Problem Of Parameter Uncertainty Research Articles

Maximum Power Point Tracking Control of Offshore Hydraulic Wind Turbine Based on Radial Basis Function Neural Network

A maximum power point tracking control strategy for an affine nonlinear constant displacement pump-variable hydraulic motor actuation system with parameter uncertainty, used within an offshore hydraulic wind turbine, is studied in this paper. First, we used the feedback linearization method to solve the affine nonlinear problem in the system. However, offshore hydraulic wind turbines have strong parameter uncertainty characteristics. This conflict was resolved through the further application of RBF neural network adaptive control theory. So, we combined feedback linearization with RBF adaptive control as the control theory, and then two control laws were compared by setting the pump rate and rating as outputs, respectively. It is shown by the MATLABR2016a/Simulink emulation results that power control is smoother than speed and friendlier for electric networks. It is also shown by the emulation results, in terms of the undulatory wind speed condition, that the feedback linearization–RBF neural network adaptive control strategy has perfect robustness. According to the simulation results, the feedback linearization–RBF neural network adaptive control strategy adopts the RBF neural network to approach complex nonlinear models and solve the parameter uncertainty problem. This control law also avoids the use of feedback linearization control alone, which can result in the system becoming out of control.

Self-tuning PID feedback control method for magnetic suspension active vibration isolation system with parameters uncertainty

A self-tuning PID (Proportion-Integral-Derivative) control method based on the adaptive mechanism was proposed for the magnetic suspension active vibration isolation system with the characteristics of nonlinear, time-varying, and model parameter uncertainty. The controller identified the controlled object parameters online and solved the controller parameters in real time. To test the effectiveness of the proposed method, a six degree-of-freedom (DOF) magnetic suspension active vibration isolation system was designed and built. The six DOF dynamic model of the vibration isolation system with uncertain model parameters was derived for control. Experiments were carried out and compared with the control effects of the tradition PID control and cascade PID control algorithm. The experimental results show that the proposed self-tuning PID controller has more outstanding practicality and effectiveness in solving the model parameter uncertainty problem compared with the existing studies at home and abroad, and it also proves that the control method has a good isolation effect on the wide band disturbances.

Hierarchical multi-objective optimization of proton exchange membrane fuel cell with parameter uncertainty

It is important to improve the performance indexes including the net power and system efficiency for polymer electrolyte membrane fuel cell (PEMFC), especially considering the parameter uncertainty. To this end, this paper proposes a hierarchical multi-objective optimization framework including the off-line and on-line optimization. Firstly, a steadystate non-linear PEMFC system model is established as the base model. Secondly, an adaptive multi-objective particle swarm optimization (AMPSO) algorithm scheme is proposed in the off-line optimization procedure for the exact optimization of operation parameters. Meanwhile, an adaptive flight parameter strategy based on the particle dispersity (PD) information is proposed to balance the convergence and diversity of Pareto solutions. Finally, to decrease the influence caused by parameter uncertainty, the interval optimization method is proposed in the on-line optimization layer based on the results of AMPSO. The convergence condition of the proposed optimization scheme is verified by theory analysis. The proposed AMPSO is compared with different algorithms in the numerical simulation and hardware-in-loop (HIL) experiments. Meanwhile, the performance of the proposed method is tested on four representative benchmark problems. These results demonstrate that the PEMFC system with the proposed optimization scheme performs better than the base model and classical optimization algorithms in terms of the net power and system efficiency indexes, revealing the success of this hierarchical optimization approach in solving the accurate optimization and parameter uncertainty problems. By using statistical test methods, the proposed algorithm performs better hypervolume (HV) metric from the Pareto solution distribution.

Current source converter-based optimal power extraction and power control of a doubly fed induction generator (DFIG) using backstepping control

An improved backstepping control technique for the operation of a doubly fed induction generator (DFIG) powered by a wind turbine (WT) was developed and discussed. It is derived from a current source converter (CSC). The optimal backstepping control (BSC) settings are determined using the CSC approach, which can improve the drive by providing a more rapid dynamic response, greater precision, and stable performance. The CSC approach control function was constructed using the index criterion integral time square error (ITSE) and integral time absolute error (ITAE). With the simulation technique in the MATLAB/Simulink environment, the proposed BSC-CSC effectiveness is confirmed, and analyse the performance by using the PI and SMC control. The results indicate the goals of this work have been achieved in the form of resilience, improved dynamic system efficiency, lowering the harmonic distortion, optimization of stator power, and successfully addressing the problem of model parameter uncertainty.

