ADAPTIVE PREDICTIVE CONTROL OF NEAR-SPACE VEHICLE USING FUNCTIONAL LINK NETWORK

A novel nonlinear adaptive control method is presented for a near-space hypersonic vehicle （NHV） in the presence of strong uncertainties and disturbances. The control law consists of the optimal generalized predictive controller （OGPC） and the functional link network （FLN） direct adaptive law. OGPC is a continuous-time nonlinear predictive control law. The FLN adaptive law is used to offset the unknown uncertainties and disturbances in a flight through the online learning. The learning process does not need any offline training phase. The stability analyses of the NHV close-loop system are provided and it is proved that the system error and the weight learning error are uniformly ultimately hounded. Simulation results show the satisfactory performance of the con- troller for the attitude tracking.

The prediction of projectile-target intersection for moving tank based on adaptive robust constraint-following control and interval uncertainty analysis

To improve the hit probability of tank at high speed,a prediction method of projectile-target intersection based on adaptive robust constraint-following control and interval uncertainty analysis is proposed.The method proposed provides a novel way to predict the impact point of projectile for moving tank.First,bidirectional stability constraints and stability constraint-following error are constructed using the Udwadia-Kalaba theory,and an adaptive robust constraint-following controller is designed considering uncertainties.Second,the exterior ballistic ordinary differential equation with uncertainties is integrated into the controller,and the pointing control of stability system is extended to the impact-point control of projectile.Third,based on the interval uncertainty analysis method combining Chebyshev polynomial expansion and affine arithmetic,a prediction method of projectile-target intersection is proposed.Finally,the co-simulation experiment is performed by establishing the multi-body system dynamic model of tank and mathematical model of control system.The results demonstrate that the prediction method of projectile-target intersection based on uncertainty analysis can effectively decrease the uncertainties of system,improve the prediction accuracy,and increase the hit probability.The adaptive robust constraint-following control can effectively restrain the uncertainties caused by road excitation and model error.

Application of Generalized Predictive Adaptive Control Algorithm in the Design of Missile Control System

To study the application of the generalized predictive adaptive control algorithm in missile control system, the algorithm is presented based on the recursive least square estimation, and a controller of the pitch channel of a missile is designed by using this algorithm. The simulations verify that the designed controller can meet the demands of the task well.

Adaptive State Feedback Predictive Control and Expert Control for a Delayed Coking Furnace

An adaptive state feedback predictive control （SFPC） scheme and an expert control scheme are presented and applied to the temperature control of a 1200 kt·a^-1 delayed coking furnace, which is the key equipment for the delayed coking process. Adaptive SFPC is used to improve the performance of temperature control in normal operation. A simplified nonlinear model on the basis of first principles of the furnace is developed to obtain a state space model by linearization. Taking advantage of the nonlinear model, an online model adapting method is presented to accommodate the dynamic change of process characteristics because of tube coking and load changes. To compensate the large inverse response of outlet temperature resulting from the sudden increase of injected steam of a particular velocity to tubes, a monitoring method and an expert control scheme based on heat balance calculation are proposed. Industrial implementation shows the effectiveness and feasibility of the proposed control strategy.

Adaptive and Predictive Control Strategies for Wind Turbine Systems: A Survey

The wind turbine(WT) is a renewable energy conversion device for transformation of kinetic energy from the wind to mechanical energy for subsequent use in different forms.This paper focuses on wind turbine control design strategies.The content is divided into the following parts: 1) An overview of the recent advances that have been made in the application of adaptive and model predictive control strategies for wind turbines. 2) Summarizes some important aspects of modeling of wind turbines for control studies. 3) Provides an outlook on the application of adaptive model predictive control for uncertain systems to stimulate new research interests for wind turbine systems. We provide an overall picture of the research results with evaluation of the merits/demerits.

