Composite Control Scheme Research Articles

Multi Combustor Turbine Engine Acceleration Process Control Law Design

Abstract Focusing on the collaborative fuel supply and acceleration problem of multi combustor turbine engine, a variable geometry composite regulation acceleration control law design method based on the combination of compressor intermediate stage air bleed(CIB) and low-pressure turbine guide vanes(LTGV) is proposed. The multi combustor collaborative fuel supply law and variable geometry regulation law during acceleration process are obtained by Particle Swarm Optimization-Sequence Quadratic Program (PSO-SQP) optimization, and the composite regulation acceleration control strategy of dual/single combustion chamber idle acceleration process is explored. The multi combustor acceleration process simulation results indicate that the variable geometry composite regulation method can effectively alleviate the stability margin limitation of the compressor during the initial acceleration stage. Compared with conventional acceleration methods, the maximum relative reduction in acceleration time of our method reaches 26%; Enhancing the combustion exergy efficiency during the acceleration process can significantly improve the engine's acceleration performance.; For the acceleration process from single combustion chamber idle operation to normal operation of a dual combustion chamber, simultaneous fuel supply from both combustion chambers during acceleration has a shorter acceleration time. The research results of this paper can provide important references for the design of transition state control systems for multi combustor turbine engines.

Design of extended dissipative-based composite anti-disturbance reliable tracking control for stochastic Takagi–Sugeno fuzzy systems

This study focuses on examining the issues of extended dissipative-based composite anti-disturbance reliable tracking control for stochastic Takagi–Sugeno fuzzy systems afflicted by sensor faults and multiple disturbances. Explicitly, the multiple disturbances include the autonomous Wiener process and non random noises with partly known information that are generated by an exogenous plant. To address the difficulty of estimating the non random disturbance while considering the partially known information, the study incorporates a fuzzy stochastic disturbance observer. Furthermore, to solve the output tracking issue of the system under consideration, a modified repetitive tracking control is implemented. Then, combining the methodologies of disturbance observer-based control technique with modified repetitive control, a composite anti-disturbance reliable tracking control strategy is developed for achieving the intended tracking goal despite the multiple disturbances and sensor faults encountered. Additionally, the proposed approach involves extended dissipativity scheme, which includes H ∞ , passivity, dissipativity, and L 2 − L ∞ performances in a unified framework. In particular, by employing Lyapunov–Krasovskii functional approach, the essential stability criteria for the proposed closed-loop system are articulated in the form of linear matrix inequalities. Additionally, numerical illustrations are presented to exemplify the application and efficiency of the developed control protocol.

Design and verification of a novel energy-efficient pump-valve primary-auxiliary electro-hydraulic steering system for multi-axle heavy vehicles

Multi-axle heavy vehicles employ a variable-length tie rod steering system to achieve precise multi-mode steering. However, the single-axle steering system need integrate two sets of electro-hydraulic control systems (EHCSs), resulting in the energy consumption significant increase. This paper proposes a novel energy-efficient pump-valve primary-auxiliary electro-hydraulic steering system (PVPA EHSS) which compose of a pump-controlled dual-steering cylinders primary system and a valve-controlled variable-length tie rod cylinder auxiliary system. The proposed system utilizes a single pump to drive both the primary and auxiliary EHCSs simultaneously, enabling high-precision and high-efficiency steering with multi steering modes. Additionally, to suppress the flow disturbance of the auxiliary system branch flow on the single-pump-controlled steering primary system, the interaction between the flow of the auxiliary system and the flow of the primary system in each steering phase is studied. A pump flow feedforward-angle feedback composite control strategy incorporating branch flow prediction is proposed to mitigate the disturbance and ensure the accurate steering of left and right tires. The experimental results demonstrate that compared to the traditional variable-length tie rod steering system, the proposed system reduces energy consumption by 58.66 %, and achieves precise adaptation of the left and right tire angles in multi steering modes.

Active vibration isolation support platform for double crystal monochromator

The quality of the beamline at a synchrotron light source is directly related to the performance of the double crystal monochromator. This paper presents a designed active vibration isolation support platform for the double-crystal monochromator to meet the higher stability demands of advancing synchrotron light sources. The platform has good decoupling characteristics through the redundant structure of eight actuators mixed with active and passive. A vibration isolation control scheme is designed based on distributed degrees of freedom and a feed-forward feedback composite control scheme to ensure high stiffness of the support platform while achieving full-band vibration isolation. Finally, constructed a verification platform for an active vibration isolation system and verified the effectiveness of the proposed control method through experiments. With the designed active vibration isolation algorithm, the system can achieve full-band vibration isolation within the 1–100 Hz bandwidth.

