Dual Adaptive Control Research Articles

Adaptive dual closed-loop trajectory tracking control for a wheeled mobile robot on rough ground

Adaptive dual closed-loop trajectory tracking control for a wheeled mobile robot on rough ground

Enhanced genetic algorithm through scattering media by searching-strategy optimization

Feedback-based wavefront shaping techniques are efficient approaches to compensate for multiple scattering and achieve desired focuses inside or through turbid media. In this paper, we introduced the negative linear (NL) hypothesis as the criterion to optimize the feedback-based optimization approaches, such as genetic algorithm (GA). This hypothesis reveals that the key to improving the parameters to their optima lies in acquiring maximum initial convergence speed and maintaining a negative linear decline thereafter. Based on the hypothesis, an efficient genetic algorithm based on searching-strategy optimization (GA-SSO), which applies dual adaptive control of mutation and fixed linear fitness scaling selection, was proposed to accelerate the convergence speed. The simulations and experiments confirmed that NL is a basic guide to optimize the parameters to the optima of feedback-based wavefront shaping techniques.

An adaptive dual‐controller on‐line efficiency optimization method of four‐switch buck‐boost converter for wide range application

SummaryIn order to achieve efficiency extremum of power conversion independent of parameter values and operational mode, an adaptive dual‐controller on‐line efficiency optimization method is presented in this paper. Firstly, based on tri‐modal analysis for four‐switch buck‐boost (FSBB) converter, efficiency extremum analysis is given and existed for such this converter. Then, adaptive dual controller on‐line efficiency seeking algorithm is shown to adaptive two variables for efficiency optimization and output voltage regulation. Furthermore, perturb and observe detector is described to realize control variables decoupling of dual‐controller and to prevent the instability of the whole system during the algorithm tuning process and to improve the algorithm speed of the dual controller. The proposed method is model independent and does not depend on the circuit parameter values. The detail implement of algorithm and performance analysis is given, and the experimental platform and experimental results are put forward to verify the effectiveness of the algorithm.

A dual adaptive robust control for nonlinear systems with parameter and state estimation

Stabilization and learning are imperative to the high-performance feedback control of nonlinear systems. A dual adaptive robust control (DARC) scheme is proposed for nonlinear systems with model uncertainties to achieve a desired level of performance. Only the output of the nonlinear system is accessible in this work, all the states and parameters are learned online. Firstly, the DARC uses the prior physical bounds of systems to design a discontinuous projection with update rate limits which confines the bounds of parameter and state estimation. Then robustness of the nonlinear system can be guaranteed by the deterministic robust control (DRC) method. Secondly, a dual adaptive estimation mechanism (DAEM) is developed to learn the unknown parameters and states of systems. One part of the DAEM is the bounded gain forgetting (BGF) estimator, which is developed to handle inaccurate parameters and parametric variations. The other is the adaptive unscented Kalman filter (AUKF) synthesized for state estimation. The AUKF contains a statistic estimator based on the maximum a posterior (MAP) rule to estimate the unknown covariance matrix. Finally, simulation results illustrate the effectiveness of the suggested method.

Dual Neural Network Control of a Hybrid Functional Electrical Stimulation Cycling System

Abstract Hybrid functional electrical stimulation (FES) cycling is a method to rehabilitate people with neurological conditions when they are not in and of themselves capable of fully controlling their extremities. To ensure smooth cycling and adequate stimulation to accomplish the rehabilitation task, admittance control is applied between the human and the robotic cycle. The cycle motor is actuated by a dual neural network control structure with an additional robust element tracking the admittance trajectory, while muscles are stimulated with a simple saturated robust controller. The dual neural network structure allows adaptation to separable functions of the dynamic system, in addition to shared adaptation through the admittance filter. A Lyapunov analysis shows that the admittance tracking controller is globally exponentially stable. A passivity analysis shows that the admittance system and cadence tracking error are output strictly passive. A combined analysis shows that the total system is passive. Experiments are performed on eight participants without neurological conditions, on 12 differing protocols including a robust controller for comparison, the addition of noise, and the addition or lack of stimulation. One participant with a neurological condition was evaluated on three different protocols, including a robust controller, a neural network controller, and a game-like mode where the participant was asked to track the trajectory as it appeared on a screen. Statistical analysis of the experiments show that the standard deviation of the tracking error is significantly improved with the adaptive dual neural network control addition when compared to the robust controller, in some instances reducing the magnitude by half.

