Adaptation of diagonal recurrent neural network model

Fault tolerant control (FTC) using an adaptive recurrent neural network model is developed in this paper. The model adaptation is achieved with the extended Kalman filter (EKF). A novel recursive algorithm is proposed to calculate the Jacobian matrix in the model adaptation so that the algorithm is simple and converges fast. A model inversion control with the developed adaptive model is applied to nonlinear processes and fault tolerant control is achieved. The developed control scheme is evaluated by a simulated continuous stirred tank reactor (CSTR) and effectiveness is demonstrated.

This paper adopts the adaptive neural internal model control strategy to design a control system for complex and uncertain process with delay. Because of the shortage of the classic internal model control system the performance of the control system will slip back if there exist large errors between the real plant and the model, an adaptive mechanism based on single neuron which can tune the parameters of the internal model and that of controller in the control system on line is designed. The multimodel approach is exploited to represent the direct model by a model basis made up of five linear models, and to construct a controller trained by the corresponding reverse models. The simulation results shows that the adaptive internal model control system designed in this paper has good performance of overcoming deviations of model parameters.

Since mobile networks are not currently deployed on a large scale, research in this area is mostly by simulation. Among other simulation parameters, the mobility model plays a very important role in determining the protocol performance in MANET. Based on random direction mobility model, a high dynamic adaptive mobility network model is proposed in the paper. The algorithms and modeling are mainly studied and explained in detail. The technique keystone is that normal distribution is combined with uniform distribution and inertial feedback control is combined with kinematics, through the adaptive control on nodes speed and prediction tracking on nodes routes, an adaptive model is designed, which can be used in simulations to produce realistic and dynamic network scenarios. It is the adaptability that nodes mobile parameters can be adjusted randomly in three-dimensional space. As a whole, colony mobility can show some rules. Such random movement processes as varied speed and dwells are simulated realistically. Such problems as sharp turns and urgent stops are smoothed well. The model can be adapted to not only common dynamic scenarios, but also high dynamic scenarios. Finally, the mobility model performance is analyzed and validated based on random dynamic scenarios simulations.

Considers the design and analysis of a robust adaptive model algorithmic controller based on the internal model control structure. The certainty equivalence principle of adaptive control is used to combine a discrete-time robust adaptive law with a model algorithmic internal model controller to obtain an adaptive model algorithmic internal model control scheme with provable guarantees of stability and robustness. The approach used parallels the earlier results obtained for adaptive internal model control. Nevertheless, there are some important differences which, together with the widespread interest in model algorithmic control, justifies a separate exposition.

In the navigation of underwater robots, large ocean current disturbance often causes significant tracking errors. To better resist ocean current disturbance, the hydrodynamic characteristics of the spherical underwater robot are studied, and a model predictive control strategy based on adaptive model parameters is proposed, according to these characteristics. Firstly, the hydrodynamic characteristics of the robot under static water and constant flow disturbance were obtained and analyzed by the computational fluid dynamics method. Then, the dynamic models of the robot under different disturbances could be calculated from the data obtained, based on the least square method. Finally, an adaptive model predictive control (AMPC) strategy, with an ocean current observer, was designed, based on the dynamic models. When the current disturbance velocity was twice the robot velocity, the proposed strategy reduced the tracking error by 39% and 42% in X and Y directions, respectively. In addition, the hydrodynamic characteristics were verified by experiments.

This paper presents an adaptive neural fuzzy network model for seasonal stream flow forecasting. The model is based on a constructive learning method that adds neurons to the network structure whenever new knowledge is necessary so that it learns the fuzzy rules and membership functions essential for modeling a fuzzy system. The model was implemented to forecast monthly average inflow on an one-step-ahead basis. It was tested on three hydroelectric plants located in different river basins in Brazil. When the results were compared with those of a multilayer feedforward neural network model, the present model revealed at least a 50% decrease in the forecasting error.

Dynamic obstacle avoidance of unmanned ship based on event-triggered adaptive nonlinear model predictive control

    Abstract - The informal public transport sector in Sub- Saharan Africa is responsible for transporting the overwhelming majority of the workforce. Often, passengers have to wait for hours for taxis to coincidentally pass by to pick them up, making the transport mode notoriously inefficient. Despite its relevance and impact, the sector is afforded little attention in terms of regulation, development and organization, giving rise to a complex and inefficient system that affects millions of people. In fact, little is known about the industry. To advance understanding of this system, minibus taxis were equipped with tracking devices in this study. Tracking data was then used to develop a model that describes the transport network - essentially finding patterns in the apparent chaos for the potential benefit of its users. The adaptive model uses unsupervised learning to predict the informal stages in the city and provide travelers with intelligence on the best time and place to get transport, thereby reducing the waiting time at the taxi rank and the informal roadside stops. Experimental results show 70.4% model accuracy in dynamically learning the taxi behavior and accurately predicting the best places to get taxis at a given time of the day. Index Terms: Adaptive Model, Informal Transport Sector, Transport Prediction, Intelligent Transport Systems.

