Online Parameter Research Articles

Adaptive-Dynamic-Programming-Based Robust Control for a Quadrotor UAV with External Disturbances and Parameter Uncertainties

Thiswork addresses the trajectory-tracking-control problem for a quadrotor unmanned aerial vehicle with external disturbances and parameter uncertainties. A novel adaptive-dynamic-programming-based robust control method is proposed to eliminate the effects of lumped uncertainties (including external disturbances and parameter uncertainties) and to ensure the approximate optimal control performance. Its novelty lies in that two radial basis function neural network observers with fixed-time convergence properties were first established to reconstruct the lumped uncertainties. Notably, they tune only the scalar parameters online and have low computational complexities. Subsequently, two actor–critic neural networks were designed to approximate the optimal cost functions and control policies for the nominal system. In this design, two new actor–critic neural network weight update laws are proposed to eliminate the persistent excitation condition. Then, two adaptive-dynamic-programming-based robust control laws were obtained by integrating the observer reconstruction information and the nominal control policies. The uniformly ultimately bounded stability of the closed-loop tracking control systems was ensured using the Lyapunov methodology. Finally, numerical results are shown to verify the effectiveness and superiority of the proposed control scheme.

A controllable neural network-based method for optimal energy management of fuel cell hybrid electric vehicles

Neural Networks (NNs) can be used for energy management of hybrid vehicles, but they are hard to tune in inference to adapt to different driving conditions. To make the NN-based energy management strategy more flexible, this paper proposes a controllable NN for optimal energy management of fuel cell hybrid electric vehicles. Inspired by the equivalent factor in the Equivalent Consumption Minimization Strategy (ECMS), we introduce an adjustable target variable for the final state as an input to the NN-based strategy. During training, classification and regression networks with single-step and multi-step inputs are considered. An efficient shooting method and an adaptive method are then introduced to realize the precise control of the final state and online parameter adaptation. Simulations of the proposed method and the benchmarking method are carried out in different battery discharge modes. Results demonstrate that the proposed shooting neural classifier can achieve 99.7% fuel optimality of dynamic programming in a similar computational time to the shooting ECMS, and the proposed adaptive neural classifier can adapt to different driving conditions and has better fuel economy than the adaptive ECMS.

Adaptive Feed-Forward Control for Gust Load Alleviation on a Flying-Wing Model Using Multiple Control Surfaces

Based on measured gust information, a multi-input multi-output (MIMO) adaptive feed-forward control scheme for gust load alleviation (GLA) on a semi-span flying-wing aircraft using multiple control surfaces is proposed. In order to remedy weight drift and biased estimation problems that are commonly encountered in adaptive control, the circular leaky LMS (CLLMS) algorithm is employed, which utilizes gust measurement information, filtered reference signals, and error signals to update controller parameters online. The results demonstrate that good load reductions are achieved in both continuous and discrete gust environments. For instance, the designed GLA control system leads to an 80.72% reduction in the root-mean-square (RMS) values of wing-root bending moment in the Dryden gust environment and a 77.59% reduction of its maximum value in the 1-cos discrete gust condition. Based on the limited power of the actuator and the limited authority for control surface deflections when integrating GLA into the flight control system, a weight-updating algorithm with deflection angle and rate constraints on control surfaces is proposed. The simulation results show that the strict constraints on control surface deflections will degrade the GLA performance. Finally, the influence of the partial jamming fault of actuators on GLA performance is studied. It is found that good GLA performance can be preserved despite the degraded performance during the initial stage of the actuator jamming fault. This is due to the robustness brought about by multiple control surfaces and the adaptability of the control algorithm.

