System Parameter Uncertainties Research Articles

Speed estimation of the spindle motor of the grinding head for a high-speed precision roll grinder based on the extended Kalman filter

ABSTRACT Since installing speed sensors on the grinding head for high-speed precision roll grinders is not feasible, it is necessary to use direct torque control (DTC) without speed sensors, and its control system requires high estimation accuracy. In this paper, speed estimation methods using extended Kalman filter (EKF) and model reference adaptive system (MRAS) have been used to realise speed estimation in the DTC system of the spindle motor of the grinding head for a high-speed precision roll grinder. The simulation results showed that the EKF method has relatively high accuracy and a fast convergence speed for the estimation speed of the spindle motor of the grinding head for a high-speed precision roll grinder by comparison with the MRAS method. Moreover, the EKF method was also able to overcome the nonlinear and parameter uncertainty of the operating system of the motor and strongly immune to the noise interference of the system. Finally, the speed estimation accuracy of the motor using the EKF method had better than the MRAS method. Therefore, this work provides a general approach to applying the DTC without speed sensors for the motor and further lays a solid foundation for developing high-speed precision roll grinders.

Backstepping sliding mode control for an active hydraulically interconnected suspension

This paper presents a backstepping sliding mode control (BSMC) method for an active anti-roll hydraulically interconnected suspension (HIS) system. The proposed control strategy enables the active HIS system tracking desired anti-roll moment within the working area. Based on the tracking error, the BSMC controller calculates the flowrate of the control cylinder. This flowrate successively changes the pressure in the accumulators and cylinder chambers, and finally determines the output anti-roll moment of the HIS. The HIS system parameters uncertainty and the system’s nonlinear characteristic are both considered in the proposed BSMC controller. To comprehensively design and validate the proposed control method, a 4-DOF half car model and a corresponding nonlinear active anti-roll HIS model are firstly established. Next, a linear quadratic regulator (LQR) upper layer controller is designed to optimize the vehicle’s roll motion, and the result of the LQR controller is set as a tracking target. Thereafter, the proposed BSMC bottom layer controller is designed to track the target moment. Finally, numerical simulation and experiments on the test bench are conducted to validate the proposed control method. The simulation and bench test results indicate that the proposed active HIS performs well in tracking target and tolerating disturbance. The proposed active HIS expands about additional ±1000 N/m dynamic anti-roll moment compared to a passive HIS at each roll angle, and the response time is less than 70 ms. The field test results show that the active HIS with proposed control method improves anti-roll property compared with a passive HIS by 23.3% in the double lane change test.

State feedback spool position control with integral compensation for servo proportional valve

In recent years, servo proportional valves have been widely used in the industry owing to the advantages of high stability and low cost. However, the nonlinearity and parameter uncertainty present in the servo proportional valves make it difficult to control the spool position. This paper proposes a state feedback control with integral compensation for the spool displacement control of the servo proportional valve, which aims to solve the problem that state feedback is difficult to overcome system nonlinearity and parameter uncertainty. The key to this control method is the addition of an outer loop negative feedback and an integral element. At the same time, the control method can realize pole configuration, convenient control parameter adjustment, low hardware requirements, and easy engineering application. Experimental and simulation results show that the integral element can compensate for the system’s nonlinearities and uncertain parameters, and eliminate the steady-state error of displacement tracking. The state feedback with integral compensation can improve the frequency response of the servo proportional valve compared to traditional PID control.

Modeling and Adaptive Boundary Robust Control of Active Heave Compensation Systems

Heave compensation systems are essential for operations’ safety, reliability, and efficiency in harsh offshore environments. This paper investigates the vibration suppression problem of a type of deep-sea robot with the length of time variation and harsh operating environments for active heave compensation systems, where hydraulic heave compensators implement actuators with input nonlinearity, model coupling, and unknown nonlinear disturbances. A robust adaptive output feedback control scheme based on the backstepping control method is designed to eliminate deep-ocean robot vibration, where the adaptive law handles the system parameter uncertainty. Meanwhile, a nonlinear disturbance observer (NDO) is introduced to overcome the effects of random disturbances and model coupling. In addition, the stability of the whole system is proved according to Lyapunov’s theory, and the scheme is shown to be feasible by theoretical analysis. Finally, a comparative simulation study was conducted to validate the effectiveness of the proposed controller.