Robust Adaptive Gain Higher Order Sliding Mode Observer Based Control-constrained Nonlinear Model Predictive Control for Spacecraft Formation Flying

This work deals with the development of a decentralized optimal control algorithm, along with a robust observer,for the relative motion control of spacecraft in leader-follower based formation. An adaptive gain higher order sliding mode observer has been proposed to estimate the velocity as well as unmeasured disturbances from the noisy position measurements.A differentiator structure containing the Lipschitz constant and Lebesgue measurable control input, is utilized for obtaining the estimates. Adaptive tuning algorithms are derived based on Lyapunov stability theory, for updating the observer gains,which will give enough flexibility in the choice of initial estimates.Moreover, it may help to cope with unexpected state jerks. The trajectory tracking problem is formulated as a finite horizon optimal control problem, which is solved online. The control constraints are incorporated by using a nonquadratic performance functional. An adaptive update law has been derived for tuning the step size in the optimization algorithm, which may help to improve the convergence speed. Moreover, it is an attractive alternative to the heuristic choice of step size for diverse operating conditions. The disturbance as well as state estimates from the higher order sliding mode observer are utilized by the plant output prediction model, which will improve the overall performance of the controller. The nonlinear dynamics defined in leader fixed Euler-Hill frame has been considered for the present work and the reference trajectories are generated using Hill-Clohessy-Wiltshire equations of unperturbed motion. The simulation results based on rigorous perturbation analysis are presented to confirm the robustness of the proposed approach.

Adaptive predictive functional control based on Takagi-Sugeno model and its application to pH process

In order to obtain accurate prediction model and compensate for the influence of model mismatch on the control performance of the system and avoid solving nonlinear programming problem,an adaptive fuzzy predictive functional control(AFPFC) scheme for multivariable nonlinear systems was proposed.Firstly,multivariable nonlinear systems were described based on Takagi-Sugeno(T-S) fuzzy models;assuming that the antecedent parameters of T-S models were kept,the consequent parameters were identified on-line by using the weighted recursive least square(WRLS) method.Secondly,the identified T-S models were linearized to be time-varying state space model at each sampling instant.Finally,by using linear predictive control technique the analysis solution of the optimal control law of AFPFC was established.The application results for pH neutralization process show that the absolute error between the identified T-S model output and the process output is smaller than 0.015;the tracking ability of the proposed AFPFC is superior to that of non-AFPFC(NAFPFC) for pH process without disturbances,the overshoot of the effluent pH value of AFPFC with disturbances is decreased by 50% compared with that of NAFPFC;when the process parameters of AFPFC vary with time the integrated absolute error(IAE) performance index still retains to be less than 200 compared with that of NAFPFC.

Adaptive Predictive Inverse Control of Offshore Jacket Platform Based on Rough Neural Network

The offshore jacket platform is a complex and time-varying nonlinear system, which can be excited of harmful vibration by external loads. It is difficult to obtain an ideal control performance for passive control methods or traditional active control methods based on accurate mathematic model. In this paper, an adaptive inverse control method is proposed on the basis of novel rough neural networks （RNN） to control the harmful vibration of the offshore jacket platform, and the offshore jacket platform model is established by dynamic stiffness matrix （DSM） method. Benefited from the nonlinear processing ability of the neural networks and data interpretation ability of the rough set theory, RNN is utilized to identify the predictive inverse model of the offshore jacket platform system. Then the identified model is used as the adaptive predictive inverse controller to control the harmful vibration caused by wave and wind loads, and to deal with the delay problem caused by signal transmission in the control process. The numerical results show that the constructed novel RNN has advantages such as clear structure, fast training speed and strong error-tolerance ability, and the proposed method based on RNN can effectively control the harmful vibration of the offshore jacket platform.

Adaptive Cascade Generalized Predictive Control

Cascade control is one of the most popular structures for process control as it is a special architecture for dealing with disturbances. However, the drawbacks of cascade control are obvious that primary controller and secondary controller should be tuned together, which influences each other. In this paper, a new Adaptive Cascade Generalized Predictive Controller (ACGPC) is introduced. ACGPC is a method issued from GPC and the inner and outer controllers of a cascade system are replaced by one cascade generalized predictive controller, where both loops model are updated by Recursive Least Squares method. Compared with existing methods, the new method is simpler and yet more effective. It can be directly integrated into commercially available industrial auto-tuning systems. Some examples are given to illustrate the effectiveness and robustness of the proposed method.