Reliable Accuracy of On-line Permanent Magnet Synchronous Machine Parameters’ Identification Process by Using a Meta-heuristic Optimization Algorithm

In wind energy systems, it is necessary to have a good knowledge of the used generator and its parameters. Despite the motor manufacturer’s specifications, accurately estimating the machine parameter reference values is critical for the perfect control system. In this context, off-line identification tests of a 3 kW permanent magnet synchronous machine were conducted to determine the mechanical and electrical parameters. In addition, this research exploits a meta-heuristic optimization technic dubbed grey wolf optimizer in the on-line parameter’s identification field. The estimate method is focused on the parameter’s estimation linked to a reference model. The rule is based on the cancelation of the current error among the measured and the associated estimated current. For each measurement, the grey wolf estimator adapts the reference model parameters until the model response and the accurate response be similar. All were implemented using the real-time interface of the DSpace DS1104 Controller card while using the ControlDesk graphical user interface and the Simulink/MATLAB environments. Results from hardware simulation confirm the efficacy and benefits of the suggested strategy.

Parameter Identification of a Permanent Magnet Synchronous Motor Based on the Model Reference Adaptive System with Improved Active Disturbance Rejection Control Adaptive Law

Aiming at the problem that online parameter identification, based on the Model Reference Adaptive System (MRAS), is easily affected by the high-frequency noise of the sensor, an improved MRAS, based on variable bandwidth linear Active Disturbance Rejection Control (ADRC) adaptive law, is proposed. The proposed ADRC adaptive law, including an extended state observer, exhibits good anti-disturbance ability while effectively tracking the error signal through adjusting the bandwidth of the controller according to the observation error. When the observation error is large, the bandwidth of the controller is increased to enhance the tracking accuracy; in the opposite situation, the bandwidth is reduced to improve the anti-disturbance performance. To illustrate feasibility, a stability analysis of the proposed ADRC adaptive law is carried out and some comparison and validation experiments are designed to identify the inductance and flux linkage of the permanent magnet synchronous motor (PMSM). The experimental results show that the proposed method has better anti-disturbance performance than the MRAS based on traditional Proportional Integral (PI) adaptive law or switched PI adaptive law.

Enhanced Position Estimation Based on Frequency Adaptive Generalized Comb Filter for Interior Permanent Magnet Synchronous Motor Drives

Accurate rotor position estimation is critical to ensure the high-performance operation of position sensorless interior permanent magnet synchronous motor (IPMSM) drives. However, the back electromotive force (BEMF) estimated by the model-based observers is usually nonideal, which may further degrade the position estimation accuracy. In this paper, a frequency adaptive generalized comb filter (FAGCF) with a quadrature phase-locked loop (PLL) is adopted to suppress the rotor position estimation errors by filtering out the dominant distortions in the BEMF, including the (6k±1)th harmonics caused by inverter nonlinearity and flux spatial harmonics, as well as the DC offset from the inaccurate current measurements. This proposed filter is constructed by multiple digital delay blocks and therefore has the advantage of easy implementation with less online parameters tuning effort. Meanwhile, a linearized model is derived for PLL parameter design, with which the stability of the FAGCF-PLL position estimation system can also be analyzed theoretically. The effectiveness of the proposed sensorless control strategy has been validated with the experimental results on a 1.5-kW IPMSM drive.

Automated Controller Calibration by Kalman Filtering

This paper proposes a method for calibrating control parameters. Examples of such control parameters are gains of PID controllers, weights of a cost function for optimal control, filter coefficients, the sliding surface of a sliding mode controller, or weights of a neural network. Hence, the proposed method can be applied to a wide range of controllers. The method uses a Kalman filter that estimates control parameters, using data of closed-loop system operation. The control parameter calibration is driven by a training objective, which encompasses specifications on the performance of the dynamical system. The performance-driven calibration method tunes the parameters online and robustly, is computationally efficient, has low data storage requirements, and is easy to implement making it appealing for many real-time applications. Simulation results show that the method is able to learn control parameters quickly, is able to tune the parameters to compensate for disturbances, and is robust to noise. A simulation study with the high-fidelity vehicle simulator CarSim shows that the method can calibrate controllers of a complex dynamical system online, which indicates its applicability to a real-world system. We also verify the real-time feasibility on an embedded platform with automotive-grade processors by implementing our method on a dSPACE MicroAutoBox-II rapid prototyping unit.

