H∞ Robust Control for ICPT System With Selected Weighting Function Considering Parameter Perturbations

This paper introduces the direct-drive volumetric control (DDVC), focusing on the direct-drive pump-controlled hydraulic motor electro-hydraulic system as the primary subject of research. Aiming to address the problem of parameter uncertainties and unknown nonlinear disturbances in the system, a robust backstepping controller based on the extended state observer (ESO) is designed to improve the system's dynamic performance and robustness. In the controller, the ESO is used to estimate the total uncertainties in the system. Then, the robust backstepping controller is designed to enhance both the dynamic and robust performance. Moreover, Lyapunov theory is utilised to rigorously demonstrate the stability of both the ESO and the closed-loop control system. Finally, the results indicate the designed controller has excellent robustness and dynamic performance.

The variable stator vane (VSV) system is a key component of gas turbine supercharged rotor components for anti‐surge, and its performance directly affects the working stability and economy of the gas turbine. The effect of the VSV system on the dynamic characteristics and control performance of the gas turbine cannot be known. Then a refined model of the VSV system refined to design parameters established and studied to explore the effect of gas turbine dynamic performance based on co‐simulation. The effects of key parameters on the performance of the gas turbine system are analyzed by embedding the refined VSV model into the engine model. The simulation results prove that servo amplifier gain, viscous friction coefficient and dead band are sensitive structural parameters that affect the dynamic performance of the gas turbines. In the proposed appropriate interval, the optimal sensitive parameters can reduce the overshoot and adjustment time by about 51% and 7%. The gain of the servo amplifier is positively correlated with the maximum opening of the VSV. When the dynamic performance of the control system has been exerted to its extreme, some nonlinear parameters of the actuator can greatly improve the overall performance of the complex system.

Reasonable ratio between the load inertia and servo motor inertia plays a decisive role for the dynamic performance and stability of the servo system, as well as the machining accuracy of the whole CNC machine. In order to improve the control performance and contour machining accuracy of the servo system of the CNC cycloidal gear form grinding machine, an optimization design method of the inertia matching for the CNC cycloidal gear form grinding machine servo system is proposed. The two-mass servo driving closed-loop PID control system is constructed, the influence of the different inertia ratios on the dynamic performance and contour errors of the servo system are deeply analyzed, and the inertia ratio is optimized to satisfied with the servo system performance requirements. Finally, the feasibility and practicability of the optimization design method of inertia matching are verified through the inertia ratio optimization grinding experiments of the cycloid gear in the CNC gear form grinding machine. This inertia matching optimization design method provides a valuable reference for the further design of CNC machine servo system.

This work continues a series of artides devoted to an illustrative comparison of the methods of modern control theory and the method of Analytical Design of Aggregated Regulators (ADAR). The ADAR method suggests two ways to ensure a nonlinear system adaptability to the external and parametric perturbations. The first way is the use of the principle of integral adaptation, when the influence of the parametric or external perturbations is compensated for by the nonlinear control laws introduced by integrators in a spedal manner. The design pгoceduгe of the adaptive control laws, according to this way, does not require a synthesis of the state and perturbation observers and consequently eliminate a real-time estimation of these perturbations. The second way is a design of the nonlinear observers of the parametric and external perturbations. In this case the designed nonlinear control laws are supplemented by a subsystem of observation, which realizes a dynamic estimation of the nonmeteringperturbations and a compensation for them. The above ways are illustrated in the artide by the known control tasks: the task of a wheel slip control in an anti-lock braking system; and the task of identification of the parameters and monitoring of the state of electгochemical pгocesses in the cell membranes in accordance with Hindmarsh and Rose model. The examples of the design procedures presented in the article demonstrate the advantages of the ADAR method concerning the analytical design of the adaptive regulators for the nonlinear objects. The theoretical results were confirmed by simulation of the closed-loop system in MATLAB software.