Dual-adaptive energy management strategy design for fast start-up and thermal balance control of multi-stack solid oxide fuel cell combined heat and power system

Solid oxide fuel cell (SOFC) hybrid system offers several advantages, including rapid start-up, effectively mitigating thermal stress and system stability. In this study, a multi-stack SOFC battery hybrid (SBH) Combined Heat and Power (CHP) system is designed. A dual-adaptive control strategy for this multi-stack system is proposed. The assembly of each power generation module (PGM) in parallel is implemented and the reliability of the system is verified. The dynamic characteristics of the multi-stack SBH-CHP system and the effect of temperature on the system are investigated. The results show that the multi-stack system can achieve stability within 80 s. The transient current observed in the system reaches up to 40 A, which accounts for at least 25% of the current generated by the SOFC. The system exhibits robust and prompt dynamic response characteristics, which ultimately contribute to an extended operational lifespan. The proposed adaptive control method and adaptive energy management strategy have been proven to be effective approaches for addressing the issues associated with rapid start-up and over-temperature within the multi-stack SBH-CHP system.

Adaptive Dual Droop Control of MTDC Integrated Offshore Wind Farms for Fast Frequency Support

This paper proposes an adaptive dual droop control (ADDC) scheme, it can provide the disturbed asynchronous system with fast frequency support from both other undisturbed asynchronous systems, and voltage source converter-based multi-terminal direct current (VSC-MTDC) integrated offshore wind farms (OWFs). With conventional droop control at onshore converters, the DC voltage and power flow will change once frequency events occur, it will lead to frequency variations in other undisturbed systems. The proposed ADDC scheme firstly detects the disturbed and undisturbed systems, and then makes the undisturbed asynchronous system provide more frequency support power, while ensuring safe operation by settling the support power limitation and regulating the droop coefficients. Moreover, the offshore stations will estimate the onshore DC voltage as the control signal for fast frequency support. After that, the OWFs will recover their rotor speed with an asymptotic control scheme to reduce second frequency drop. Case studies are carried out on 3-terminal and 5-terminal test systems, and the Opal-RT real-time simulation platform, respectively. Different control schemes are compared, and the parameter uncertainty and noise disturbance are considered to illustrate the performance and effectiveness of the proposed ADDC scheme.

Analysis and design of control systems via parameter‐based approach

Analysis and design of control systems via parameter‐based approach

Development of adaptive dual predictive control schemes based on Wiener–Hammerstein models

Adaptive MPC schemes often employ local linear approximation of the system dynamics, which may not be adequate to capture the dynamic behavior of systems exhibiting strongly nonlinear dynamics. In particular, it is challenging to control a system exhibiting input multiplicity behavior at its optimum operating point as the steady-state gain matrix can smoothly become singular at the optimum operating point. In this work, with the aim of controlling systems with fading memory that exhibit such strongly nonlinear dynamics, a novel adaptive dual nonlinear MPC (ADNMPC) formulation is developed based on discrete-time block-oriented models. The block-oriented models are parameterized using the generalized orthonormal basis filters (GOBF). The nonlinear black-box model is further used to formulate a stochastic optimal control problem. A deterministic approximation to the stochastic optimal control problem is then systematically derived. Subsequently, an ADNMPC scheme is developed using the deterministic approximation as a basis. The efficacy of the proposed approach is demonstrated by simulating the problem of controlling a continuously operated fermenter system at its optimum operating point. Analysis of the simulation study shows that in contrast to the conventional nonlinear MPC (NMPC), the proposed ADNMPC scheme injects perturbation to the process whenever a model update is needed while meeting the control objectives. Moreover, with reference to a non-adaptive NMPC controller, the proposed ADNMPC schemes achieve better servo control performance while controlling the fermenter at the singular optimum operating point.

Adaptive dual model predictive control for linear systems with parametric uncertainties

Adaptive dual model predictive control (DMPC) for linear systems with constant parametric uncertainties is investigated in this paper. In particular, chance constraints on output and input are considered. The online set-membership identification and recursive least squares are utilized for shrinking the uncertain parameter set and getting the estimation point of the parameters. The dual effect represented by the parameters' prediction error is considered in the receding horizon optimization problem for easing the impact of the uncertainties and improving the performance simultaneously. Chance constraints on output are tackled by converting into convex alternatives. The output tracking problem is transformed into a quadratically constrained quadratic-programming (QCQP) problem, which is computationally tractable. A numerical example is provided to illustrate the effectiveness of the proposed method.

Adaptive dual control with online outlier detection for uncertain systems

This paper proposes an adaptive dual control with outlier detection that is robust to the occurrence of outliers in uncertain systems. Outliers occasionally exist in system process noise and observation noise, which could cause poor parameter estimation and degraded control performance of uncertain systems. For this reason, we devise an online outlier detection mechanism to filter the outliers so as to enhance the parameter estimation of uncertain systems. The devised mechanism makes decisions on outlier detection via the generated predicted regions where the newly arriving data is expected to locate, and the predicted regions are updated in real-time according to the historical data. The detection mechanism is integrated into the design of adaptive dual control, which is derived based on the bicriterial method. Compared with classical dual control merely considering uncertainty in input and output data stream, we are the first to include the uncontrollable excitations into the structure of dual control to fit practical scenarios, and this inclusion also provides an extensive cover on outliers to be detected. The improved performance of the proposed approach is verified using a mathematical model through one-time simulation and Monte Carlo simulations under different conditions, and we also evaluate our method in the control of fermentation sterilization process for more convincing results.