Intelligent vehicle trajectory tracking exhibits problems such as low adaptability, low tracking accuracy, and poor robustness in complex driving environments with uncertain road conditions. Therefore, an improved method of adaptive model predictive control (AMPC) for trajectory tracking was designed in this study to increase the corresponding tracking accuracy and driving stability of intelligent vehicles under uncertain and complex working conditions. First, based on the unscented Kalman filter, longitudinal speed, yaw speed, and lateral acceleration were considered as the observed variables of the measurement equation to estimate the lateral force of the front and rear tires accurately in real time. Subsequently, an adaptive correction estimation strategy for tire cornering stiffness was designed, an AMPC method was established, and a dynamic prediction time-domain adaptive model was constructed for optimization according to vehicle speed and road adhesion conditions. The improved AMPC method for trajectory tracking was then realized. Finally, the control effectiveness and trajectory tracking accuracy of the proposed AMPC technique were verified via co-simulation using CarSim and MATLAB/Simulink. From the results, a low lateral position error and heading angle error in trajectory tracking were obtained under different vehicle driving conditions and road adhesion conditions, producing high trajectory-tracking control accuracy. Thus, this work provides an important reference for improving the adaptability, robustness, and optimization of intelligent vehicle tracking control systems.

Adaptive estimation-based hierarchical model predictive control methodology for battery active equalization topologies: Part II - equalizer control

Adaptive internal model control: H2 optimization for stable plants

This article proposes adaptive internal model controls for the collocated output regulation of a flexible wing, where distributed disturbances, boundary disturbances, and references are from an exactly unknown exosystem. Observer-based tracking error feedback controls are first designed to address the robust output regulation in case of a known exosystem matrix. If the exosystem has an unknown matrix, an adaptive observer is further proposed with the observer error system converging to zero exponentially. Then, we can obtain adaptive observer-based controls by combining adaptive observers and observer-based controls, which are able to regulate the tracking errors toward zero in case of the exactly unknown disturbances and references. The corresponding closed-loop system is proved to be internally asymptotically stable. A simulation example is further provided for adaptive internal model control of the wing system.

A continuous-time decentralized adaptive internal model controller is proposed for the control of an N/spl times/N MIMO system with unknown parameters. By treating the MIMO system as an interconnection of N linear SISO subsystems with weak interactions, robust adaptive internal model controllers are designed for each isolated subsystem. Each local adaptive control law utilizes a normalizing signal which is generated using the local output and the inputs of all the subsystems. This necessitates the exchange of input signals between the different subsystems so that the scheme is no longer totally decentralized. This, however, does not pose any problem in industrial process control applications, where all the signals are anyway brought to or originate from a central control room. An additional feature which makes the scheme very attractive is the fact that its computational complexity is about the same as that of a totally decentralized one.

In this article, a nonlinear disturbance observer‐based multiple‐model adaptive explicit model predictive control (NDO‐MMAEMPC) scheme is developed for a nonlinear MIMO system with parametric uncertainty as well as an external disturbance. The proposed method manages external disturbances without affecting the degree of smoothness of the control signals. Further, to cope with the unknown system uncertainty, blending‐based adaptive identification schemes are used for the same class of systems. For each identification model, an explicit nonlinear model predictive controller is computed off‐line for the corresponding model in advance, which saves computation power during operation. The generated control inputs from the set of explicit controllers are being blended online using adaptive weight. An extended Kalman filter algorithm is employed for the estimation of the unavailable states of the nonlinear system. Using an aerodynamic laboratory setup, the effectiveness of the proposed control algorithm is verified.

Pitch control is related to the efficient energy conversion and safe operation of wind turbine, which is a challenging control issue because of the highly nonlinear dynamics of wind turbine, system constraints and stochastic environmental disturbances, etc. In this article, we propose the adaptive model predictive pitch control strategy based on linear parameter varying (LPV) model to solve the pitch control issue of large-scale wind turbine. Firstly, a high-fidelity LPV model is constructed to approximate the high-nonlinearity dynamics of wind turbine. The gap metric theory is introduced to optimize the selection of local models. Based on the proposed LPV model, an adaptive model predictive controller with linear time-varying Kalman filter is designed for large-scale wind turbine. The benchmark 5 megawatt (MW) wind turbine model provided by FAST is employed to evaluate the controller performance. The results show that, compared with the traditional gain scheduling proportional–integral control strategy, the proposed control strategy can achieve better power regulation and load reduction performance under gust, step and turbulent wind.