Online Adaptation Of Parameters Research Articles

Obstacle avoidance shape control of deformable linear objects with online parameters adaptation based on differentiable simulation

The manipulation of deformable linear objects (DLOs) such as ropes, cables, and hoses by robots has promising applications in various fields such as product assembly and surgical suturing. However, DLOs are more difficult to manipulate than rigid objects because their shape changes during manipulation. Furthermore, preventing a DLO from colliding with the environment is important to prevent it from becoming entangled and causing shape control to fail. In this paper, we proposed an obstacle avoidance and shape control scheme for DLOs based on differentiable simulation that does not require prior data or a specialized controller. First, we established a dynamic model of the DLO that allows for both forward dynamics transfer and error backpropagation to obtain gradients. Then, we employed model predictive control to optimize the embedded neural network for predicting the actions that would manipulate the DLO. Finally, the control scheme was made applicable to DLOs with different material properties by allowing online adaptation of the model parameters essential to deformation during manipulation. Simulations and real-world experiments demonstrate that the proposed control scheme could manipulate the DLO stably and accurately to avoid obstacles and achieve the goal state. In addition, the online adaptation of parameters helped mitigate the sim-to-real gap.

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.

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.

Deterministic learning-based sensor fault detection, isolation and accommodation of nonlinear systems

This paper presents a systematic deterministic learning-based rapid sensor fault detection, isolation and accommodation (SFDIA) scheme for a class of nonlinear systems with unmodeled output dynamics. First, locally accurate approximations of sensor faults and unmodeled output dynamics are achieved by using a deterministic learning-based neural network observer. Second, a sensor fault pattern bank which stores the knowledge of sensor faults and unmodeled output dynamics is established. A set of observers utilising the learned knowledge are constructed to generate residuals for rapid SFDIA. The contributions are: (1) the fault detectability and isolability conditions are rigorously analysed and verified in the simulation; (2) using the proposed fault accommodation approach, compared with online parameter adaptation techniques, better tracking performance can be achieved in the presence of sensor faults. Simulation results show the effectiveness of the proposed method.

Offset-Free Quasi-Infinite Horizon Nonlinear Model Predictive Controller Design Using Parameter Adaptation

Abstract This paper proposes an offset-free Quasi-Infinite Horizon Nonlinear Model Predictive Controller (QIH-NMPC) using online parameter adaptation. In the proposed method, the adaptation law is modeled by a first order differential equation as a function of the tracking error and subsequently combined with a QIH-NMPC algorithm for online updating of the unknown parameter. The effectiveness of the proposed control scheme is demonstrated on a continuous stirred tank reactor (CSTR) and an experimental cascaded three-tank system with uncertain model parameters, structural plant/model mismatch and noisy measurements. For the purpose of comparison, the state-of-the-art online state and parameter estimators such as moving horizon estimation (MHE) and extended Kalman filter (EKF) were also incorporated into QIH-NMPC algorithm. The simulation and experimental results obtained showed the efficacy of the proposed adaptation scheme as it demonstrated a comparable performance to standard estimators (MHE and EKF) although with a lesser computational time.

Development of an Online Adaptive Parameter Tuning vSLAM Algorithm for UAVs in GPS-Denied Environments.

In recent years, unmanned aerial vehicles (UAVs) have been applied in many fields owing to their mature flight control technology and easy-to-operate characteristics. No doubt, these UAV-related applications rely heavily on location information provided by the positioning system. Most UAVs nowadays use a global navigation satellite system (GNSS) to obtain location information. However, this outside-in 3rd party positioning system is particularly susceptible to environmental interference and cannot be used in indoor environments, which limits the application diversity of UAVs. To deal with this problem, in this paper, a stereo-based visual simultaneous localization and mapping technology (vSLAM) is applied. The presented vSLAM algorithm fuses onboard inertial measurement unit (IMU) information to further solve the navigation problem in an unknown environment without the use of a GNSS signal and provides reliable localization information. The overall visual positioning system is based on the stereo parallel tracking and mapping architecture (S-PTAM). However, experiments found that the feature-matching threshold has a significant impact on positioning accuracy. Selection of the threshold is based on the Hamming distance without any physical meaning, which makes the threshold quite difficult to set manually. Therefore, this work develops an online adaptive matching threshold according to the keyframe poses. Experiments show that the developed adaptive matching threshold improves positioning accuracy. Since the attitude calculation of the IMU is carried out based on the Mahony complementary filter, the difference between the measured acceleration and the gravity is used as the metric to online tune the gain value dynamically, which can improve the accuracy of attitude estimation under aggressive motions. Moreover, a static state detection algorithm based on the moving window method and measured acceleration is proposed as well to accurately calculate the conversion mechanism between the vSLAM system and the IMU information; this initialization mechanism can help IMU provide a better initial guess for the bundle adjustment algorithm (BA) in the tracking thread. Finally, a performance evaluation of the proposed algorithm is conducted by the popular EuRoC dataset. All the experimental results show that the developed online adaptive parameter tuning algorithm can effectively improve the vSLAM accuracy and robustness.