Dual Adaptive Neural Network Controller for Underactuated Systems

SUMMARYA new stable adaptive controller based on a neural network for underactuated systems is proposed in this paper. The control scheme has been developed for two underactuated systems as examples. The Furuta pendulum and the Inertia Wheel Pendulum (IWP) have been examined in this paper. The presented approach aims to address the control problem of the given system in swing up, stabilization, and disturbance rejection. To avoid oscillations, two adaptive neural networks (ANNs) are implemented. The first one is used to approximate the equivalent control online and the second one to minimize the oscillations.

Dual Adaptive Model Predictive Control with Disturbances

Abstract Model-based control requires good accuracy of model parameters to achieve high performance. Controller design under parametric uncertainties is therefore a challenging topic in control system engineering. One well known control design for uncertain systems is adaptive control. An adaptive controller has two tasks: process regulation and parameter learning. Dual control explores the trade-off between the two seemingly conflicting tasks. The control structure in an adaptive system consists of a model-based controller and a recursive update rule for parameter estimation. In this paper, the adaptive control framework consists of model predictive control for system regulation and recursive least squares for parameter estimation. The requirement for persistent excitation is shown to be necessary for systems with high-dimensional parameter space. Then, a dual formulation is proposed in an attempt to generate excitation signals while maintaining control performance in the adaptive control scheme. The algorithm is implemented and tested on a simulated SISO system.

Dual Adaptive Nonlinear Droop Control of VSC-MTDC System for Improved Transient Stability and Provision of Primary Frequency Support

This paper presents an adaptive nonlinear droop control (ANLDC) strategy for the voltage source converter (VSC) in multi-terminal high voltage direct current (MTDC) system, which enables the converters to provide primary frequency control for connected AC grids. The presented strategy inherits the concept of an emergency control approach. In normal condition when there is no significant deviation in RoCoF and DC voltage, it will be in inactive mode. The combination of two control techniques, nonlinear frequency-droop control, and nonlinear voltage-droop control results in a dual nonlinear controller that includes the advantages of both of them at the same time. The ANLDC strategy bears the advantage of the traditional droop control that is based on local measurements and there is no need for a communication system. The simulation results indicate that the AC/DC station with the proposed dual control strategy can improve simultaneously the primary frequency response and transient stability of the connected AC grid.

Distributed Adaptive Dual Control via Consensus Algorithm in the Energy Internet

This article investigates a distributed adaptive dual control that employs both consensus algorithm and improved equal incremental principle (IEIP), to guarantee the security operation while reducing the energy consumption of the energy Internet (EI). Since the security operation is a critical factor of the EI with the energy hub (EH), it is necessary for the EI to adjust the outputs of EHs and the system parameters, which have a great influence on the performance. The consensus-based control of the EI can resolve the energy-coupling issue and accurately share the electricity and heat loads power without requiring the information of the network parameters, which is hard to know. Meanwhile, the variations of system parameters caused by the droop action greatly impact the security operation of the EI. It can be adaptively recovered by executing the proposed control strategy. Furthermore, in order to reduce the energy consumption, the equipment in hubs should also be managed in view of the features of EH. The minimal loss of the energy can be achieved by utilizing the control of devices via the IEIP by considering the coupling characteristics of devices. The performance of the proposed method is demonstrated through numerical case studies.

Dual adaptive robust control for uncertain nonlinear active suspension systems actuated by asymmetric electrohydraulic actuators

This study investigates the vibration control issue of active suspension systems. Unlike previous results that neglect the actuator dynamics or consider the impractical symmetrical hydraulic cylinder model, this paper incorporates more reasonable asymmetric electrohydraulic actuator into active suspension system and derives its dynamic model. However, whether active suspension or electrohydraulic actuator suffers from nonlinearities (e.g. nonlinear spring, nonlinear damper and nonlinear actuator dynamics) and parameters uncertainties (e.g. the variations of sprung mass and hydraulic fluid’s bulk modulus as well as hydraulic cylinder original control volumes) , which were rarely synthetically considered in the existing researches.To address these issues, we develop a novel dual adaptive robust controller (ARC). An ARC is firstly designed for main-loop system for stabilizing the car body and improving ride comfort in the presence of nonlinearities and parameter uncertainties as well as road disturbances. In order to meet the constraints requirements of suspension system, the tunable parameters in main-loop control law are optimized by solving linear matrix inequality with kidney-inspired algorithm. Another ARC is further synthesized for sub-loop system to deal with the nonlinear and uncertain dynamics in electrohydraulic actuator for ensuring the force tracking performance. Meanwhile, the uncertain parameters are estimated online to compensate the model deviation. The terminal control law is able to guarantee the asymptotic stability of close-loop system within Lyapunov framework. Finally, the effectiveness and robustness of the proposed controller are demonstrated via excessive simulation experiments over different road conditions.

