vehicle can be transmitted to the following vehicles through V2V communication. A Takagi–Sugeno fuzzy model is then utilized to estimate the preceding vehicle model, and the predicted state sequence of the preceding vehicle can be obtained. Second, based on these predicted data, the following vehicle is controlled by the proposed ANFPC scheme to maintain each vehicle within the desired distance headway and thus achieve string stability of vehicle platooning and fuel efficiency. The experimental results on the CarSim environment show that the proposed control strategy for the CACC system can significantly reduce the fuel consumption while ensuring driving comfort and safety.

Design of Output Feedback Adaptive Control System with Anti-Windup Compensator for Input Saturated Systems

Design of Output Feedback Adaptive Control System with Anti-Windup Compensator for Input Saturated Systems

Output Feedback-Based Output Tracking Control with Adaptive Output Predictive Feedforward for Multiple-Input–Multiple-Output Systems

This paper proposes a two-degree-of-freedom adaptive output feedback control system design scheme with an adaptive output predictive feedforward for multiple-input–multiple-output (MIMO) LTI system...

A New Adaptive Control System Design Method Based on Neural Network Prediction

A New Adaptive Control System Design Method Based on Neural Network Prediction

Adaptive point-to-point control system design using friction-based optimal state feedback

Abstract This paper presents an adaptive control system design for high-precision positioning of a point-to-point (PTP) servo actuator. These mechanisms are characterized by nonlinear friction behaviour, vibrational modes, and dead times which are limitations to precise motion. In the proposed strategy, adaptation of a primary controller (PID) efforts is obtained depending on an optimal state feedback. As a new methodology, estimating the states of a PTP servo actuator was carried out based on the observed friction forces besides the control signal. Performances of the proposed control systems were verified using numerical simulations and compared with that of the primary controller. The results demonstrate that, the proposed scheme achieved high-precision positioning of the PTP servo actuator regardless of the presence of its dynamics problems. Moreover, the simplicity of the proposed control technique and its effectiveness for getting high-precision motion support its applicability to variety of applications.

Adaptive Anti-Windup Control System Design for ASPR-based Adaptive Output Feedback Control

ASPR-based adaptive output feedback control is considered as one of the simple and robust adaptive control method. However, in the case where there is an input saturation, windup phenomena might occur in the ASPR- based adaptive output feedback control and it may degrade the control performance significantly. In this paper, we propose an anti-windup control strategy for the ASPR-based adaptive output feedback control system. An adaptive anti-windup control based on a model recovery anti-windup control technique is proposed to improve the control performance of ASPR-based adaptive output feedback control systems with an input saturation. The stability of the obtained adaptive anti-windup control system will also be analyzed.

Design and Development of Adaptive Fuzzy Control System for Power Management in Residential Smart Grid Using Bat Algorithm

An adaptive fuzzy logic control system was designed for the management of power in smart grid. The system was developed using BAT algorithm. This paper analyses the approach of adaptive fuzzy logic system accompanied with bat algorithm in power management of smart grid. The path of carrying information i.e. communication layer between users and the energy provider in smart grid is analyzed. To achieve this, behavior of bats and its algorithm is studied and compared with vectors in adaptive fuzzy logic system and applied in power utility side of smart grid. It helps in indicating peak in load and also helps in prediction of power consumption in users. The offline algorithm is developed, simulated using Matlab 16a and simulation results are evaluated.

Output Feedback Q-Learning Control for the Discrete-Time Linear Quadratic Regulator Problem.