This study proposes an improved super-twisting sliding mode (STSM) control method for a brushless doubly fed induction generator (BDFIG) used in standalone ship shaft power generation systems. Focusing on the problem of the low tracking accuracy of the power winding (PW) voltages caused by the parameter perturbation of BDFIG systems, a mismatched uncertain model of the BDFIG is constructed. Additionally, an improved STSM control method is proposed to address the power load variation and compensate for the mismatched uncertainty through virtual control technology. Based on the direct vector control of the control winding (CW), the proposed method ensured that the voltage amplitude error of the power winding could converge to the equilibrium point rather than the neighborhood. Finally, in the experimental investigation of the BDFIG-based ship shaft independent power system, the dynamic performance in the startup and power load changing conditions were analyzed. The experimental results show that the proposed improved STSM controller has a faster dynamic response and higher steady-state accuracy than the proportional integral control and the linear sliding mode control, with strong robustness to the mismatched uncertainties caused by parameter perturbations.

To address slow convergence speed and low convergence accuracy in the inverse model (IM) learning and training of the controlled object during BP neural network (BP-NN) feedforward compensation control for tracking a step signal, which results in poor dynamic and steady-state performance of the control system. This study introduces an improved Adam algorithm (IADAM)-enabled BP-NN feedforward compensation control method. This method introduces a dynamic learning rate adjustment mechanism based on the Adam algorithm, which adjusts the learning rate in phases based on the NN training parameter gradients. When the gradient exceeds a preset threshold, the learning rate is dynamically increased via an adjustment function to accelerate IM convergence. When the gradient is below the threshold, the maximum historical second-order moment estimate of the gradient is retained to keep the learning rate at a lower value, thereby improving IM convergence accuracy. The control system is composed of a PID controller, a neural network controller (NNC), and an IADAM-based neural network identifier (IADNNI). The PID controller provides online learning samples. The IADNNI learns the IM of the controlled system online, while the NNC shares the same structure and parameters as the IADNNI, generates real-time compensation control signals. Experimental results from a DC servo motor position control platform reveal that the proposed control method achieves zero overshoot in tracking both continuous step signals and square signals, while the settling time is reduced by 60.05% and 39.53%, respectively, compared to the ADAM + BP + PID control method. resulting in significant improvements in both dynamic and steady-state performance.

In this article, a deadbeat predictive current control (DPCC) method based on gain factor self-tuning technology is proposed, which can effectively improve the dynamic response performance and robustness of the control system. Since the performance of DPCC is highly dependent on parameter accuracy, this article establishes a closed-loop transfer function model of the DPCC method and analyzes in detail the impact of parameter mismatch on the current dynamic response performance. Then, the current error caused by parameter mismatch is suppressed by designing the gain factor, on this basis, a parameter self-tuning algorithm based on gradient descent optimization method is constructed. Compared with traditional parameter identification algorithms, the proposed method has the advantages of strong versatility and greatly reduces computational burden. Finally, the accuracy of the tuning results of the proposed method is proved by experimental results, which effectively suppresses the influence of parameter mismatch and improves the current response performance.

In this paper, the dynamic performance of an interconnected two-area non-reheated thermal power system with inherent nonlinearity due to generation rate constraint (GRC) present in the turbine has been investigated using a robust fractional order controller. The controller parameters have been optimized with the help of a recently proposed Salp Swarm Algorithm (SSA) based on the social behavior of Salpidae found in the deep ocean. Extensive simulation studies carried out in this paper establish that SSA optimized controllers provide significant improvement in the dynamic performance of the system even under parametric variations in comparison to genetic algorithm and bacterial foraging optimization algorithm optimized controller.