Composite anti-disturbance predictive control of unmanned systems with time-delay using multi-dimensional Taylor network

A composite anti-disturbance predictive control strategy employing a Multi-dimensional Taylor Network (MTN) is presented for unmanned systems subject to time-delay and multi-source disturbances. First, the multi-source disturbances are addressed according to their specific characteristics as follows: (A) an MTN data-driven model, which is used for uncertainty description, is designed accompanied with the mechanism model to represent the unmanned systems; (B) an adaptive MTN filter is used to remove the influence of the internal disturbance; (C) an MTN disturbance observer is constructed to estimate and compensate for the influence of the external disturbance; (D) the Extended Kalman Filter (EKF) algorithm is utilized as the learning mechanism for MTNs. Second, to address the time-delay effect, a recursive τ−step-ahead MTN predictive model is designed utilizing recursive technology, aiming to mitigate the impact of time-delay, and the EKF algorithm is employed as its learning mechanism. Then, the MTN predictive control law is designed based on the quadratic performance index. By implementing the proposed composite controller to unmanned systems, simultaneous feedforward compensation and feedback suppression to the multi-source disturbances are conducted. Finally, the convergence of the MTN and the stability of the closed-loop system are established utilizing the Lyapunov theorem. Two exemplary applications of unmanned systems involving unmanned vehicle and rigid spacecraft are presented to validate the effectiveness of the proposed approach.

Research on Heave Compensation System Based on Switched Reluctance Motor

Aiming at the requirements of the marine work platform for real-time control, time-varying speed and the time-varying torque control of the motor-driven active heave compensation device, this paper introduces a composite control strategy based on the switched reluctance motor (SRM)-driven active heave compensation device. As for the compensation error caused by the time lag in the real-time system, the model prediction trajectory algorithm is used to predict the compensation displacement obtained using the dynamic model. The next time, the control parameters are then provided for the SRM control system in advance to reduce the compensation error. The SRM control strategy selects a double closed-loop compound control strategy of Back Propagation (BP) fuzzy neural network Proportion Integration Differentiation (PID) control. Its outer speed loop uses a fuzzy controller to quickly track a wide range of speed changes. The torque inner loop uses BP neural network adaptive PID control. This helps to reduce torque ripple and to ensure that the electromagnetic torque output of the SRM remains stable. Finally, the system feasibility is verified by setting different wave parameters. The simulation results show that the simulation conditions can reach 97.5% and 96.4% under the 3 and 4 wave levels, respectively. The simulation effect is satisfying, which verifies the feasibility of the proposed scheme.

Radial Basis Function Neural Network and Feedforward Active Disturbance Rejection Control of Permanent Magnet Synchronous Motor

A composite control strategy is proposed to improve the position-tracking performance and anti-interference capabilities of permanent magnet synchronous motors (PMSMs). This strategy integrates an active disturbance rejection controller (ADRC) and a radial basis function neural network (RBFNN) with feedforward control. Initially, the flexibility and robustness of the ADRC are utilized in the position loop control. Subsequently, the parameters of the extended state observer (ESO) within the ADRC are optimized, benefiting from the fast convergence speed and optimal approximation provided by the RBFNN. To further enhance the dynamic tracking performance, a differential feedforward link is introduced between the desired speed and the output signal. The simulation and experimental results demonstrate that when the expected electrical angle inputs are sinusoidal and pulse signals, the incorporation of the feedforward link and the adjustment of parameters in the ADRC lead to improved position-tracking capabilities and greater adaptability to load disturbances.

A guaranteed cost based memory event‐triggered control of networked control systems with multiple disturbances via an interconnected disturbance observer approach

AbstractThis article is dedicated to event‐triggered anti‐disturbance control of networked control systems via a guaranteed cost triggering scheme. A distribution interconnected observer structure is built to estimate these unknown matched disturbances. A novel event triggered condition is constructed in terms of real‐time cost and averaged functions composed of observer states and the last successfully transmitted one. A composite control scheme is provided by an event‐triggered observer‐based state feedback controller and compensators for the matched disturbances. A sufficient condition in term of linear matrix inequality is supplied to ensure the asymptotic stability of the resultant closed‐loop system. A simulation is carried out to demonstrate the effectiveness of the proposed triggering control strategy.