Approximate dynamic programming (ADP) and reinforcement learning (RL) have emerged as important tools in the design of optimal and adaptive control systems. Most of the existing RL and ADP methods make use of full-state feedback, a requirement that is often difficult to satisfy in practical applications. As a result, output feedback methods are more desirable as they relax this requirement. In this paper, we present a new output feedback-based Q-learning approach to solving the linear quadratic regulation (LQR) control problem for discrete-time systems. The proposed scheme is completely online in nature and works without requiring the system dynamics information. More specifically, a new representation of the LQR Q-function is developed in terms of the input-output data. Based on this new Q-function representation, output feedback LQR controllers are designed. We present two output feedback iterative Q-learning algorithms based on the policy iteration and the value iteration methods. This scheme has the advantage that it does not incur any excitation noise bias, and therefore, the need of using discounted cost functions is circumvented, which in turn ensures closed-loop stability. It is shown that the proposed algorithms converge to the solution of the LQR Riccati equation. A comprehensive simulation study is carried out, which illustrates the proposed scheme.

An Adaptive Liquid Level Controller Using Multi Sensor Data Fusion

This paper describes a design of adaptive liquid level control system using the concept of Multi Sensor Data Fusion (MSDF). Purpose of the work is to design a controller for accurately controlling the level of liquid in a process tank with liquid temperature changes. The proposed objective is obtained by i) implementing a MSDF framework using Pau’s framework for measuring liquid level and temperature, ii) analyzing the behavior of actuator output for variation in liquid temperature, and iii) designing a suitable adaptive controller which will produce desired control action for controlling liquid level accurately using neural network algorithms. Outputs from sensors are fused to obtain the fluid level output and also relation of level transmitter output for change in temperature. This information is used by controller to train the neural network so as to tune the controller parameters (proportional gain, integral constant, and differential constant), to drive the actuator. Results obtained show that the system is able to control liquid level within range of 1.915% of set point even with variations in liquid temperature.

Aircraft neural modeling and parameter estimation using neural partial differentiation

PurposeThe purpose of this paper is to build a neural model of an aircraft from flight data and online estimation of the aerodynamic derivatives from established neural model.Design/methodology/approachA neural model capable of predicting generalized force and moment coefficients of an aircraft using measured motion and control variable is used to extract aerodynamic derivatives. The use of neural partial differentiation (NPD) method to the multi-input-multi-output (MIMO) aircraft system for the online estimation of aerodynamic parameters from flight data is extended.FindingsThe estimation of aerodynamic derivatives of rigid and flexible aircrafts is treated separately. In the case of rigid aircraft, longitudinal and lateral-directional derivatives are estimated from flight data. Whereas simulated data are used for a flexible aircraft in the absence of its flight data. The unknown frequencies of structural modes of flexible aircraft are also identified as part of estimation problem in addition to the stability and control derivatives. The estimated results are compared with the parameter estimates obtained from output error method. The validity of estimates has been checked by the model validation method, wherein the estimated model response is matched with the flight data that are not used for estimating the derivatives.Research limitations/implicationsCompared to the Delta and Zero methods of neural networks for parameter estimation, the NPD method has an additional advantage of providing the direct theoretical insight into the statistical information (standard deviation and relative standard deviation) of estimates from noisy data. The NPD method does not require the initial value of estimates, but it requires a priori information about the model structure of aircraft dynamics to extract the flight stability and control parameters. In the case of aircraft with a high degree of flexibility, aircraft dynamics may contain many parameters that are required to be estimated. Thus, NPD seems to be a more appropriate method for the flexible aircraft parameter estimation, as it has potential to estimate most of the parameters without having the issue of convergence.Originality/valueThis paper highlights the application of NPD for MIMO aircraft system; previously it was used only for multi-input and single-output system for extraction of parameters. The neural modeling and application of NPD approach to the MIMO aircraft system facilitate to the design of neural network-based adaptive flight control system. Some interesting results of parameter estimation of flexible aircraft are also presented from established neural model using simulated data as a novelty. This gives more value addition to analyzing the flight data of flexible aircraft as it is a challenging problem in parameter estimation of flexible aircraft.