Composite control based on FNTSMC and adaptive neural network for PMSM system

To enhance the dynamic performance and disturbance rejection capability of the permanent magnet synchronous motor speed control system, a novel speed control method based on a novel sliding mode control (NSMC) and load torque observer is proposed on the basis of model predictive current control (MPCC) with a sliding mode disturbance observer. First, on the basis of MPCC, the influence of parameters such as resistance, inductance, and flux linkage on MPCC is analyzed. To address the aggregated disturbance caused by parameter mismatches, a piecewise square-root switching function sliding mode disturbance observer (SMDO) is designed to enhance the robustness of the parameters. To address the poor dynamic performance and inadequate robustness resulting from the proportional-integral-controller (PI) velocity loop control in the MPCC, a novel NSMC velocity control method is proposed. This method utilizes the hyperbolic sine function and fractional-order integral sliding mode surface, resolving the dilemma faced by traditional slide mode controllers (SMC) in balancing fast response and reduced vibration. Additionally, to enhance the system’s disturbance rejection capability, a sliding mode torque observer (SMTO) is designed to continuously update the observed load torque value into the NSMC controller, achieving speed compensation control. Finally, through comparative experiments among the proportional integral controller (PI), SMC, NSMC, and NSMC + SMTO, the results indicate that the proposed NSMC + SMTO exhibits the best speed response, steady-state characteristics, and disturbance rejection capability.

<div class="htmlview paragraph">In this investigation a robotic system's dynamic performance is optimized for high reliability under uncertainty. The dynamic capability equations allow designers to predict the dynamic performance of a robotic system for a particular configuration (i.e.,point design). While the dynamic capability equations are a powerful tool, they can not account for performance variations due to aleatory uncertainties inherent in the system. To account for the inherent aleatory uncertainties, a reliability-based design optimization (RBDO) strategy is employed to design robotic systems with robust dynamic performance. RBDO has traditionally been implemented as a nested multilevel optimization process in which reliability constraints require solution to an optimization problem (i.e., reliability analysis). In this work a robust unilevel performance measure approach(PMA) is developed for performing reliability-based design optimization which eliminates the lower level problem in RBDO. A robotic test problem is used to illustrate the efficacy of the approach.</div>

In this investigation a robotic system's dynamic performance is optimized for high reliability under uncertainty. The dynamic capability equations (DCE) allow designers to predict the dynamic performance of a robotic system for a particular configuration and reference point on the end-effector (i.e.,point design). Here the DCE are used in conjunction with a reliability-based design optimization (RBDO) strategy in order to obtain designs with robust dynamic performance with respect to the end-effector reference point. In this work a unilevel performance measure approach (PMA) is used to perform RBDO. This is important for the reliable design of robotic systems in which a solution to the DCE is required for each constraint call. The method is illustrated on a robot design problem

Direct-drive actuators, linear motors are widely used in many industrial and military applications, particularly in high-end manufacturing due to advantages of high force density, rapid dynamic response, and low thermal losses. Permanent magnet linear synchronous motors (PMLSMs) can dramatically improve the dynamic and static performance of the motion system. However, as one of the most critical sources of error, the thrust ripple of linear motors can deteriorate performance and even excite the mechanical resonance. Thrust ripple suppression technology had received broad interest and has been researched extensively. Therefore, this paper summarizes different types of thrust ripple suppression methods and their principles are analyzed in detail. Firstly, structural optimization methods are introduced to suppress the thrust ripple and increase the precision of the thrust. Secondly, control methods are described to decrease the velocity fluctuation caused by the thrust ripple. Thirdly, a combination of structural design and control method is presented to compensate the ripple, meaning high order harmonic components are eliminated by permanent magnets (PM) skewing technology and low order harmonic are compensated by a linearization observer. Finally, conclusions are made regarding thrust ripple suppression technology and the future trend is proposed.

Dynamic Model-based Saddle-point Approximation for Reliability and Reliability-based Sensitivity Analysis

To improve the dynamic response performance of a high-flow electro-hydraulic servo system, scholars have conducted considerable research on the synchronous and time-sharing controls of multiple valves. However, most scholars have used offline optimization to improve control performance. Thus, control performance cannot be dynamically adjusted or optimized. To repeatedly optimize the performance of multiple valves online, this study proposes a method for connecting a high-flow proportional valve in parallel with a low-flow servo valve. Moreover, this study proposes an algorithm in which a proportional–integral–derivative system and multivariable predictive control system are used as an inner loop and outer loop, respectively. The simulation and experimental results revealed that dual-valve parallel control could effectively improve the control accuracy and dynamic response performance of an electro-hydraulic servo system and that the proportional-integral-derivative–multivariable predictive control controller could further dynamically improve the control accuracy.