Active Composite Control of Disturbance Compensation for Vibration Isolation System with Uncertainty

The pointing and positioning accuracy of precision instruments in aerospace are often disturbed by low-frequency vibrations. An active/passive vibration isolation system is a feasible solution to suppress low-frequency vibrations. However, the vibration isolation performance of the active control strategy is seriously affected by the uncertainty of the system and the difficulty to meet the higher requirements of new-generation equipment. This paper proposes an active composite control (ACC) strategy for vibration isolation systems with uncertainty. The proposed ACC integrates feedforward control based on known systems and feedback control based on the Kalman filter for systems with uncertainty. Further, the derivation and stability analyses of the proposed ACC algorithm are provided, and the influence of system uncertainty on vibration isolation performance based on the proposed ACC is analyzed. Experimental verification is conducted and the experimental results confirm that the proposed ACC can effectively realize the low-frequency and wide-band vibration isolation for the system with uncertainty. Starting from 30 Hz, the vibration isolation performance of the proposed ACC with uncertainty is significantly improved than that of the ACC completely based on a deterministic system model.

A Composite Vibration Control Strategy for Active Suspension System Based on Dynamic Event Triggering and Long Short-Term Memory Neural Network

A Composite Vibration Control Strategy for Active Suspension System Based on Dynamic Event Triggering and Long Short-Term Memory Neural Network

A New Composite Control Strategy for Large-Signal Stabilization of Constant Power Loads in Islanded AC Microgrids

A New Composite Control Strategy for Large-Signal Stabilization of Constant Power Loads in Islanded AC Microgrids

A composite motion control method for quadruped robots in trot gait

This work aims to address the issues of instability in trot gait walking and poor terrain adaptability in rugged environments under conventional control strategies for quadruped robots by proposing a novel composite control strategy. During the support phase, a separated force-position mixing control method is employed, utilizing gravity-compensated PD (Proportional Derivative) control at the lateral swing and knee joints while employing position control at the hip joint. During the swing phase, VMC (Virtual Model Control) is used to construct three sets of virtual spring-damper components, guiding the foot to move along a predetermined trajectory and converting virtual forces into joint torques for the swinging leg. At the body, IMU (Inertial Measurement Unit) feedback data combined with PI (Proportional Integral) control is used to adjust leg length and maintain the robot’s posture stability. Finally, the proposed separated force-position hybrid control method is theoretically validated using the Lyapunov stability criterion, and simulation tests on flat and rugged terrains under the quadruped robot’s composite control method are conducted using MATLAB/Simulink, comparing it with the VMC and position control methods. The findings indicate that the composite control strategy leads to smaller changes in the attitude angles and reduced impact forces at the feet during the quadruped robot’s walk on flat ground while also demonstrating strong adaptability to rugged terrains.

Study on Chassis Leveling Control of a Three-Wheeled Agricultural Robot

Three-wheeled agricultural robots possess the advantages of high flexibility, strong maneuverability, and low cost. They can adapt to various complex terrains and operational environments, making them highly valuable in the fields of crop planting, harvesting, irrigation, and more. However, the horizontal stability of the three-wheeled agricultural robot chassis is compromised when working in harsh terrain, significantly impacting the overall operational quality and safety. To address this issue, this study designed a leveling system based on active suspension and proposed a stepwise leveling method based on an adaptive dual-loop composite control strategy (ADLCCS-SLM). Firstly, in the overall control of the three-wheeled chassis, a stepwise leveling method (SLM) was introduced. This method allows for rapid leveling by incrementally adjusting one or two suspensions, effectively avoiding the complex interactions between suspension components encountered in traditional methods involving the simultaneous linkage of three suspensions. Next, in terms of suspension actuator control, an adaptive dual-loop composite control strategy (ADLCCS) was proposed. This strategy employs a dual-loop composite control both internally and externally and utilizes an improved adaptive genetic algorithm to adjust critical control parameters. This adaptation optimizes the chassis leveling performance across various road conditions. Finally, the effectiveness of the proposed ADLCCS-SLM was validated through simulation and experimental testing. The test results showed that the control effect of the proposed method was significant. Compared to the traditional multi-suspension linkage leveling method based on PID, the peak values of pitch angle and roll angle were reduced by 31.8% and 33.3%, respectively.

Underwater manipulator arm control based on Harris Hawk algorithm optimized RBF neural network

This article addresses the control issues of underwater manipulator arms in complex marine environments, proposing a composite control strategy based on the Harris Hawk Optimization (HHO) algorithm and Radial Basis Function (RBF) neural network. Combining the global search capability of the HHO algorithm with the fast approximation characteristics of RBF neural networks, a self-adaptive control method for underwater manipulator arms is designed. By automatically optimizing the parameters of the neural network, the performance and robustness of the control system are enhanced. Through simulation experiments, the effectiveness of the proposed control algorithm is verified. The results show that compared with the traditional RBF neural network control and fuzzy sliding mode control, the optimized control algorithm proposed in this paper has significant improvement compared with both of them, demonstrates good control effect and high practical value, and provides an effective solution for the precise control of the underwater manipulator arm.