Application of adaptive global terminal sliding mode control based on limited error in high-speed train

High-precision tracking control is very important in high-speed train (HST) operation. This study proposes an adaptive global terminal sliding mode control scheme based on limited error (AEG-SMC) for trajectory tracking of HST. AEG-SMC is firstly proposed for the nonlinear dynamic system to improve the accuracy of tracking error and solve the problem of parameter uncertainty in the model. Then, AEG-SMC uses an adaptive method to approximate the uncertain parameters in the model to obtain real-time parameter estimation, and an error index function is given to realise fast and high-precision tracking of the system by global terminal sliding mode control (GSMC). AEG-SMC is able to design the required error indicators before conducting control, thereby ensuring the high-quality response and stability of the system. Finally, the simulation results show that the speed tracking error and control input of HST with AEG-SMC are also the smallest among the mentioned methods.

About PMSM Current Tracking Control with Parameter Uncertainty

In this work, the problem of parameter uncertainty of the Permanent Magnet Synchronous Motor (PMSM) model in the $dq$ reference frame is studied by implementing an adaptive control scheme. The starting point of this paper is to analyze the system structure and represent the system as an interconnection between the electric subsystem, which is a completely actuated system, and the mechanical subsystem which is not actuated. This decomposition allows the design of a current tracking control based on the Passivity Based Control (PBC) methodology for the electrical subsystem. The interconnection, which is power preserving, allows to consider the unactuated mechanical subsystem as a passive perturbation that is proven to be bounded. The numerical evaluation was done by a MATLAB Simulink simulation with a comparison that shows that the designed control is able to deal with the parameters uncertainties of the electrical subsystem.

Design of the electric vehicle powertrain systems based on μ-analysis

Aiming at the oscillation in the electric vehicle powertrain systems, a control strategy based on the structural singular value [Formula: see text] is proposed. To solve the problem of robustness under parameter uncertainty, the traditional [Formula: see text]-synthesis needs to obtain all-pass characteristics through the weight function in the nominal system, which reduces the perturbation suppression ability of the system. Unlike the previous use of [Formula: see text]-analysis as a system performance criterion, this paper starts from the essence of parameter uncertainty problems and designs controllers based on the relationship between parameter uncertainty and nominal systems, using structural singular value [Formula: see text] as a performance constraint. The result shows that the [Formula: see text]-analysis not only takes into account the robustness and disturbance rejection ability but also reduces the controller order. In addition, the oscillation suppression effect in the presence of transmission backlash is verified, and the [Formula: see text]-analysis can suppress the oscillation better than the [Formula: see text]-synthesis.

Adaptive Convex Optimization Guidance for Lunar Landing

In this paper, a novel guidance algorithm based on adaptive convex optimization is proposed to ensure robustness in the uncertainty of a lunar lander’s parameters and satisfy the constraints of terminal position, velocity, attitude, and thrust. To address the problem of parameter uncertainty in the landing process, the parameter-adaptive method is used to achieve online real-time optimal estimations of specific impulse and mass by the optimal observer, and the estimated parameters are used to realize optimal trajectory programming. To overcome the difficulty in constraining the attitude and the thrust level at the final stage in the convex optimization process, a rapid adjustment phase is added to meet the final attitude and thrust constraints; the target-adaptive method is used to adjust the target adaptively at the same time. Therefore, the position and velocity deviations caused by the rapid adjustment phase can be eliminated by the target offset. Finally, the results of numerical experiments demonstrate the effectiveness of the proposed algorithm.