A High-Bandwidth Current Estimator With Self Tunning for Digital Buck Controller

In order to solve the low-cost problem of high-speed current sampling in digital buck controllers, a current estimation method based on the direct current resistance (DCR) current detection principle is proposed in this paper. The inductor current including direct current (DC) and alternating current (AC) is obtained by performing high-frequency digital filtering on the voltage across the inductor ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_L</i> ). Quantitative analysis of various factors affecting the accuracy of current estimation is carried out. For improving the estimated current accuracy, the multi-phase interleaved clock sampling technique and the algorithm of the average duty cycle in the filter cycle are proposed to improve the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V_L</i> accuracy in each filter cycle. Meanwhile, considering the completeness of the current estimation algorithm, a calibration mechanism with a known current sink to adjust filter parameters online is adopted for the change of the inductance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> ) and its equivalent impedance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_L</i> ) due to process and aging. The proposed algorithm is verified in a buck prototyping board. Experimental results are presented to show that the proposed method is effective in both fixed frequency and variable frequency control modes. The DC and AC accuracy of estimated current reach 5% and 2%, respectively. The estimated inductor current delay is within 100 ns.

Digital twin for electric vehicle battery management with incremental learning

The current Industry 4.0 revolution promotes the use of cyber–physical systems to enhance manufacturing and other industrial processes via automation, real-time analysis, etc. Data communication between individual systems plays an important role in this revolution’s success. As defined by researchers, Digital Twin is the digital representation of a physical system that enables predictive maintenance. Due to the increase in environmental pollution, battery-powered electric vehicles (EVs) are regarded as the urgent solution to internal combustion engines in the transportation business, despite obstacles such as safety concerns and range estimation. State of Health (SoH) and State of Charge (SoC) are two battery metrics that, when precisely anticipated, permit safer and longer battery use. Predicting these parameters online is computationally and financially expensive. Alternately, some of these factors could be predicted in the cloud rather than on the vehicle, hence cutting costs. Consequently, the EV business is one example where cloud-to-vehicle data connection saves total costs. A digital twin for an EV battery would aid in the estimate of battery parameters for predictive maintenance. This paper presents a Digital Twin paradigm for EV battery management in which SoH is predicted in the cloud and SoC is estimated on-vehicle. A continuous learning method is also proposed for forecasting SoH, whereas the Kalman filter is used to estimate SoC. The proposed framework predicts the SoH with a mean square error of 0.022.

Real-time state of charge and capacity estimations of vanadium redox flow battery based on unscented Kalman filter with a forgetting factor

The monitoring of the state of charge (SOC) and capacity of the vanadium redox flow battery (VRFB) is challenging due to the complex electrochemical reactions. In addition, the apparent nonlinearity and time-varying nature of the battery increase the difficulty of monitoring. Herein, we propose an unscented Kalman filtering approach with a forgetting factor, which considers the impact of the battery's historical states on the current state without remarkably increasing computational cost, to observe the battery SOC precisely. By accurately estimating the SOC, the exact capacity is acquired with the ampere-hour method, which has extreme precision requirements for the SOC. To implement the proposed method, an equivalent circuit model with a first-order branch is constructed to catch the VRFB dynamics without considering its intricate chemical behaviors. Moreover, the recursive least-squares approach is used to extract the model parameters online to improve model accuracy. Experiments were carefully conducted on a self-assembled VRFB with the constant current charge profile and the defined hybrid pulse discharge profile to validate the novel method under five charge-discharge cycles. The results denote that the proposed method enables faster estimation convergence conditions as well as realizes accurate high-fidelity monitoring of the SOC and capacity with higher stability after convergence under the same conditions than other methods.