Finite Control Set Model Predictive Current Control of a Five-Phase PMSM with Virtual Voltage Vectors and Adaptive Control Set

This paper presents an improved finite control set model predictive current control(FCS-MPCC)of a five-phase permanent magnet synchronous motor(PMSM).First,to avoid including all the 32 voltage vectors provided by a two-level five-phase inverter into the control set,virtual voltage vectors are adopted.As the third current harmonics can be much reduced by virtual voltage vectors automatically,the harmonic items in the cost function of conventional FCS-MPCC are not considered.Furthermore,an adaptive control set is proposed based on voltage prediction.Best control set with proper voltage vector amplitude corresponding to different rotor speed can be achieved by this method.Consequently,current ripples can be largely reduced and the system performs much better.At last,simulations are established to verify the steady and transient performance of the proposed FCS-MPCC,and experiments based on a 2 kW five-phase motor are carried out.The results have validated the performance improvement of the proposed control strategy.

ADAPTIVE GENERALIZED PREDICTIVE CONTROL OFSWITCHED SYSTEMS

The problem of adaptive generalized predictive control which consists of output prediction errors for a class of switched systems is studied.The switching law is determined by the output predictive errors of a finite number of subsystems.For the single subsystem and multiple subsystems cases,it is proved that the given direct algorithm of generalized predictive control guarantees the global convergence of the system.This algorithm overcomes the inherent drawbacks of the slow convergence and large transient errors for the conventional adaptive control.

Global Convergence of Adaptive Generalized Predictive Controller Based on Least Squares Algorithm

Some papers on stochastic adaptive control schemes have established convergence algorithm using a least-squares parameters. With the popular application of GPC, global convergence has become a key problem in automatic control theory. However, now global convergence of GPC has not been established for algorithms in computing a least squares iteration. A generalized model of adaptive generalized predictive control is presented. The global convergebce is also given on the basis of estimating the parameters of GPC by least squares algorithm.

Adaptive Nonlinear Model Predictive Control Using an On-line Support Vector Regression Updating Strategy

The performance of data-driven models relies heavily on the amount and quality of training samples, so it might deteriorate significantly in the regions where samples are scarce. The objective of this paper is to develop an online SVR model updating strategy to track the change in the process characteristics efficiently with affordable computational burden. This is achieved by adding a new sample that violates the Karush–Kuhn–Tucker conditions of the existing SVR model and by deleting the old sample that has the maximum distance with respect to the newly added sample in feature space. The benefits offered by such an updating strategy are exploited to develop an adaptive model-based control scheme, where model updating and control task perform alternately.The effectiveness of the adaptive controller is demonstrated by simulation study on a continuous stirred tank reactor. The results reveal that the adaptive MPC scheme outperforms its non-adaptive counterpart for largemagnitude set point changes and variations in process parameters.

Model-Based Adaptive Predictive Control with Visual Servo of a Rotary Crane System

This paper investigated the implementation of an adaptive predictive controller using nonlinear dynamic echo state neural （ESN） model for a rotary crane system by the visual servo method. The control sequences within the control horizon were described using cubic spline interpolation to enlarge the predictive horizon. Verification of the proposed scheme in the face of exogenous disturbances and modeling error with inaccurate string length was demonstrated by both simulations and experiments.

Model Free Adaptive Predictive Control of Desulfurization Slurry pH Based on CPS Framework

In order to improve the slurry pH control accuracy of the absorption tower in the wet flue gas desulfurization process,a model free adaptive predictive control algorithm for the desulfurization slurry pH which is based on a cyber physical systems framework is proposed.First,aiming to address system characteristics of non-linearity and pure hysteresis in slurry pH change process,a model free adaptive predictive control algorithm based on compact form dynamic linearization is proposed by combining model free adaptive control algorithm with model predictive control algorithm.Then,by integrating information resources with the physical resources in the absorption tower slurry pH control process,an absorption tower slurry pH optimization control system based on cyber physical systems is constructed.It is turned out that the model free adaptive predictive control algorithm under the framework of the cyber physical systems can effectively realize the high-precision tracking control of the slurry pH of the absorption tower,and it has strong robustness.