Research on Control Strategy of Electro-Hydraulic Lifting System Based on AMESim and MATLAB

Given the influence of flow instability, parameter uncertainty, and unpredictable disturbances in electro-hydraulic lifting systems, high-precision position control for electro-hydraulic lifting systems is challenging to achieve. This study proposes an observer–sliding mode control strategy to improve the control accuracy of the tractor electro-hydraulic lifting system. Firstly, the principle of the electro-hydraulic hoisting system is analyzed. Secondly, a mathematical model of the electro-hydraulic hoisting system is established, and the electro-hydraulic hoisting system is reduced to design an observer to achieve a real-time evaluation of the unknown system state and disturbance. The observer and the sliding mode control are then integrated into a controller to improve system response. Theoretical analysis demonstrates that the controller ensures that the actuator can achieve the desired control effect even under disturbing effects. Finally, a joint AMESim–MATLAB simulation and conducting pilot studies are carried out to compare the observer–sliding mode control with PID (Proportion–Integral–Derivative) control and sliding mode control. At the same time, in the process of the simulation and test, the symmetric structure as the electro-hydraulic lifting system was used to build a ploughing depth simulation system (changes in the hydraulic cylinder thrust simulate changes in ploughing depth values). The results show that the proposed observer–sliding mode control strategy can achieve a better position and pressure tracking and parameter change robustness than PID control and sliding mode control.

Tracking and Synchronization Control Strategy of Vehicle Dual-Motor Steer-by-Wire System via Active Disturbance Rejection Control

A novel vehicle dual-motor steer-by-wire (SBW) system is proposed to improve the reliability and safety of traditional SBW system. However, the biggest challenge of dual-motor SBW is its tracking and synchronization control issues. In order to solve the problems of poor tracking and synchronization of the vehicle dual-motor SBW system caused by load disturbance, parameter perturbation, and model mismatch, a tracking and synchronization control strategy of the vehicle dual-motor SBW system via the active disturbance rejection control is proposed. First, the driving mechanism of dual-motor SBW system is analyzed, and the system dynamics model is established. Then, considering the system disturbance and parameter uncertainty, a second-order active disturbance rejection controller is designed for the position loop of the steering motor, which is composed of a third-order extended state observer and a state error feedback control law. Next, in order to simplify the adjustment difficulty of the controller, the bandwidth method is introduced to greatly reduce the number of controller parameters and the difficulty of parameter adjustment. Based on this, the disturbance rejection performance and stability of the designed active disturbance rejection controller are proved. Furthermore, the cross-coupling control structure is employed to enhance the synchronization performance of the dual-motor system. Finally, the performance of the controller to suppress the external disturbance and internal parameter perturbation is analyzed in step condition and double lane change condition, respectively, and the effectiveness of the proposed control strategy is verified on the dual-motor experimental platform.

Adaptive optimized fractional order control of doubly‐fed induction generator (DFIG) based wind turbine using disturbance observer

AbstractWind energy systems are pollution free and clean form of the renewable energy production. The dynamic model of a wind turbine system based on a doubly fed induction generator (DFIG) is exposed to external disturbances, uncertainties, and nonlinear dynamics. In this paper to ensure the system robustness against external disturbance and uncertainty in system parameters, a novel optimized fractional order robust adaptive sliding mode controller is proposed by utilizing a disturbance observer. The controller's main goal is to track the maximum power point of the wind turbine. In order to show the superiority of the proposed method, the results under normal conditions and in the presence of disturbance and uncertainty have been compared with the classical sliding mode control (SMC) and adaptive sliding mode control (ASMC). The parameters of all three controllers have been optimized by ant colony optimization (ACO) algorithm. The proposed method does not need the knowledge of the upper bounds of model uncertainty and disturbance. Also by using the fractional order operators in the control signal of the proposed method, its robustness against model uncertainty and disturbance is increased and it can extract the maximum power than the other compared methods.

Mechanistic model and probability characteristics of micro-milling force with a new parameter identification method

As a critical part of the analysis of the micro-milling process, mechanical modeling directly affects the prediction of the surface finish of machining materials and the cutting stability. In this paper, a flexible force model of micro-milling is established, which accounts for the influence of the actual cycloid tool path, tool runout, elastic recovery, tool deformation, and chip separation state. The Harris Hawks Error Optimization parameter identification method is proposed to efficiently obtain the cutting force coefficient/parameter. Based on the uncertainty of system parameters caused by the manufacturing and assembly process, probability distribution characteristics of the micro-milling force are predicted and analyzed. Additionally, the Adaptive Kriging method is used to reconstruct the complex implicit relationship between the cutting parameters and milling forces, improving the computational efficiency of the probability analysis. Finally, a series of micro-milling experiments and computational results verified the accuracy of the proposed method.

