Excellent Control Performance Research Articles

Research on Satellite Navigation Control of Six‐Crawler Machinery Based on Fuzzy PID Algorithm

ABSTRACTThe six‐crawler driving mechanism plays a crucial role in the operation of large machines such as bucket‐wheel excavators, dumping machines, and mobile crushing stations, as it serves functions like bearing, movement and steering. The driving characteristics of this mechanism directly influence the safety and efficiency of these machinery systems. To enhance the design methodology for multi‐crawler machinery, improve path controllability, and achieve adaptive driving, a satellite navigation control system for six‐crawler machinery was developed based on the principles of real‐time kinematic (RTK) satellite positioning. This system utilizes the distance deviation and heading angle deviation between the actual path and the predetermined path of the six‐crawler machinery as inputs to a fuzzy proportion integration differentiation (fuzzy PID) controller. This controller regulates the deviation angle of the steering crawler and the driving speeds of each track, thereby ensuring precise path tracking control. To evaluate the path tracking control performance under both straight and curved driving conditions, a virtual prototype model of the six‐crawler mechanical system was established, and co‐simulation analysis was conducted. In addition, an experimental platform for path tracking control of six‐crawler machinery was established to validate the efficacy of the satellite navigation system. The actual tracking data obtained from various driving conditions and initial deviations demonstrated that the RTK satellite navigation path tracking control system exhibited excellent control performance.

Neural algorithm for optimization of multidimensional object controller parameters

Optimal control of multivariable systems is a complex dynamic process that minimizes the cost function to obtain the optimal control strategy. Unfortunately, for nonlinear systems, it is not possible to use the traditional linear quadratic regulator (LQR), which would be optimal over the entire range of parameter variation. The problem of nonlinear multivariable systems and their optimal control is very momentous. The solution presented in this paper is based on the application of Reinforcement Learning (RL) networks in controlling a five-degree-of-freedom overhead crane system. Additionally, unlike the classical approach, the algorithm is adapted to directly analyze tabular data of inputs and outputs of the controlled model instead of analyzing its state as feedback (model-free). Implementing the new control structure for the multivariable system improved control quality compared to the classical LQR controller with linearization at the operating point. In addition to quality, the resource indicators, which in the LQR controller are represented by the matrix R, have been significantly improved. The architecture of the neural control system is presented, ensuring that over the entire range of nonlinearity, the quality of control is preserved while reducing the cost of its resource intensity. Obtaining optimal control with reduced resources for its implementation induces a wide range of applications of such neural control in engineering systems. The effectiveness of the proposed control system has been demonstrated in simulation studies. The simulation results present the system’s excellent control performance and adaptability over the entire range of object nonlinearity. The neural algorithm resulted in significantly shorter adjustment time and better control quality with significantly less system resource consumption and increased system dynamics.

Theoretical and experimental research on multi-modal vibration control of flexible beams via the multiple-input multiple-output decoupling control strategy

In this study, a multiple-input multiple-output decoupling control (MIMO-DC) strategy is proposed based on the linear active disturbance rejection control (LADRC) algorithm to suppress the multi-modal vibration of flexible beams. Firstly, the dynamic equation of flexible beams is established using the Euler-Bernoulli beam theory. Then, a virtual control vector is introduced to decouple the MIMO system. The effectiveness of the flexible beam model is verified by comparing it with the experimental frequency response. Finally, experimental studies are carried out to analyze the vibration suppression performance of three control forms in suppressing the vibration of flexible beams under first-order modal excitation, second-order modal excitation, multi-modal excitation, variable multi-modal excitation, and multi-modal excitation with random disturbance, respectively. The robustness of the MIMO-DC strategy against parameter perturbations is also studied. The results show that the proposed MIMO-DC strategy not only exhibits excellent control performance for various types of modal excitations but also has strong adaptability to variable multi-modal excitation and strong anti-disturbance ability. Furthermore, the strategy exhibits strong robustness against parameter perturbations.

Design and research of fuzzy PID-based liquid pump performance testing platform

Abstract The pump is a crucial component of agricultural sprayer machinery, directly impacting its operational quality. This paper designs a performance testing platform for pumps. Utilizing a brushless DC motor as the power source for the pump, a closed-loop mathematical model for the brushless DC motor is established. Simulation experiments and indoor experimental tests are conducted. Simulation results indicate that the motor control system exhibits fast response, no overshooting, and excellent dynamic and static performance. Experimental tests on the platform show that the error range of motor speed is less than 0.3%, achieving excellent control performance. The constructed pump performance testing system achieves precise measurement of pump flow rates at different speeds, demonstrating outstanding performance.