Online adaptive parameter identification of an unmanned surface vehicle without persistency of excitation

This paper is concerned with the parameter identification of an unmanned surface vehicle (USV) with fully unknown coefficients. An online adaptive parameter identification method is proposed for the USV to identify its model parameters without the condition of persistence of excitation. Specifically, a composite adaptive update law is developed based on a truncated integral filtered regressor, such that the model parameters can be estimated online, adapting to possible parameter changes. Then, an extended state observer is used to monitor the total estimation errors caused by environmental disturbances and parameter estimation errors. A salient feature of the proposed method is that the acceleration information is not required and only linear velocities and yaw rate are used for identification. Moreover, the convergence of the estimation errors is assured under the condition of interval excitation only. The stability of the online parameter identification method is proved by Lyapunov stability analysis. Simulations and experiments are carried out to demonstrate the effectiveness of the proposed online identification method for the USV.

A novel lumped thermal characteristic modeling strategy for the online adaptive temperature and parameter co-estimation of vehicle lithium-ion batteries

Accurate modeling of thermal characteristics is critical to the safe use and reliable management of lithium-ion batteries. However, limitations in sensors and testing methods make online real-time acquisition of internal temperatures extremely difficult. This paper uses the similarity of dynamic system modeling to construct a lumped thermal characteristic model of the battery. By analyzing the heat conduction mechanism inside the battery, the optimized heat path model is combined with the classical Bernardi equation to realize the state description of the battery thermal characteristic system. In addition, the forgetting factor recursive least squares algorithm is used to realize the online identification of the parameters of the lumped thermal characteristics model. Meanwhile, the identification of the external thermal resistance is coupled with the estimation of the internal temperature, and a novel online adaptive co-estimation strategy based on the forgetting factor recursive least squares — joint Kalman filter is proposed, which solves the problem that the external thermal resistance cannot be accurately identified adaptively in a complex environment. The experimental results show that the maximum root-mean-square error of the model under different experiments is 0.53 °C, which verifies the high-accuracy of the lumped thermal characteristics modeling strategy.

Advanced Control With PLC—Code Generator for aMPC Controller Implementation and Cooperation With External Computational Server for Dealing With Multidimensionality, Constraints and LMI Based Robustness

The manufacturers of Programmable Logic Controllers (PLC) usually equip their products with extremely simple control algorithms, such as PID and on-off regulators. However, modern PLCs have much more efficient processors and extensive memory, which enables implementing more sophisticated controllers. The paper discusses issues related to the implementation of matrix operations, time limitations for code execution within one PLC cycle, and memory requirements. The adaptive Model Predictive Controller (aMPC) algorithm is selected for predictive control with on-line adaptation of model parameters. The combination of predictive and adaptive properties in the regulator enables control of many industrial objects for which PID control is ineffective, e.g. nonstationary plants with time-varying delays. The presented generic approach consists in developing a C++ application for desktop PC that generates, based on user provided parameters - such as MPC horizons, the code in Structured Text (ST) language compliant with the IEC-61131 standard for PLCs. Despite the enhanced capabilities of programmable controllers, there are limitations to this platform that cannot be overcome. The implementation of optimization-based control algorithms requires cooperation with an external computing server. In the article, the PLC/external computer cooperation is used to implement the control with constraints taken into account. The robust control using the Linear Matrix Inequalities (LMI) for a multivariable plant is also presented. A number of tests were carried out to verify the correctness of implementation of this control in software-in-the-loop and hardware-in-the-loop structures.