Design of High Precision Interval Observer for Robot System

In order to solve the problem of parameter uncertainty and unknown external interference of wheeled mobile robots (WMR) in a complex environment, the design of a high-precision interval observer for the robot system is proposed. In this paper, the kinematics and dynamics model of a wheeled mobile robot is derived first, and then the control strategy of high-precision interval observer is introduced to estimate and compensate for the unknown state and uncertainty of the system in real-time, which realizes the robustness of the system to disturbance and high adaptability to the environment. The stability of the system is proved by Lyapunov’s theory. The experimental results show that other methods based on coordinate transformation, though the design conditions are relaxed to a certain extent, bring some conservatism. The method proposed in this paper can obtain more accurate interval estimation, so the performance of the method proposed in this paper is better. In conclusion, the control method proposed in this paper can make the mobile robot system have good tracking control performance and strong robustness.

Back-Analysis of Slope GNSS Displacements Using Geographically Weighted Regression and Least Squares Algorithms

Numerical simulation is a powerful technique for slope stability assessment and landslide hazard investigation. However, the physicomechanical parameters of the simulation results are susceptible to uncertainty. Displacement back-analysis is considered an effective method for the prediction of the geomechanical parameters of numerical models; therefore, it can be used to deal with the parameter uncertainty problem. In this study, to improve the interpretability of the back-analysis model, an analytical function relationship between slope displacements and physicomechanical parameters was established using geographically weighted regression. By combining the least-squares and linear-algebra algorithms, a displacement back-analysis method based on geographically weighted regression (DBA-GWR) was developed; in particular, the multi-objective displacement back-analysis was represented as an analytical problem. The developed method was subsequently used for a slope of the Guiwu Expressway in Guangxi, China. Simulation experiments and GNSS real-data experiments demonstrated that the GWR could achieve high-precision deformation modelling in the spatial domain with model-fitting precision in the order of mm. Compared with state-of-the-art methods, the precision of the simulated displacement with the proposed method was significantly improved, and equivalent physicomechanical parameters with higher accuracy were obtained. Based on the corrected numerical model, the most severely deformed profiles were forward-analysed, and the simulated deformation and distribution patterns were found to be in good agreement with the field investigation results. This approach is significant for the determination of geomechanical parameters and the accurate assessment of slope safety using monitoring data.

Research on a New Control System Based on L1 Adaptive Control Scheme for the Voice Coil Motor

With the continuous development of voice coil motors, it has also been widely used in today’s sports competitions. For example, the Hawk-Eye system in tennis matches uses voice coil motors to focus on the camera to capture the trajectory of the tennis ball. Therefore, in order to better solve the problem of dynamic parameter uncertainty, external load disturbance, and tracking control of the voice coil motor servo system in motion, this paper proposes for the first time the strategy of using L1 adaptive control algorithm to control the voice coil motor servo system. First, it briefly analyzes the working principle and control method of the voice coil motor, and then constructs the uncertain parameter model according to the mathematical model of the voice coil motor. The closed-loop system of voice coil motor is simulated and analyzed by L1 adaptive controller. The results show that the L1 adaptive control method effectively suppresses the high-frequency interference caused by the voice coil motor in operation, can control the tracking error to gradually converge and so the tracking effect is better, and has strong robustness to the perturbation of system parameters. It can be applied to voice coil motor servo control.

Multilayer Neural Dynamics-Based Adaptive Control of Multirotor UAVs for Tracking Time-Varying Tasks

To realize the robust control of multirotor unmanned aerial vehicle (UAV) systems, adaptive multilayer neural dynamics (AMND) controllers are proposed and analyzed. The proposed AMND controllers with the strong anti-perturbation property can drive multirotor UAVs to track time-varying tasks and deal with parameter uncertainty problems. First, the design method of the general multilayer neural dynamics (MLND) controllers is introduced and analyzed. Second, based on the design method, the attitude angles, height, and position controllers of a UAV system are designed. Third, according to the adaptive control theory, a novel AMND controller is designed, which can self-tune the parameters of the UAV. Finally, the proposed AMND method applies to a real-world hexrotor UAV system to illustrate its reliability. Mathematical analysis, computer simulations, and experiments verify the reliability, stability, and effectiveness of the proposed controllers which are used to track time-varying tasks.