Carbon-based phase change composites with directional high thermal conductivity for interface thermal management

Phase change materials (PCMs) can provide a buffer platform for thermal management systems to deal with thermal runaway problems. However, prompt heat dissipation and cost-effective thermal regulation in desired devices are still limited by insufficient thermal transport behavior and liquid leakage of PCMs. Here, we report a facile strategy to develop high-power-density device by carbon-based phase change composites (PCCs) with directional high thermal conductivity. The synergistic effect of self-assembled aligned graphite nanoplatelets (GNPs) inside the resultant PCCs on thermal transport and structural reinforcement enable the PCCs to show high thermal conductivity (in-plane KPCCs of up to 32.86 W m−1 K−1), large heat storage density (158.2–248.3 J g−1), thermal cycling stability, and leakage-proof properties. Furthermore, a PCC-based high-power-density energy device is demonstrated for the tunable thermal regulation by coordinating the orientation of GNPs heat conduction skeleton inside the PCCs with the thermal transport direction of device. The PCC-based module maintains a suitable temperature range of 149–151 °C under various operating conditions and exhibits a decrease of ∼10 °C in the surface peak temperature of power device, showing excellent temperature control performance. Our work holds promising application prospects in the engineering scalable functional PCCs for thermal regulation of electronics and interface thermal management.

A novel programmable gain control low noise amplifier (PGCLNA) design for automatic gain control loop in BLE applications

Bluetooth low energy (BLE) is a widely used short-range communication protocol and BLE receivers frequently encounter transient high-power RF (radio frequency) signals from antennas, leading to receiver saturation. To address this issue, this work proposes a 2.4 GHz programmable gain control low-noise amplifier (PGCLNA), which is a key element in an automatic gain control (AGC) system for BLE receiver applications. The proposed PGCLNA achieves discrete levels of gain using the g m -boosting technique by splitting the negative feedback. The proposed circuit is simulated in UMC 180 nm technology and the maximum gain achieved is 41.4 dB, with a step decrease of 15 dB in discrete levels. The gain variations do not degrade the performance of S11, which is always less than -16 dB. The noise figure (NF) is always minimum of 5.5 dB and in high gain mode, the minimum value of NF is 1.82 dB. The third-order intercept point (IIP3) is −7.621 dBm in the best circumstances and always remains above −10.69 dBm. These results are achieved by utilising 5.4 mW power at 1.8 V of supply voltage. The circuit occupies an area of 732 μm × 771 μm. The proposed PGCLNA design offers a solution to accommodate a wide range of input signals in BLE receivers, excellent gain control, low noise and robust performance characteristics, making it an essential component in an AGC system for BLE receiver applications.

Performance Evaluation and Comparative Analyses of Tuned Inerter Damper for Stochastic Base Excited Structures Based on Hybrid H2/H∞ Optimization

Compared with tuned mass damper, tuned inerter damper (TID) has higher damping efficiency and lightweight characteristic if appropriate optimal methods are selected. Generally, [Formula: see text] or [Formula: see text] optimal methods are adopted to determine the optimal parameters of TID individually. But the vibration mitigation performance of the TID under near resonance frequency band based on [Formula: see text] optimization is less than [Formula: see text] optimization, but the peak frequency response based on [Formula: see text] optimization will be greater than [Formula: see text] optimization at the same time. Dual-characteristic-based vibration control may not be achievable based on [Formula: see text] or [Formula: see text] criteria, respectively. For this reason, hybrid [Formula: see text] optimization can be adopted. In this work, closed-form expressions are derived for the control of structural displacement responses based on hybrid [Formula: see text] optimal method. The vibration mitigation performance of the different optimized TIDs is evaluated considering the single-degree-of-freedom and multi-degrees-of-freedom systems are subjected to typical ground motions excitation. Results illustrate that because of the use of the optimal stiffness ratio of [Formula: see text] optimization and very close value of [Formula: see text] optimizations’ nominal damping ratio in [Formula: see text] optimization, [Formula: see text] optimization shows ‘dual characteristics’ in different excitation situations. Specifically, the peak response and structural fundamental frequencies’ response in displacement frequency response function using hybrid [Formula: see text] optimal methods are between [Formula: see text] and [Formula: see text] optimization, which indicates an excellent and balanced control performance combining the advantages of [Formula: see text] and [Formula: see text] optimal solutions. Hence, this hybrid method is more suitable for more complex ground motions excitation to control critical structural responses instead of single characteristic excitation. And the comprehensive vibration control in dynamic time histories analyses can be achieved by this dual characteristic-based optimal strategy. When designing the specific TID, hybrid [Formula: see text] optimization can be considered as the best choice for its compatibility and high adaptability for complex practical engineering scenarios with random and diverse excitations.