Distributed model predictive control based on adaptive sampling mechanism

In this work,an adaptive sampling control strategy for distributed predictive control is proposed.According to the proposed method,the sampling rate of each subsystem of the accused object is determined based on the periodic detection of its dynamic behavior and calculations made using a correlation function.Then,the optimal sampling interval within the period is obtained and sent to the corresponding sub-prediction controller,and the sampling interval of the controller is changed accordingly before the next sampling period begins.In the next control period,the adaptive sampling mechanism recalculates the sampling rate of each subsystem’s measurable output variable according to both the abovementioned method and the change in the dynamic behavior of the entire system,and this process is repeated.Such an adaptive sampling interval selection based on an autocorrelation function that measures dynamic behavior can dynamically optimize the selection of sampling rate according to the real-time change in the dynamic behavior of the controlled object.It can also accurately capture dynamic changes,meaning that each sub-prediction controller can more accurately calculate the optimal control quantity at the next moment,significantly improving the performance of distributed model predictive control(DMPC).A comparison demonstrates that the proposed adaptive sampling DMPC algorithm has better tracking performance than the traditional DMPC algorithm.

Enhancing Safety in Autonomous Vehicle Navigation:An Optimized Path Planning Approach Leveraging Model Predictive Control

This paper explores the application of Model Predictive Control(MPC)to enhance safety and efficiency in autonomous vehicle(AV)navigation through optimized path planning.The evolution of AV technology has progressed rapidly,moving from basic driver-assistance systems(Level 1)to fully autonomous capabilities(Level 5).Central to this advancement are two key functionalities:Lane-Change Maneuvers(LCM)and Adaptive Cruise Control(ACC).In this study,a detailed simulation environment is created to replicate the road network between Nantun andWuri on National Freeway No.1 in Taiwan.The MPC controller is deployed to optimize vehicle trajectories,ensuring safe and efficient navigation.Simulated onboard sensors,including vehicle cameras and millimeterwave radar,are used to detect and respond to dynamic changes in the surrounding environment,enabling real-time decision-making for LCM and ACC.The simulation resultshighlight the superiority of the MPC-based approach in maintaining safe distances,executing controlled lane changes,and optimizing fuel efficiency.Specifically,the MPC controller effectively manages collision avoidance,reduces travel time,and contributes to smoother traffic flow compared to traditional path planning methods.These findings underscore the potential of MPC to enhance the reliability and safety of autonomous driving in complex traffic scenarios.Future research will focus on validating these results through real-world testing,addressing computational challenges for real-time implementation,and exploring the adaptability of MPC under various environmental conditions.This study provides a significant step towards achieving safer and more efficient autonomous vehicle navigation,paving the way for broader adoption of MPC in AV systems.

EXPERT SYSTEM FOR SINTER CHEMICAL COMPOSITION CONTROL BASED ON ADAPTIVE PREDICTION

In order to stabilize the sinter chemical composition,the expert system for composition control based on adaptive prediction has been developed on the basis of modern control theory and artificial intelligence.It has been verified by using real data at No.3 Sintering Plant of Anshan Iron and Steel Co.,and satisfactory results have been obtained.

Research on the Control Strategy of Micro Wind-Hydrogen Coupled System Based on Wind Power Prediction and Hydrogen Storage System Charging/Discharging Regulation

This paper addresses the micro wind-hydrogen coupled system,aiming to improve the power tracking capability of micro wind farms,the regulation capability of hydrogen storage systems,and to mitigate the volatility of wind power generation.A predictive control strategy for the micro wind-hydrogen coupled system is proposed based on the ultra-short-term wind power prediction,the hydrogen storage state division interval,and the daily scheduled output of wind power generation.The control strategy maximizes the power tracking capability,the regulation capability of the hydrogen storage system,and the fluctuation of the joint output of the wind-hydrogen coupled system as the objective functions,and adaptively optimizes the control coefficients of the hydrogen storage interval and the output parameters of the system by the combined sigmoid function and particle swarm algorithm(sigmoid-PSO).Compared with the real-time control strategy,the proposed predictive control strategy can significantly improve the output tracking capability of the wind-hydrogen coupling system,minimize the gap between the actual output and the predicted output,significantly enhance the regulation capability of the hydrogen storage system,and mitigate the power output fluctuation of the wind-hydrogen integrated system,which has a broad practical application prospect.

Adaptive Coordinated Path Tracking Control Strategy for Autonomous Vehicles with Direct Yaw Moment Control

It is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions.In this study,an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm.The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model.To adaptively adjust the priorities of path tracking accuracy and vehicle stability,an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function.An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions.To ensure vehicle stability,the sideslip angle,yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame.It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and largecurvature conditions.