Research on Position Control of an Electro–Hydraulic Servo Closed Pump Control System

A control strategy combining adaptive backstep sliding mode control and dead-zone inverse compensation control in a series was proposed to solve the problem of system parameter uncertainty and system dead-zone in the process of position control in an electro–hydraulic servo closed pump control system. Firstly, an adaptive backstepping sliding mode controller was designed by introducing the sliding mode control principle. Secondly, the smooth dead-zone inverse function was constructed by using the smooth continuous index function to design the dead-zone inverse compensation controller, which is combined with the adaptive sliding mode controller to form a series controller. Finally, the feasibility of the controller was verified by using the pump control servo system of a lithium battery pole strip mill. The experimental results show that, compared with traditional PID control, the control strategy displayed no excessive overshoot before the steady state, the steady-state control accuracy could reach ±0.002 mm, and the time required to reach the steady state was 1 s to 2 s shorter, which shows this method’s ability to effectively improve the position control accuracy of the pump control system, as well as its dynamic response performance.

A finite control set model predictive direct speed controller for PMSM application with improved parameter robustness

Model predictive control (MPC) is an advanced control method that relies on a mathematical model containing the motor parameters in the field of Permanent Magnet Synchronous Motor (PMSM) application. However, the internal parameters of the motor and external load characteristic parameters are uncertain, and their actual values ​​may be inconsistent with the nominal values, resulting in prediction errors, which in turn affect the performance of the control algorithm. Based on the analysis of the parameter sensitivity of the motor prediction model, traditional finite control set model predictive direct speed controller (FCS-MPDSC) predicts state parameters based on a mathematical model, and there is a problem that the parameters of the motor model are uncertain, which will lead to prediction errors. In order to improve the robustness of FCS-MPDSC parameters, this paper proposes an improved FCS-MPDSC for PMSM application, which abandons the traditional PMSM control cascade structure and has the advantages of fast speed response performance, small speed error and strong parameter robustness. In order to solve the problem of load characteristic parameter uncertainty, Luenberger observer and the Model Reference Adaptive System (MRAS) algorithm are used to estimate the variation of load characteristic parameters in this paper. Moreover, a proportional-integral (PI) control term of speed error is introduced into the cost function to achieve the zero steady state error of speed and torque, which make full use of the robustness of integral action and also a faster dynamic response of direct speed control is maintained. In this paper, the HIL experimental platform is built according to the simulation. The proposed method reduces the steady-state error of the traditional method under the parameter mismatch. When the inductance parameter and the permanent magnet flux parameters deviate two times larger than nominal values, the steady-state error of the motor speed are 0.1125% and 0.8725%, respectively, demonstrating the effectiveness of the proposed method.

Cascade-based tracking control for dynamic positioning vessels under unknown sea loads

This paper investigates the trajectory tracking control problem of a dynamic positioning vessel (DPV) subject to tracking performance, time-varying sea loads and inertia uncertainties. Firstly, a novel cascade model of the DPV is built to describe the dynamics. Then, in order to guarantee the performance constraints of the trajectory tracking, a new-type fixed-time performance function is proposed, which can not only guarantee the transient and steady-state performance during task execution, but also achieve convergence at a fixed time. In addition, an adaptive neural network is introduced to handle the problem of parameter uncertainty and unknown sea loads in the system. Furthermore, it is proved by the Lyapunov stability theory that all signals in the closed-loop system are bounded . Finally, numerical simulation results verified the effectiveness of the proposed method and ensure that the tracking error is stable in the interval of 2 m or less within a given time.

Unscented Kalman Filter-Based Robust State and Parameter Estimation for Free Radical Polymerization of Styrene with Variable Parameters.