Adaptive TOPSIS fuzzy CMAC back-stepping control system design for nonlinear systems

This paper aims to propose a more efficient control algorithm for nonlinear systems. A novel adaptive Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) fuzzy cerebellar model articulation controller (FCMAC) back-stepping control system is developed. The proposed adaptive TOPSIS FCMAC (ATFCMAC) incorporates a multi-criteria decision analysis with a fuzzy CMAC structure to determine the optimal threshold values for selecting suitable firing nodes, improving the computational efficiency, reducing the number of firing rules, and achieving good performance for nonlinear systems. A back-stepping technique is employed for the control system design. The proposed control system comprises an ATFCMAC and a robust compensator; the ATFCMAC is used to approximate an ideal controller and the robust compensator is used to reduce the influence of residual approximation error between the ideal controller and the ATFCMAC. The parameters of the proposed ATFCMAC are tuned online using the adaptation laws that are derived from a Lyapunov stability theorem, so that the stability of the control system is guaranteed. The simulation and experimental results for a Duffing–Holmes chaotic system and a magnetic ball levitation system are used to verify the effectiveness of the proposed control scheme.

Predictive intelligent driver model for eco-driving using upcoming traffic signal information

Without accounting for the signalized intersection constraints in the design of adaptive cruise control (ACC) system, the ACC-equipped connected vehicle (CV) traveling on signalized roadway should be taken over by driver at signalized intersection frequently and speed variations increase significantly. Aiming at addressing this issue, a predictive intelligent driver model (IDM) for eco-driving based on V2X communication is proposed by using upcoming traffic signal information, which can be regarded as the upper controller of ACC system. The intersection signal constraints considered in IDM is treated as a dummy preceding vehicle at red light and no barriers at green light to cope with the application extending problem. To reduce idling time at signalized intersection, an intersection passing decision is presented with model prediction to forecast the arrival time with downstream queue discharge time under consideration, and an eco-driving model with speed reduction strategy is proposed by solving the combined constraints of the signal phase and timing (SPaT) and the vehicle status. Numerical simulations show that taking the speed profile generated by eco-driving model as speed advisor can reduce idling times and fuel consumption levels in the vicinity of signalized intersection.

Adaptive Output Feedback Control System Design with Adaptive Feedforward Input for MIMO Systems with the Relative Degree of Zero

Adaptive Output Feedback Control System Design with Adaptive Feedforward Input for MIMO Systems with the Relative Degree of Zero

A robust neuro-based adaptive control system design for a surface effect ship with uncertain dynamics and input saturation to cargo transfer at sea

This paper is concerned with the problem of safely cargo transfer over a ramp between a cargo vessel and a lighter surface effect ship (SES). For this purpose, an adaptive neural network (NN) controller is proposed to control of ramp motions in the presence of entirely unknown dynamics and disturbances wherein the controller is subject to input saturation constraint. In this regard, we develop a novel non-affine nonlinear SES model by considering the nonlinear relationship among the air cushion pressure, the air flow into and out of the air cushion and the air cushion volume. To deal with the saturation constraint, an auxiliary system is proposed. To ensure the system stability, Lyapunov’s direct method is employed to investigate the uniformly ultimately boundedness (UUB) of all closed-loop system states. The simulation results demonstrate the effectiveness of the proposed controller in critical sea conditions, including regular and irregular waves with high frequencies and large amplitudes. A comparative analysis with model-based approaches including Proportional Integral Derivative (PID) and Linear-Quadratic Regulator (LQR) control systems are given to highlight the performance of the controller.

Modeling and Design of Adaptive Video Streaming Control Systems

Adaptive video streaming systems aim at providing the best user experience given the user device and the network available bandwidth. With this purpose, a controller selecting the video bitrate (or level) from a discrete set $\mathcal{L}$ has to be designed. The control goal is to maximize the video bitrate while avoiding playback interruptions and minimizing video bitrate switches. In this paper, we propose a hybrid dynamical system modeling the essential features of an important class of controllers for adaptive video streaming systems. We derive tuning rules to achieve key performance goals by sizing the control system parameters. We show how to: (i) tune the controller parameters to keep the video level switching frequency below a given target; (ii) design the video levels set $\mathcal{L}$ to obtain a performance tradeff between switching frequency and storage costs at the servers; (iii) find the minimum amount of playout buffer that should be stored to avoid rebuffering events with a given probability in case of temporary bandwidth drop. The theoretical results are validated through numerical simulation and experimental evaluation.