The dual control algorithm of nonlinear dynamic processes in the conditions of incomplete a priori information

The problem of controlling nonlinear dynamic systems under conditions of incomplete a priori information about the model of the differential equation of the process is considered. Nonparametric models are given in which the order of the difference equation of the dynamic process is refined based on the rule for distinguishing significant variables. According to this rule, only those variables are included in the nonparametric model for which the minimum bandwidth is minimal. Nonparametric algorithms of dual control of nonlinear objects are given. Control devices built on the basis of these algorithms perform not only the direct function of controlling the object, but also its study. The process of training the dual control system with active accumulation of information is analyzed. The results of a numerical study of the application of the nonparametric adaptive dual control algorithm are presented.

A dual adaptive fault-tolerant control for a quadrotor helicopter against actuator faults and model uncertainties without overestimation

This paper presents a dual adaptive fault-tolerant control strategy for a quadrotor helicopter based on adaptive sliding mode control and adaptive boundary layer. Within the proposed adaptive control strategy, both model uncertainties and actuator faults can be compensated without the knowledge of the uncertainty bounds and fault information. By virtue of the proposed adaptive control scheme, the minimum discontinuous control gain is adopted, which significantly reduces the control chattering effect. As compared to the existing adaptive sliding mode control schemes in the literature, larger actuator faults can be tolerated by employing the proposed control scheme while suppressing control chattering. Moreover, boundary layer is used to smoothen control discontinuity and further eliminate control chattering. Nevertheless, the choice of boundary layer thickness is a trade-off between system stability and tracking accuracy. By explicitly considering this fact, an adaptive boundary layer is developed and synthesized with the proposed adaptive control framework to ensure stability and tracking accuracy of the considered system. When the control parameter tends to be overestimated, the thickness of boundary layer can be appropriately adjusted to avoid control parameter overestimation. Simulation and experimental tests of a quadrotor helicopter are both conducted to validate the effectiveness of the proposed control scheme. Its advantages are demonstrated in comparison with a conventional adaptive sliding mode control scheme.

Dual Adaptive Model Predictive Controller Application to Vertical Roller Mill Process Used in the Cement Industry

Diversified operating conditions, input-output constraints, and parametric variations in the Vertical Roller Mill (VRM) make it to have complicated dynamics and closed-loop instability. Existing traditional controllers are not superlative and may lead to an uneven plant shutdown. Model predictive controller with adaptive models can track these parametric variations and ensure the plant’s smooth running, which has been addressed in this paper. Data from the real-time VRM is acquired, and correlation analysis is carried out, which illustrates the use of outlet temperature and differential pressure as the output variables with tensile pressure and booster fan speed as the input variables. The base model for VRM is identified using the selected variables by data-driven system identification methods. The fourth-order state-space model was found to be optimal in capturing the dynamic behavior of VRM. Dual Adaptive Model Predictive Controller (DAMPC) is designed to handle each output variable individually. The use of DAMPC minimizes the complexity involved in the on-line parametric estimation for higher-order models by distributing the control authority to different controllers. The performance of the proposed DAMPC is compared with the existing Proportional Integral (PI) controller and Model Predictive Controller (MPC). Simulation experiments for reference tracking and rejection of slowly varying internal disturbances by considering parametric variations are carried out. Results illustrate DAMPC provides lesser overshoot and faster settling time amidst parametric variations.

Dual Control for Stochastic Linear MIMO Systems With Parameter Uncertainty

This paper considers a dual control problem for stochastic linear MIMO (Multiple Input Multiple Output) systems with parameter uncertainty. A novel dual adaptive control law for MIMO systems is proposed. The design is based on the innovation of the dual control cost function, which was originally developed for conventional adaptive control of linear systems. However, the design process is modified and developed to cater to the stochastic MIMO case. This is a more challenging problem because the superposition of parameter uncertainty and the MIMO property makes the problem more intractable. As in all dual adaptive strategies, it leads to a control law that balances out the need for caution, due to parameter uncertainty, with the conflicting requirement of probing that acts to quickly reduce parameter uncertainty, which is the nature of dual control. The proposed control law has two parts. One reflects the goal of regulating the output, and the second reflects the ability to handle uncertain parameters. A learning factor is introduced to balance these two parts to obtain a control law that can be applied to the original system. In the simulation examples, the uncertain parameters can be estimated quickly and accurately for the unknown but constant case and the abrupt parameter case. Furthermore, it is shown that the novel dual control law is superior to other control strategies by comparing the performance of the cost function in a statistical sense.