A novel high-frequency resonance controllable pneumatic actuator and its high-precision motion trajectory tracking control

The strong nonlinearity and uncertainty of the pneumatic cylinder friction is one of the main reasons for the poor control accuracy of the pneumatic cylinder. For this reason, improving the friction characteristics of the pneumatic cylinder should contribute to the improvement of the pneumatic cylinder motion trajectory tracking accuracy. Reducing friction is a common method to improve the friction characteristics of pneumatic cylinders. In this paper, a novel longitudinal resonance friction-reducing pneumatic cylinder based on the principle of inverse piezoelectric effect is developed from the hardware perspective. Meanwhile, a promising adaptive robust controller (ARC) with online parameter identification and nonlinear robust control function is designed for the pneumatic position servo system from the software perspective. Finally, this paper focuses on exploring the possibility of further improving the accuracy by excitation of high-frequency resonance of the developed novel pneumatic actuator on the basis of the accuracy achievable at the software level (control algorithm). Experimental results show that friction reduction by means of high-frequency resonance can significantly improve the motion trajectory tracking accuracy of the pneumatic system. The maximum tracking error is reduced by 17.95% and 27.87% when tracking sinusoidal reference trajectories of 0.5 Hz and 0.25 Hz, respectively. The maximum tracking error is reduced by 15.79% when tracking a smooth square reference trajectory of 0.25 Hz. In addition, the designed controller is effective and robust to parameter variations and sudden perturbations.

I&I adaptive fault-tolerant safe control of safety-critical systems

This paper proposed an Immersion and Invariance (I&I) adaptive fault-tolerant safe control scheme for a class of safety-critical (SC) systems with parametric faults. The scheme combines I&I Adaptive Control Lyapunov Function (I&I aCLF) with I&I Adaptive Control Barrier Function (I&I aCBF) to shape the transient performance of constrained states. Forward invariance of a safe set is achieved through online parameter adaptation, and the parameter convergence is guaranteed through I&I adaptation law. The conservatism is reduced by both shaping transient performance and ensuring parameters convergence. A SC controller guaranteeing stability and safety is obtained by solving an optimization problem with safety constrain, and an analytical solution is provided to facilitate the calculation and execution of the SC controller. A numerical simulation is carried out to illustrate the effectiveness of the proposed method.

Parameter-Free State Estimation Based on Kalman Filter with Attention Learning for GPS Tracking in Autonomous Driving System.

GPS-based maneuvering target localization and tracking is a crucial aspect of autonomous driving and is widely used in navigation, transportation, autonomous vehicles, and other fields.The classical tracking approach employs a Kalman filter with precise system parameters to estimate the state. However, it is difficult to model their uncertainty because of the complex motion of maneuvering targets and the unknown sensor characteristics. Furthermore, GPS data often involve unknown color noise, making it challenging to obtain accurate system parameters, which can degrade the performance of the classical methods. To address these issues, we present a state estimation method based on the Kalman filter that does not require predefined parameters but instead uses attention learning. We use a transformer encoder with a long short-term memory (LSTM) network to extract dynamic characteristics, and estimate the system model parameters online using the expectation maximization (EM) algorithm, based on the output of the attention learning module. Finally, the Kalman filter computes the dynamic state estimates using the parameters of the learned system, dynamics, and measurement characteristics. Based on GPS simulation data and the Geolife Beijing vehicle GPS trajectory dataset, the experimental results demonstrated that our method outperformed classical and pure model-free network estimation approaches in estimation accuracy, providing an effective solution for practical maneuvering-target tracking applications.

Adaptive control for a class of high‐order nonlinear parameterized systems with multiple unknown control directions by switching linear controllers

SummaryIn this paper, the adaptive control is investigated by switching linear controllers for a class of nonlinear parameterized systems with multiple unknown control directions. Parametric uncertainties entering the state equations nonlinearly can be fast time‐varying or jumping at unknown time instants, the bounds of the parametric uncertainties are not required to know a priori and the multiple control directions are unknown. Our proposed adaptive controller is a switching type controller. First, sufficient conditions for designing an adaptive stabilizer are derived. Then, a switching‐type adaptive controller is designed, in which a linear controller with two undetermined design parameters to be tuned is recursively designed by adding a power integrator, and a switching mechanism is proposed to tune these parameters online for compensating the multiple unknown control directions and the unknown bounds of the parametric uncertainties. The undetermined design parameters are based on the upper bound estimation of the unknown parameters existing not only in the nonlinear functions but also in the control directions functions. The proposed adaptive controller globally asymptotically stabilizes the system in the sense that, for any initial conditions, the state converges to the origin while all the signals of the closed‐loop system are bounded. Finally, an example is given to illustrate the effectiveness of the proposed method.