Design and Evaluation of Robust Cooperative Adaptive Cruise Control Systems in Parameter Space

This paper is on the design of cooperative adaptive cruise control systems for automated driving of platoons of vehicles in the longitudinal direction. Longitudinal models of vehicles with simple dynamics, an uncertain first order time constant and vehicle to vehicle communication with a communication delay are used in the vehicle modeling. A robust parameter space approach is developed and applied to the design of the cooperative adaptive cruise control system. D-stability is chosen as the robust performance goal and the feedback PD controller is designed in controller parameter space to achieve this D-stability goal for a range of possible longitudinal dynamics time constants and different values of time gap. Preceding vehicle acceleration is sent to the ego vehicle using vehicle to vehicle communication and a feedforward controller is used in this inter-vehicle loop to improve performance. Simulation results of an eight vehicle platoon of heterogeneous vehicles are presented and evaluated to demonstrate the efficiency of the proposed design method. Also, the proposed method is compared with a benchmark controller and the feedback only controller. Time gap regulation and string stability are used to assess performance and the effect of the vehicle to vehicle communication frequency on control system performance is also investigated.

The Design of Adaptive Control System with Reference Model for Population Dynamics

The Design of Adaptive Control System with Reference Model for Population Dynamics

Adaptive Output Feedback Control System Design with Adaptive PFC for Combustion Control of Diesel Engine

This paper deals with a combustion control system design problem of diesel engines. We propose an adaptive output feedback control system design scheme with an adaptive parallel feedforward compensator (PFC) for diesel engines with triple fuel injections. In order to guarantee the stability of the designed adaptive control system, a PFC is introduced and adaptively adjusted for maintaining the almost strictly positive real (ASPR) property of the resulting augmented system. The SISO control system is designed by considering main injection timing as the control input and peak pressure timing as the output. The effectiveness of the proposed method is confirmed through numerical simulations.

Design of adaptive control and virtual reality-based fine hand motion rehabilitation system and its effects in subacute stroke patients

ABSTRACTRobot-assisted therapy is regarded as an effective and reliable method for the delivery of highly repetitive training that is needed to trigger neuroplasticity following a stroke. This study focuses on the pre-defined research gaps by combining adaptive assist-as-needed control and immersive virtual environment (VE) into a rehabilitation system, with special attention on the rehabilitation of fine hand motion skills. The virtual reality-based integrated rehabilitation system using Oculus Rift DK2 head mounted display, featuring rehabilitation gaming system with rendering immersive VE is proposed to strengthen activities of daily living and task-oriented kinematic features such as force, range of motion (ROM) and finger coordination. The effectiveness of the system is examined by conducting clinical trials on a group of 8 subacute stroke patients for a period of six weeks with 18 training sessions. The results are verified through clinical evaluation methods and key kinematic features such as active ROM and finger strength. By comparing the pre- and post-training results, the study demonstrates that the proposed method can enhance the efficiency of fine hand motion rehabilitation training by 35% on average in the patients who participated in the experimental work.

Adaptive Output Feedback based Output Tracking Control with Adaptive Parallel Feedforward Compensator

This paper deals with an almost strictly positive real (ASPR) based adaptive output tracking control system design problem. A design scheme of adaptive output feedback based output tracking control system with an adaptively adjusting parallel feedforward compensator is proposed. For non-ASPR system, in order to guarantee the stability of the designed adaptive control system, a parallel feedforward compensator (PFC) is introduced and is adaptively adjusted for remaining the ASPR-ness of the resulting augmented system. Furthermore, an adaptive NN feedforward control is added to attain the output tracking. The stability of the resulting control system is analyzed theoretically and the effectiveness of the proposed method will be confirmed through numerical simulations.