Reinforcement Learning-Based Adaptive Position Control Scheme for Uncertain Robotic Manipulators with Constrained Angular Position and Angular Velocity

Aiming at robotic manipulators subject to system uncertainty and external disturbance, this paper presents a novel adaptive control scheme that uses the time delay estimation (TED) technique and reinforcement learning (RL) technique to achieve a good tracking performance for each joint of a manipulator. Compared to conventional controllers, the proposed control scheme can not only handle the system parametric uncertainty and external disturbance but also guarantee both the angular positions and angular velocities of each joint without exceeding their preset constraints. Moreover, it has been proved by using Lyapunov theory that the tracking errors are uniformly ultimately bounded (UUB) with a small bound related to the parameters of the controller. Additionally, an innovative RL-based auxiliary term in the proposed controller further minimizes the steady state tracking errors, and thereby the tracking accuracy is not compromised by the lack of asymptotic convergence of tracking errors. Finally, the simulation results validate the effectiveness of the proposed control scheme.

Linear Quadratic Optimal Control with the Finite State for Suspension System

The control algorithm could greatly help the suspension system improve the comprehensive performance of the vehicle. Existing control methods need to obtain the intermediate states, which are difficult to obtain directly or accurately when estimated by filters or observers. Thus, this paper proposed a new practical finite state LQR control method to deal with this problem. By combining with the output state of the finite sensor of the vehicle suspension system and weakening the unknown state as the goal, an optimization model is established with the design variables as the LQR weight coefficients. Then, the direct relationship between the current control input and the finite sensor output is obtained, and the finite state LQR control is realized. Taking the quarter-car suspension model as an example, the corresponding noise is added considering sensor accuracy, and the control performance of the four control methods is studied considering the uncertainties of suspension system parameters. In addition, the acceleration of sprung mass and the dynamic travel coefficient of suspension have been separately calculated by methods of finite state LQR control, LQR control, and PID control. The results show that there is not much difference between them under shock excitation or random excitation. However, the finite state LQR control method has the best comprehensive control performance in that its dynamic tire load coefficient is better than other methods; it could take into account the suspension work stroke coefficient, dynamic tire load coefficient, and sprung mass’ acceleration of the vehicle suspension system at the same time. In order to realize the optimal control effect with limited sensor arrangement, the finite state LQR control method only needs to obtain the current sensor output and the current control input, without estimating the unknown intermediate state. By this means, the proposed control method greatly simplifies the design of the control system and has great advantages on practical value.

Robust Nonlinear Adaptive Control for Power Quality Enhancement of PMSG Wind Turbine: Experimental Control Validation

Due to the intense penetration of wind energy into the power grid, grid quality and stability have become a crucial necessity in this type of power generation. It is in this context that this article has just designed an Adaptive Nonlinear Control strategy applied to the Permanent Magnet Synchronous Generator (PMSG) of 1.5 MW power, in order to generate good quality and cleanly usable energy. Interestingly, this robust control algorithm mainly uses the Lyapunov stability theory, which ensures the stability of the Wind Energy Conversion System (WECS), and therefore offers excellent results in the presence of system parametric uncertainties and changes in the elements of the external environment. To this end, the methodology followed in this in-depth study focuses on the application of the Adaptive Backstepping Control algorithm for WECS by exploiting the MATLAB/Simulink toolbox. The theoretical study and simulation of the WECS was supported by the Processor-in-the-Loop (PIL) implantation of the control in the dSPACE DS1104 embedded board to approve the effect of the control in terms of robustness against different wind profiles and parametric changes. ST-LINK communication is used to connect the embedded board and the host computer. The results obtained revealed a fast response of the different signals, a practically low ripple rate of the order of 0.1% and minor overshoots for the different electrical quantities. Operation with a unity power factor is well ensured via this control strategy. Therefore, the adaptive control applied to the WECS has verified the high performance offered and benefits from additional robustness properties.