On-line adaptive asynchronous parameter identification of lumped electrical characteristic model for vehicle lithium-ion battery considering multi-time scale effects

The accurate modeling of lithium-ion batteries is extremely important to improve the reliability of battery management systems, and solving the problem of multi-time scales is extremely beneficial for high-accuracy battery modeling and adaptive asynchronous parameter identification. This paper distinguishes the fast and slow change characteristics of the model resistor-capacitor link parameters, a strong applicability model for the aggregate electrical characteristics of vehicle-mounted lithium-ion batteries based on multi-time scales is established. By combining the advantages of different identification algorithms, an adaptive asynchronous parameter identification strategy is proposed, which solves the problem of data saturation caused by the time scale identification strategy. Then, the complex charge-discharge pulse and the mixed discharge pulse tests are designed explicitly, and the parameter results and terminal voltage tracking effects under different identification strategies are compared. Moreover, the consistency results of the parameter identification test under single-time scale forgetting factor recursive least squares and multi-time scale adaptive asynchronous parameter identification strategy are analyzed. The results show that under different working conditions, the identification precision of the terminal voltage based on the adaptive asynchronous parameter identification strategy is increased by 0.420% and 1.114% respectively, and the maximum error of parameter consistency is reduced by 158.300%. • An adaptive online identification strategy for asynchronous parameters is proposed. • Different dynamic characteristics are characterized by different time scales. • Realized the hysteresis effect modeling of the battery open-circuit voltage. • The hysteresis effect equation is coupled iteratively with the state-space equation. • Combine FFRLS and EKF to complete the asynchronous identification of parameters.

A New Neural Network Based Sliding Mode Adaptive Controller for Tracking Control of Robot Manipulator

In this paper a New RBF Neural Network based Sliding Mode Adaptive Controller (NNNSMAC) for Robot Manipulator trajectory tracking in the presence of uncertainties and disturbances is introduced. The research offers a learning with minimal parameter (LMP) technique for robotic manipulator trajectory tracking. The technique decreases the online adaptive parameters number in the RBF Neural Network to only one, lowering computational costs and boosting real-time performance. The RBFNN analyses the system's hidden non-linearities, and its weight value parameters are updated online using adaptive laws to control the nonlinear system's output to track a specific trajectory. The RBF model is used to create a Lyapunov function-based adaptive control law. The effectiveness of the designed NNNSMAC is demonstrated by simulation results of trajectory tracking control of a 2 dof Robotic Manipulator. The chattering effect has been significantly reduced.

An adaptive learning and control framework based on dynamic movement primitives with application to human–robot handovers

Object handover is a fundamental skill needed in many human–robot collaboration tasks ranging from industrial manipulation to daily service. It remains challenging for robots to perform a handover as flexibly and fluently as a human. This article proposes a framework based on Dynamic Movement Primitives (DMP) that enables robot to learn from human demonstrations and transfer the skill into human–robot handovers. In particular, we focus on the problem of dealing with time varying handover locations. Compared to the conventional DMP formalism, the proposed method contains the following extensions: (1) uncertainty-aware learning with Gaussian Process, (2) a weighting function to control the transition of the shape and goal attraction terms, (3) an orientation-based spatial scaling, (4) online parameter adaption with human feedback. Moreover, inspired by the principle of cooperative DMPs, we present an equivalent model to study the interactive dynamics in human–robot handovers. The proposed framework has been validated in experiments and evaluated by both subjective and objective metrics. Results show an enhancement of success rate, fluency and human comfort.