An Output Cooperative Controller for a Hydraulic Support Multi-Cylinder System Based on Neural Network Compensation

The straightness control of a fully mechanized working face is the key technology used in intelligent coal mining, so the position control of a hydraulic support multi-cylinder moving system is of great significance. However, due to the harsh environment of coal mines, complex friction, external disturbances, and the coupling relationship between adjacent cylinders, the accuracy of position control is restricted. Therefore, an output cooperative controller is proposed in this paper for a multi-cylinder system. A high-order sliding mode observer is utilized to estimate the system states with the only available output position signal. A neural network disturbance observer is applied to estimate the lump disturbance of the strict-feedback model, including the system uncertainty, disturbance force and the coupling force between adjacent cylinders. Then, continuous motion position tracking simulation is conducted and the estimation performance of the state observer and neural network is analyzed. Furthermore, a multi-cylinder collaborative control test rig is designed, and experiments based on the actual actions of the hydraulic support are conducted. The results show that the proposed output cooperative controller has an excellent position control performance compared with the traditional proportional–integral controller.

Trajectory compensation based adaptive control (TCAC) for high-precision piezoelectric actuators

Piezoelectric actuator (PEA) has been widely used in micro-systems, due to the advantages of nano-scale precision, rapid response speed, high stiffness, etc. Nevertheless, the intrinsic nonlinearities of PEA, particularly hysteresis, severely deteriorate its positioning and tracking control accuracy. In most existing control algorithms of PEA, the approximate inverse model of hysteresis is developed to compensate for motion error, which requires high modeling accuracy. To avoid the influence of complex modeling procedures and modeling errors on tracking precision, a trajectory compensation-based adaptive control method has been proposed in this paper. Firstly, a simple linear controller is used to establish the closed-loop feedback system and to guarantee the basic tracking performance of the PEA system. And then, the trajectory compensation is designed according to the frequency analysis of the closed-loop control system. Finally, nonlinear adaptive compensation is used to eliminate the residual errors of the above linear frequency domain model. Different from conventional controllers, the proposed method can achieve excellent tracking control performance without any hysteresis model of PEA. The stability and convergence of the proposed method have been theoretically proved. In addition to theoretical guarantees, the proposed method is validated experimentally and demonstrated to achieve the root-mean-square and maximum value of tracking errors to less than 8 nm and 25 nm, respectively, which has a clear accuracy advantage over other methods.

Miniaturized Active-Frequency Selective Surfaces for Low-Power Internet of Things Devices.

With the proliferation of smart devices, the Internet of Things (IoT) is rapidly expanding. This study proposes a miniaturized controllable metamaterial with low control voltage for achieving low-power and compact designs in IoT node devices. Operating at a target frequency of 2.4 GHz, the proposed metamaterial requires only a 3.3 V control voltage and occupies approximately one-third of the wavelength in size. Experimental validation demonstrates its excellent reflective control performance, positioning it as an ideal choice for low-power IoT systems, particularly in the context of miniaturized and low-power IoT node applications.

Study on the Effect of Asphalt Static Conditions on the Tensile Properties of Acidic Aggregate Hydraulic Asphalt Concrete.

Hydraulic asphalt concrete is known for its excellent seepage control performance and strong deformation resistance. This engineering material has widespread applications in the seepage control structures of hydraulic buildings. Recent projects have investigated the use of acidic aggregates to improve economic efficiency. However, they have also highlighted the weaker adhesion between acidic aggregates and asphalt, which necessitates stringent construction process control. This study investigates the impact of resting conditions on the tensile properties of acidic aggregate hydraulic asphalt concrete. The results of the tensile testing indicate that the storage time significantly affects the performance of asphalt concrete. The tensile strength of the specimens without anti-stripping agents decreased from 1.711 MPa to 0.914 MPa after resting periods of 0, 10, 20, and 30 days. The specimens treated with anti-stripping agents also showed a decrease in tensile strength over time, similar to the trend observed in the previous specimens. Digital specimen simulations indicated a decrease in cohesion between the asphalt and the aggregate from 5.375 MPa to 2.664 MPa after 30 days, representing a reduction of 50.44%. To counteract the effect of the storage time on the bonding between acidic aggregates and asphalt, this study recommends reducing the grading index and maximum size of aggregates, decreasing the coarse aggregate content, and selecting smooth aggregate shapes.