The free radical polymerization of styrene (FRPS) is a complex process system with uncertain parameters in its mechanistic model. When the reaction conditions are switched, or the reaction process generates faults, the parameters will change. Therefore, state and parameter estimation (SPE) becomes an important part of the process monitoring and process control for free radical polymerization of styrene. The unscented Kalman filter (UKF) is widely used for nonlinear process systems, but it rarely considers the problem of model parameter uncertainty. UKF can be used for SPE, called UKF-based SPE (UKF-SPE), where the parameters are usually estimated simultaneously as an extension of the state space. However, when the parameters change with system switching, the traditional UKF-SPE cannot detect and track the parameter changes in time, and inaccurate parameters generate modeling errors. To deal with the problem, a UKF-based robust SPE method (UKF-RSPE) for the free radical polymerization of styrene with variable parameters is proposed, introducing a parameter testing criterion based on hypothesis testing and moving windows to directly detect whether the parameters have changed. Based on the detection results, a gradient descent method with adaptive learning rate is used to iteratively update the parameters to speed up the tracking of the parameters and to obtain more accurate parameters and states. Finally, the proposed UKF-based robust SPE is applied to free radical polymerization of styrene in a jacketed continuous stirred tank reactor. The experimental results verify the effectiveness and robustness of the method, which can track the parameters faster and obtain more accurate states.

Distribution of test statistics under parameter uncertainty for time series data: an application to testing skewness, kurtosis and normality

In this paper, we provide a general result under some high level assumptions that shows how to account for the parameter uncertainty problem in test statistics formulated with the quasi maximum likelihood (QML) estimator. We use our general result to develop various test statistics for testing skewness, kurtosis and normality for time series data. We show that the asymptotic distributions of our test statistics coincide with the asymptotic distributions of some tests suggested in the literature. Thus, our general result provides a unified approach for test statistics formulated with the QML estimator for time series data.

Adaptive on-Line Approximator-Based Finite-Time Trajectory Tracking Control for the Surface Vessel

An adaptive finite-time trajectory tracking scheme is proposed to solve the problem of trajectory tracking of underactuated surface ship affected by dynamic uncertainties and external disturbances. In this scheme, underactuated transformation is performed by using kinematic virtual control law transformation and bounded constraint. By designing adaptive control to approach the upper bound of uncertainty and unknown disturbance, the problem of parameter uncertainty and external disturbance are solved. It is proven by Lyapunov theory that the error signals in the system can converge quickly to the stable region in finite time. Finally, by comparing the simulation results, it is verified that the proposed control scheme can make the underactuated ship track the desired trajectory in finite time. Compared to the traditional control method, the convergence rate of the system error is faster, and it exhibits strong robustness in the face of unknown external disturbances. This has a certain reference value for practical engineering applications.

Position Output Adaptive Backstepping Control of Electro-Hydraulic Servo Closed-Pump Control System

A nonlinear adaptive backstepping control method was proposed to address the system parameter uncertainty problem in the position control process of an electro-hydraulic servo closed-pump control system. This control strategy fully considers the parameter uncertainty of the nonlinear system and establishes the adaptive rate of the uncertain parameter to adjust the parameter disturbance online in real time, thereby improving the accuracy and robustness of the control system. A pump control system experiment platform was used to verify the feasibility of the controller. The experimental results showed that the proposed control strategy provided a good control effect. The pump control system can be controlled with high precision, with a steady-state control accuracy of ±0.02 mm, which serves as a good foundation for the engineering application and promotion of the pump control system.

Fixed Effect Meta-Analytic Structural Equation Modeling (MASEM) Estimation Using Generalized Method of Moments (GMM)

The fixed effect meta-analytic structural equation modeling (MASEM) model assumes that the population effect is homogeneous across studies. It was first developed analytically using Generalized Least Squares (GLS) and computationally using Weighted Least Square (WLS) methods. The MASEM fixed effect was not estimated analytically using the estimation method based on moment. One of the classic estimation methods based on moment is the Generalized Method of Moments (GMM), whereas GMM can possibly estimate the data whose studies has parameter uncertainty problems, it also has a high accuracy on data heterogeneity. Therefore, this study estimates the fixed effect MASEM model using GMM. The symmetry of this research is based on the proof goodness of the estimator and the performance that it is analytical and numerical. The estimation results were proven to be the goodness of the estimator, unbiased and consistent. To show the performance of the obtained estimator, a comparison was carried out on the same data as the MASEM using GLS. The results show that the estimation of MASEM using GMM yields the SE value in each coefficient is smaller than the estimation of MASEM using GLS. Interactive GMM for the determination of the optimal weight on GMM in this study gave better results and therefore needs to be developed in order to obtain a Random Model MASEM estimator using GMM that is much more reliable and accurate in performance.