Finite-time composite learning control for flexible-link manipulator based on disturbance observer

With the development of robotic technology and theory, there is an expectation for robots to operate in complex environments. This paper addresses the joint tracking control problem of flexible-link manipulator system under unknown external disturbances, modelling uncertainties, and actuator saturation constraints. A research is conducted, proposing a composite learning control scheme based on adaptive finite-time disturbance observer (AFTDO). The designed AFTDO can estimate the upper bound of unknown disturbances by adaptive laws, and a neural network controller is developed based on the AFTDO algorithm to compensate and suppress disturbances from multiple sources, ensuring the stability of all variables within a finite time. Simultaneously, a barrier Lyapunov function (BLF) is chosen to guarantee that the system satisfies constraints under flexible vibrations. Finally, through simulation comparisons with other control strategies, the feasibility and superiority of the proposed method are convincingly demonstrated.

Research on photovoltaic grid-connected inverters with source filtering function

Abstract To suppress the current harmonics caused by the nonlinear load connected to the grid when the photovoltaic system is connected to the grid for power generation, this paper adopts a composite control strategy based on the PI-RC controller to realize the synthesis of the command current signal in different modes. According to the different command current signals, the system can work in three modes: photovoltaic grid-connected harmonic compensation control, photovoltaic grid-connected control, and harmonic compensation unified control. Under the composite control strategy, the system realizes the active output of the photovoltaic grid-connected system with the function of source filtering, compensates for the harmonics caused by nonlinear loads, successfully reduces the harmonic distortion rate of grid current, and then realizes the function expansion of photovoltaic grid-connected inverter. Finally, the feasibility and rationality of the unified control strategy designed in this study are verified by simulation experiments.

Research on Inverter Control Strategy Based on FPGA

Abstract This study adopts a composite repetitive controller strategy in a three-phase four-wire circuit, combining proportional resonance and repetitive control, which effectively suppresses harmonics and improves system performance. At higher switching frequencies, FPGA is used to achieve efficient control to ensure system stability and performance. This research provides new ideas for improving the performance of on-board inverters, especially in terms of high-speed control, nonlinear load and harmonic processing. SiC devices and composite control strategies provide more sustainable solutions for future rail transit systems.

Current Harmonic Suppression in Maritime Vessel Rudder PMSM Drive System Based on Composite Fractional-Order PID Repetitive Controller

To address the control performance and harmonic suppression issues in maritime vessel rudder permanent magnet servo systems, a fractional-order PID controller was introduced into the existing improved repetitive control strategy. We used the Oustaloup approximation algorithm and particle swarm optimization for tuning the fractional-order PID controller. The optimized parameters substantially improved the control performance. By integrating the fractional-order PID controller with the improved repetitive controller, a composite fractional-order PID repetitive control strategy was formed. Finally, MATLAB/Simulink simulations were conducted to compare and verify the disturbance rejection and harmonic suppression capabilities of the improved control strategy. The results demonstrate its superior control performance, thereby increasing the practicality of the control system in dealing with various situations.

Homogeneous domination-based composite adaptive control strategy for perturbed chain of integrators with mismatched disturbances

Homogeneous domination-based composite adaptive control strategy for perturbed chain of integrators with mismatched disturbances

Neural network-based adaptive optimal tracking control for hypersonic morphing aircraft with appointed-time prescribed performance

In this paper, an adaptive optimal attitude tracking controller for hypersonic morphing aircraft with appointed-time prescribed performance is presented. A proper error transformation methodology is developed to ensure the performance constraints are met without knowing initial tracking conditions. Subsequently, an adaptive composite control scheme is devised for the transformed system, comprising a feedforward neuro-backstepping controller and a feedback optimal controller. The former is employed to convert the optimal tracking problem into an equivalent optimal regulation problem, while the latter is constructed using adaptive dynamic programming (ADP) with a single critic network to achieve optimality. A novel weight-tuning law for the critic network is introduced, incorporating a stability indicator and the concurrent learning technique. This approach relaxes the underlying restrictive conditions in ADP, improving the robustness and availability of the system. The stability of the closed-loop system is analyzed using the Lyapunov direct method. Finally, the effectiveness and improved performance of the proposed control scheme are validated through numerical simulations.