Resilient PIDA Control Design Based Frequency Regulation of Interconnected Time-Delayed Microgrid Under Cyber-Attacks

This paper addresses the robust and resilient PIDA control design for frequency regulation of the cyber-physical power system. PIDA control design is carried-out using a recently develop supply demand optimization algorithm (SDOA). This SDOA inspires by the supply-demand mechanism in economics theory. Besides, SDOA is a global optimization algorithm and simple in design. Further, this paper is introduced a new fitness function to obtain the unknown parameters of PIDA controller. From application point of view, the proposed control design is validated on the interconnected time-delayed microgrid system configuration with cyber-attacks. The effectiveness and efficacy of the proposed control design are analyzed under various practical conditions such as cyber-attacks, communication time-delay, parametric uncertainty in system parameters, stochastic load deviation, stochastic wind power deviation, and stochastic solar power deviation. Further, the superiority of SDOA shows in comparison to the grey wolf optimizer (GWO), and whale optimization algorithm (WOA). Finally, it is concluded that the proposed PIDA control design performs better, robust and resilient in comparison to the existing PID control design.

Adaptive boundary control of an axially moving system with large acceleration/deceleration under the input saturation.

We present the dynamical equation model of the axially moving system, which is expressed through one partial differential equation (PDE) and two ordinary differential equations (ODEs) obtained using the extended Hamilton's principle. In the case of large acceleration/deceleration axially moving system with system parameters uncertainty and input saturation limitation, the combination of Lyapunov theory, S-curve acceleration and deceleration (Sc A/D) and adaptive control techniques adopts auxiliary systems to overcome the saturation limitations of the actuator, thus achieving the purpose of vibration suppression and improving the quality of vibration control. Sc A/D has better flexibility than that of constant speed to ensure the operator performance and diminish the force of impact by tempering the initial acceleration. The designed adaptive control law can avoid the control spillover effect and compensate the system parameters uncertainty. In practice, time-varying boundary interference and distributed disturbance exist in the system. The interference observer is used to track and eliminate the unknown disturbance of the system. The control strategy guarantees the stability of the closed-loop system and the uniform boundedness of all closed-loop states. The numerical simulation results test the effectiveness of the proposed control strategy.

Chatter-Free Non-Singular Fast Terminal Sliding Mode Control of Interleaved Boost Converter

In this brief, a global non-singular fast terminal sliding mode controller (NSFTSMC) is proposed to stabilize the output voltage of an interleaved boost converter which is affected by disturbances such as variations in supply voltage, load resistance, and system parameter uncertainties. First, the converter model is transformed into a simplified form to make the controller design process simple. Second, the two-time scale disturbance estimation technique is adopted to estimate the matched, unmatched disturbances, and un-modeled system dynamics. Then, a nonlinear controller based on the sliding mode algorithm is developed to achieve guaranteed large-signal stability. Unlike other controllers, the proposed one ensures global stability and features a fast dynamic response with accurate output voltage tracking ability over wide variations in operating range. Both simulations and experimentations verify the robustness and feasibility of the proposed controller.

Robust linear parameter varying frequency control for islanded hybrid AC/DC microgrids

• The LPV representation of a nonlinear isolated hybrid microgrid model is investigated. • The intermittent nature of the wind speed, solar irradiation, and loads, and several parameter uncertainties are considered in the frequency control design procedure. • A dynamic robust LPV controller synthesis based on the three-step algorithm is proposed for the nonlinear model. • The proposed secondary frequency control approach suggests a practical dynamic output feedback controller with lower order than the IHMG model. • The algorithm superiority is verified in various disturbance-based and uncertain parameter-based scenarios. The applicability of a robust linear parameter varying (LPV) control for frequency fluctuation damping in an islanded hybrid microgrid (IHMG) system (DEG/WTG /PV/FC/ESSs) is investigated in this paper. Due to the erratic behavior of the majority of the renewable energy sources (RESs) and the varying time nature of loads, frequency deviation from the nominal value is inevitable, especially in the islanded mode. Load variation, solar irradiation, and wind power disturbance, as well as system parametric uncertainties, can significantly disturb the system frequency operation. In this paper, the wind speed and rotated speed of the nonlinear wind turbine are highlighted as scheduling parameters, and the nonlinearity of the wind turbine is hidden by the LPV approach. In the proposed method, a dynamic output feedback controller is obtained based on the power balance equation, by solving three LMIs to minimize the upper bound of the L 2 -Norm of the uncertain IHMG in an algorithmic approach. For better evaluation, the ultimate LPV control approach is compared with two other methods on the understudy IHMG model. The result represents a noticeable advantage of the proposed robust method compared to other methods in terms of frequency stability based on time-domain and norms characteristics in presence of the simultaneous disturbances and parametric uncertainties.