Sequential Offloading for Distributed DNN Computation in Multiuser MEC Systems

This paper studies a sequential task offloading problem for a multiuser mobile edge computing (MEC) system. While most of the existing works consider static one-shot offloading optimization with fixed wireless channel conditions and fixed computational tasks, we consider a dynamic optimization approach, which embraces wireless channel fluctuations and random deep neural network (DNN) task arrivals over an infinite horizon. Specifically, we introduce a local CPU workload queue (WD-QSI) and an MEC server workload queue (MEC-QSI) to model the dynamic workload of DNN tasks at each WD and the MEC server, respectively. The transmit power and the partitioning of the local DNN task at each WD are dynamically determined based on the instantaneous channel conditions (to capture the transmission opportunities) and the instantaneous WD-QSI and MEC-QSI (to capture the dynamic urgency of the tasks) to minimize the average latency of the DNN tasks. The joint optimization can be formulated as an ergodic Markov decision process (MDP), in which the optimality condition is characterized by a centralized Bellman equation. However, the brute force solution of the MDP is not viable due to the curse of dimensionality as well as the requirement for knowledge of the global state information. To overcome these issues, we first decompose the MDP into multiple lower dimensional sub-MDPs, each of which can be associated with a WD or the MEC server. Next, we further develop a parametric online Q-learning algorithm, so that each sub-MDP is solved locally at its associated WD or the MEC server. The proposed solution is completely decentralized in the sense that the transmit power for sequential offloading and the DNN task partitioning can be determined based on the local channel state information (CSI) and the local WD-QSI at the WD only. Additionally, no prior knowledge of the distribution of the DNN task arrivals or the channel statistics will be needed for the MEC server. The proposed solution can achieve the superb performance over various state-of-the-art baselines.

Antisaturation fixed-time attitude tracking control based low-computation learning for uncertain quadrotor UAVs with external disturbances

External disturbances, uncertain parameters, asymmetric saturation input, and high computational burden can significantly damage the attitude tracking control performance of uncertain quadrotor unmanned aerial vehicles (UAVs). To accomplish the high-precision attitude tracking control, this study proposes an antisaturation fixed-time attitude tracking control based low-computation learning. Firstly, a fixed-time state observer is constructed to estimate the system states in fixed time. Secondly, by developing the fast fixed-time stable system with the time-varying gain function, a nonsingular fast fixed-time sliding mode surface is designed to improve the convergence speed and avoid the singularity problem. Thirdly, to solve the problem of asymmetric input saturation, an auxiliary compensation system is integrated to regulate the control inputs. Subsequently, an adaptive neural network (NN) technology is used to overcome the negative effects of external disturbances and uncertain parameters, where the designed adaptive mechanism is to adjust a virtual parameter online instead of the weight vector of the NN, which has the characteristics of low computational burden and simple structure. The Lyapunov-based stability analysis concludes that the closed-loop system is practical fixed-time stable and the tracking errors can converge to bounded regions around the origin in fixed time independently of the initial system states. Finally, comparative results are given to demonstrate that compared with the existing controllers, the controller developed in this study can achieve stronger robustness, faster convergence, and saturation elimination with lower error-index values.

A Robust Adaptive Control Strategy for Electro-hydraulic Servo Based on Friction Compensation

Background: The electro-hydraulic servo system has the advantages of high stiffness, fast response speed, high precision and power-to-weight ratio. Objective: In this patent, an adaptive robust control strategy based on friction compensation is proposed for insufficient control accuracy due to a large number of nonlinearities and uncertainties in the system. Methods: In this patent, the mathematical relationship between the physical quantities of the valve-controlled cylinder is analyzed. Then the mathematical model of valve controlled cylinder considering the main nonlinearity and uncertainty of the system is established. The fuzzy proportion-integral-derivative, deterministic robust, and adaptive robust single-cylinder trajectory tracking controllers are designed and the tracking performance of different reference trajectories is analyzed by MATLAB/Simulink software. Results: In this patent, all the three controllers can track the reference trajectories smoothly. Adaptive robust control strategy can make the uncertain parameters of system converge and approach truth value. Conclusion: In this patent, the adaptive robust control strategy has the function of online parameter adjustment. When tracking accuracy is taken as the index, its tracking effect is better than fuzzy proportion-integral-derivative and deterministic robust.