Adaptive neural fault-tolerant control for course tracking of unmanned surface vehicle with event-triggered input

This study investigates the course-tracking problem for the unmanned surface vehicle in the presence of constraints of the actuator faults, control gain uncertainties, and environmental disturbance. A novel event-triggered robust neural control algorithm is proposed by fusing the robust neural damping technique and the event-triggered input mechanism. In the algorithm, no prior information of the system model about the unknown yawing dynamic parameters and unknown external disturbances is required. The transmission burden between the controller and the actuator could be relieved. Moreover, the control gain-related uncertainties and the unknown actuator faults are compensated through two updated online adaptive parameters. Sufficient effort has been made to verify the semi-global uniform ultimate bounded stability for the closed-loop system based on Lyapunov stability theory. Finally, simulation results are presented to illustrate the effectiveness and superiority of the proposed algorithm.

Direct Flux Vector Control of Synchronous Motor Drives: A Small-Signal Model for Optimal Reference Generation

A novel direct flux vector control (DFVC) scheme is presented based on the real time use of the motor small-signal model for optimal reference generation without preprocessed look-up tables (LUTs). The control scheme is valid for reluctance- and PM-synchronous machines. The stator flux magnitude and the load angle are the controlled variables and the optimal reference values respecting maximum torque per ampere (MTPA), maximum torque per volts (MTPV), voltage, and current limit conditions are computed in real time from the small-signal model. Analytical expressions of MTPA and MTPV criteria are derived to enable online adaptation according to the small-signal approximation of the motor model. The motor parameters reside in the flux-map LUTs used in the flux observer; besides that, no additional tables are necessary. Furthermore, online parameter adaptation is proposed to further improve torque tracking accuracy against flux-map LUTs errors. The feasibility of the proposed scheme is demonstrated through experiments on a 1.1-kW synchronous reluctance (SyR) machine test-bench. The proposed control scheme simplifies the implementation and calibration of the DFVC, while improving its MTPV control and its roughness against model parameter errors. Prospective fields of application are spindle and traction drives.

Robust Adaptive Control Barrier Functions: An Adaptive and Data-Driven Approach to Safety

A new framework is developed for control of constrained nonlinear systems with structured parametric uncertainty. Forward invariance of a safe set is achieved through online parameter adaptation and data-driven model estimation. The new adaptive data-driven safety paradigm is merged with a recent adaptive controller for systems nominally contracting in closed-loop. This unification is more general than other safety controllers as contraction does not require the system be invertible or in a particular form. The method is tested on the pitch dynamics of an aircraft with uncertain nonlinear aerodynamics.

Vehicle Dynamics Control Using Model Predictive Control Allocation Combined with an Adaptive Parameter Estimator

Advanced passenger vehicles are complex dynamic systems that are equipped with several actuators, possibly including differential braking, active steering, and semi-active or active suspensions. The simultaneous use of several actuators for integrated vehicle motion control has been a topic of great interest in literature. To facilitate this, a technique known as control allocation (CA) has been employed. CA is a technique that enables the coordination of various actuators of a system. One of the main challenges in the study of CA has been the representation of actuator dynamics in the optimal CA problem (OCAP). Using model predictive control allocation (MPCA), this problem has been addressed. Furthermore, the actual dynamics of actuators may vary over the lifespan of the system due to factors such as wear, lack of maintenance, etc. Therefore, it is further required to compensate for any mismatches between the actual actuator parameters and those used in the OCAP. This is done by combining the MPCA solver with an online adaptive parameter estimation (APE) algorithm. In this study, an advanced solution to the OCAP is proposed by combining MPCA with APE. This solution coordinates differential braking and active front steering (AFS) of a passenger vehicle, to stabilize the lateral and yaw motion. The simulation results indicate that the APE+MPCA combination effectively accounts for actuator dynamics and actuator parameter mismatches.

Extended-State-Observer-Based Adaptive Control of Electrohydraulic Servomechanisms Without Velocity Measurement