Robustness of energy landscape controllers for spin rings under coherent excitation transport

Abstract The design and analysis of controllers to regulate excitation transport in quantum spin rings presents challenges in the application of classical feedback control techniques to synthesize effective control and generates results in contradiction to the expectations of classical control theory. This paper examines the robustness of controllers designed to optimize the fidelity of an excitation transfer to uncertainty in system and control parameters. We use the logarithmic sensitivity of the fidelity error as the robustness measure, drawing on the classical control analog of the sensitivity of the tracking error. Our analysis shows that quantum systems optimized for coherent transport demonstrate significantly different correlation between error and the log-sensitivity depending on whether the controller is optimized for readout at an exact time T or over a time-window T ± Δ/2.

Optimized Load Frequency Controller Performances With a Reduced Number of Decision Variables: A Gravitational Search Algorithm-Based Method

Ensuring stability and maintaining performances of the load frequency controller (LFC) are stimulating tasks. A state feedback controller (SFC) with a generalized range of operation satisfying the necessary and sufficient conditions for system stability along with optimized performances through the gravitational search algorithm (GSA) are proposed. The proposed range is dependent on the system parameters. The agent-based systems in the literature consider the dimension of an agent as per the number of decision variables of the problem. The proposed method reduces the number of decision variables to obtain the controller gains and to use in the optimization process. The proposed method is validated through extensive results for different objective functions, contemplating both the steady state and the transient requirements as well as a consideration of different perturbed system parameters and variations in the input disturbances. Results and comparative studies with popular control techniques and optimization algorithms testify that the proposed method maintains system stability and improves the system performances while considering different system parameters, parametric uncertainties, and variations in input disturbances.

Finite-time tracking control with fast reaching condition for disturbed more electric aircraft direct current microgrid with constant power loads

Future more electric aircraft (MEA) can be seen as a direct current microgrid (DC MG). While DC MG can generally achieve safe, efficient and reliable operation of the system, constant power loads (CPLs), unknown resistive loads, DC source voltage variations, and disturbance inputs might degrade the overall stability and performance if they are not compensated in the control design procedure. In this paper, a fast-reaching condition-based sliding mode control (SMC) is proposed for the DC MG. The fast-reaching law contains two terms that improve the transient performance by forcing the system states to reach a sliding surface with two different rates. This attribute enhances the speed of convergence of the states to the sliding surface compared to conventional SMC. Additionally, the suggested method is resilient to system parameter uncertainties, which is indispensable in practice. The proposed controller is implemented to a DC MG that is supplied by an uncertain DC source and feeds CPLs and resistive load with time-varying power and resistance. OPAL RT results illustrate the robustness and fast convergence of the planned SMC controller.

Design of Robust Control for Vehicle Steer-by-Wire System

This study presented the design of a robust controller based on Integral Sliding Mode Control (ISMC) for controlling the Vehicle Steer-by-Wire (VSbW) system. The dynamic model of the VSbW system is first developed and then the design of ISMC has been conducted via the states of the system. The VSbW system has been described by two terms; one term represents the nominal model, which is free from nonlinearities, and the other term lumps the uncertainties in system parameters. The integral sliding mode controller has been designed for controlling the VSbW system. The control design consists of two parts. The first control part has addressed the nominal term of the system, while the second control part tackles and eliminates the effects of uncertainties and perturbation due to the uncertain term of the system. The numerical simulation has been conducted to show the robustness of ISMC and its capability to reduce the chattering effect in the control signal. In addition, a comparison study in performance has been conducted between the proposed controller and other controllers in the literature. We also carry out bibliometric analysis to see research trends. Based on our analysis, the number of publications regarding the keywords "controller", "steer", and "wire system" changes every year (25 (2018), 56 (2019), 51 (2020), 71 (2021), and 61 (2022)).

When artificial parameter evolution gets real: particle filtering for time-varying parameter estimation in deterministic dynamical systems

Estimating and quantifying uncertainty in unknown system parameters from limited data remains a challenging inverse problem in a variety of real-world applications. While many approaches focus on estimating constant parameters, a subset of these problems includes time-varying parameters with unknown evolution models that often cannot be directly observed. This work develops a systematic particle filtering approach that reframes the idea behind artificial parameter evolution to estimate time-varying parameters in nonstationary inverse problems arising from deterministic dynamical systems. Focusing on systems modeled by ordinary differential equations, we present two particle filter algorithms for time-varying parameter estimation: one that relies on a fixed value for the noise variance of a parameter random walk; another that employs online estimation of the parameter evolution noise variance along with the time-varying parameter of interest. Several computed examples demonstrate the capability of the proposed algorithms in estimating time-varying parameters with different underlying functional forms and different relationships with the system states (i.e. additive vs. multiplicative).