Parametric Modeling and Deep Learning for Enhancing Pain Assessment in Postanesthesia.

The problem of reliable and widely accepted measures of pain is still open. It follows the objective of this work as pain estimation through post-surgical trauma modeling and classification, to increase the needed reliability compared to measurements only. This article proposes (i) a recursive identification method to obtain the frequency response and parameterization using fractional-order impedance models (FOIM), and (ii) deep learning with convolutional neural networks (CNN) classification algorithms using time-frequency data and spectrograms. The skin impedance measurements were conducted on 12 patients throughout the postanesthesia care in a proof-of-concept clinical trial. Recursive least-squares system identification was performed using a genetic algorithm for initializing the parametric model. The online parameter estimates were compared to the self-reported level by the Numeric Rating Scale (NRS) for analysis and validation of the results. Alternatively, the inputs to CNNs were the spectrograms extracted from the time-frequency dataset, being pre-labeled in four intensities classes of pain during offline and online training with the NRS. The tendency of nociception could be predicted by monitoring the changes in the FOIM parameters' values or by retraining online the network. Moreover, the tissue heterogeneity, assumed during nociception, could follow the NRS trends. The online predictions of retrained CNN have more specific trends to NRS than pain predicted by the offline population-trained CNN. We propose tailored online identification and deep learning for artefact corrupted environment. The results indicate estimations with the potential to avoid over-dosing due to the objectivity of the information. Models and artificial intelligence (AI) allow objective and personalized nociception-antinociception prediction in the patient safety era for the design and evaluation of closed-loop analgesia controllers.

An adaptive control strategy for microgrid secondary frequency based on parameter identification

Complex microgrid structures and time-varying conditions, among other factors, cause problems in the mechanical modeling of microgrids, making model-based controller optimization difficult. Therefore, this study proposed a secondary frequency adaptive control strategy based on parameter identification, which uses an online parameter identification method to identify the parameters in the microgrid in real-time. The identified parameters are then used in the secondary frequency adaptive controller to optimize the real-time controller performance. The proposed method realizes adaptive optimization of the controller in the microgrid operation state and is applied to a microgrid with unknown parameters to adjust the controller parameters. Finally, a simulation experiment was conducted to verify the model accuracy and the frequency regulation effect of the proposed adaptive control strategy

State of Charge Estimation of Lithium-Ion Batteries Based on Vector Forgetting Factor Recursive Least Square and Improved Adaptive Cubature Kalman Filter

Accurate online parameter identification and state of charge (SOC) estimation are both very crucial for ensuring the operating safety of lithium-ion batteries and usually the former is a base of the latter. To achieve accurate and stable SOC estimation results, this paper proposes a model-based method, which incorporates a vector forgetting factor least square (VFFLS) algorithm and an improved adaptive cubature Kalman filter (IACKF). Firstly, considering it is difficult for the traditional forgetting factor recursive least square (FFRLS) algorithm to balance the accuracy, convergence, and stability for multiple parameters with different time-varying periods, an improved VFFLS method is employed to determine the multiple parameters of the first-order RC battery model online. It supersedes the single forgetting factor in the FFRLS with multiple forgetting factors in a vector form for improving adaptive capability to multiple time-varying parameters. Secondly, aiming at the fact that the standard cubature Kalman filter (CKF) cannot operate properly when the error covariance matrix is non-positive definite, which is caused by disturbance, initial error, and the limit of the computer word length, the UR decomposition rather than the Cholesky decomposition is applied, thus improving the algorithm stability. In addition, an adaptive update strategy is added to the CKF to enhance accuracy and convergence speed. Finally, comparative experiments with different operating patterns, positive and non-positive definite error covariance matrices, and temperatures are carried out. Experimental results showed that the proposed method can estimate the SOC accurately and stably.