Velocity signal is difficult to obtain in practical electrohydraulic servomechanisms. Even though it can be approximately derived via numerical differentiation on position measurement, the strong noise effect will greatly deteriorate the achievable control performance. Hence, how to design a high-performance tracking controller without velocity measurement is of practical significance. In this paper, a practical adaptive tracking controller without velocity measurement is proposed for electrohydraulic servomechanisms. To estimate the unmeasurable velocity signal, an extended state observer (ESO) that also provides an estimate of the mismatched disturbance is constructed. The ESO uses the unknown parameter estimates updated by a novel adaptive law, which only depends on the actual position and desired trajectory. Moreover, the matched parametric uncertainty is also handled by online parameter adaptation and the matched disturbance is suppressed via a robust control law. The proposed ESO-based adaptive controller theoretically achieves an excellent asymptotic tracking performance when time-invariant modeling uncertainties exist. In the presence of time-variant modeling uncertainties, guaranteed transient performance and prescribed final tracking accuracy can also be achieved. The proposed control strategy bridges the gap between the adaptive control and disturbance observer-based control without using the velocity signal and preserves the performance results of both control methods while overcoming their practical performance limitations. Comparative experiments are performed on an actual servovalve-controlled double-rod hydraulic actuator to verify the superiority of the proposed control strategy.

A reinforcement learning based approach for on-line adaptive parameter extraction of photovoltaic array models

At present, most methods for the fault detection and diagnosis (FDD) of the photovoltaic (PV) array strongly rely on comparing the on-line measured electrical parameters with the modeled reference ones, which are challenging the on-line accuracy and time cost of the parameter extraction for modeling the current-voltage (I-V) curves of the PV array. In this paper, a reinforcement learning (RL) based approach for on-line adaptive parameter extraction of PV array models is proposed. The model parameters, including the ideality factor, series and shunt resistance, and the compensated irradiance for the uncalibrated pyranometer, are extracted. Corresponding environmental states, actions, rewards, and the entire framework for the on-line adaptive parameter extraction are reasonably designed and investigated. The annual experimental results verify that the proposed RL-based approach can obtain higher on-line accuracy for modeling the I-V curve of PV array with fast extraction speed, compared with the conventional meta-heuristic-based approach and the analytical approach for parameter extraction. The annual experimental results reveal that the proposed approach can guarantee the 50% probability for obtaining the root mean square error (RMSE) less than 0.1, and 90% probability for obtaining the RMSE less than 0.25. The average computational time cost of the proposed approach is approximate 38.12 ms. In addition, the annual trend of extracted model parameters is analyzed. The annual results also show that the series and shunt resistance have the inverse seasonal trend. Besides, the measurement error of the pyranometer can be identified statistically. The proposed RL-based approach can also be integrated with the presented on-line FDD method, which realizes the on-line training of RL agents and the FDD of PV array simultaneously.

An online and real-time adaptive operational modal parameter identification method based on fog computing in Internet of Things

A large number of smart devices make the Internet of Things world smarter. However, currently cloud computing cannot satisfy real-time requirements and fog computing is a promising technique for real-time processing. Operational modal analysis obtains modal parameters that reflect the dynamic properties of the structure from the vibration response signals. In Internet of Things, the operational modal analysis method can be embedded in the smart devices to achieve structural health monitoring and fault detection. In this article, a four-layer framework for combining fog computing and operational modal analysis in Internet of Things is designed. This four-layer framework introduces fog computing to solve tasks that cloud computing cannot handle in real time. Moreover, to reduce the time and space complexity of the operational modal analysis algorithm and support the real-time performance of fog computing, a limited memory eigenvector recursive principal component analysis–based operational modal analysis approach is proposed. In addition, by examining the cumulative percent variance of principal component analysis, this article explains the reasons behind the identified modal order exchange. Finally, the time-varying operational modal identification results from non-stationary random response signals of a cantilever beam whose density changes slowly indicate that the limited memory eigenvector recursive principal component analysis–based operational modal analysis method requires less memory and runtime and has higher stability and identification effect.

Practical Fixed-time Position Tracking Control of Permanent Magnet DC Torque Motor Systems

This article focuses on the position control problem for permanent magnet dc torque motor systems. An adaptive position tracking controller is proposed with online parameter adaptation. The position of the permanent magnet dc torque motor under the proposed strategy tracks the reference signal with fixed-time convergence. Different from previous fixed-time control methods, the approach proposed in this article can be implemented under two different modes with continuous transition, which facilitates the controller design in reducing the initial shock of the control signal. An adaptive law with smooth function is presented to counteract the disturbance and attenuate the chattering. Both numerical simulation and real-time experiment are given to verify the effectiveness of the proposed strategy.