Anti-swing control of varying rope length tower crane based on adaptive neural network sliding mode

Due to its complex nonlinear, underdriven, and strongly coupled characteristics, the tower crane will cause the load to swing violently when working, which will cause the tower crane to tip over in serious cases, and there exists a huge safety hazard. In this article, a new artificial neural network sliding mode control method is designed for the control problems of tower crane lifting in position and load swing prevention, which has strong robustness to disturbances and unmodeled dynamics, and ensures that the tower crane lifting is accurately tracked in position and suppresses the load swing at the same time. First, a nonlinear dynamics model of a five-degree-of-freedom tower crane considering the actual working conditions is established. Aiming at the problem that it is difficult to effectively control the nonlinear model of the tower crane system, a new neural sliding mode controller and compensation controller are designed based on the sliding mode control theory and using radial basis function neural network. The neural sliding mode controller is used to approximate the sliding mode equivalent controller with uncertainty and strong nonlinearity, and the compensation controller realizes the compensation of the neural sliding mode controller for the difference between the system control inputs and the uncertainty of the system. The convergence and stability of the proposed control system is rigorously demonstrated using the Lyapunov stability theory. Simulation studies have been carried out to verify the correctness of the model established in this article, as well as the excellent control performance of the control system and the ability to deal with system uncertainty, proving its strong robustness.

Fixed-time trajectory tracking for autonomous surface vehicles via output feedback under various constraints

This paper proposes a novel fixed-time output feedback trajectory tracking control method suitable for autonomous surface vehicles (ASVs). Firstly, various complex uncertainty terms, such as unknown external disturbances, unmodeled errors, and actuator faults are considered in this paper. And these terms are uniformly converted into the total uncertainty terms through model transformation for convenience. Then, a fixed-time extended state observer (FTESO) with the newly expanded state is designed to observe and estimate the total uncertainty terms. Next, the saturation dynamic filter (SDF) is adopted to address the input saturation, and the saturation factors of SDF are incorporated into the trajectory error calculation to reduce the torque mutation caused by an excessive error in the control input. Finally, a fixed-time control law is proposed by taking into account the ASV system error and FTESO estimation state. Simulation experiments show that this method has excellent control performance, and this conclusion is validated through stability proof and case comparison.

Study on Preparation and Humidity-Control Capabilities of Vermiculite/Poly(sodium Acrylate-acrylamide) Humidity Controlling Composite.

This paper focuses on the preparation and evaluation of a novel humidity-control material, vermiculite/(sodium polyacrylate(AA)-acrylamide(AM)), using inverse suspension polymerization. Acrylic acid and acrylamide were introduced into the interlayer of modified vermiculite during the polymerization process, leading to the formation of a strong association with the modified vermiculite. The addition of vermiculite increased the specific surface area and pore volume of the composites. To investigate the moisture absorption and desorption properties of the composites, an orthogonal experiment and single-factor experiment were conducted to analyze the impacts of vermiculite content, neutralization degree, and the mass ratio of AA to AM. According to the control experiment, the addition of vermiculite was found to enhance the pore structure and surface morphology of the composite material, surpassing both vermiculite and PAA-AM copolymer in terms of humidity control capacity and rate. The optimal preparation conditions were identified as follows: vermiculite mass fraction of 4 wt%, a neutralization degree of 90%, and mAA:mAM = 4:1. The moisture absorption rate and moisture release rate of the composite material prepared under these conditions are 1.285 g/g and 1.172 g/g. The humidity control process of the composite material is governed by pseudo second-order kinetics, which encompasses the complete adsorption process. These results indicate that the vermiculite/PAA-AM composite humidity control material has excellent humidity control performance and is a simple and efficient humidity control method.

Research on adaptive coupling trajectory tracking anti-swing control strategy for three-dimensional double-pendulum overhead crane

In the transportation process of a three-dimensional double-pendulum overhead crane, the system is significantly influenced by the pronounced coupling introduced by the double-pendulum effect, posing a considerable challenge for the development of effective anti-swing control strategies. Moreover, uncertainties in certain system parameters and errors in trolley positioning contribute to the complexity of anti-swing control strategy design. To address these practical issues, a control strategy is proposed: First, an S-shaped transport trajectory with minimal positioning error is introduced, incorporating more system parameters into the coupling signal design to enhance system coupling. Based on this, an error-coupled trajectory signal is introduced. Second, the error-coupled trajectory signal is integrated into the energy function, and leveraging adaptive principles, an adaptive coupled trajectory tracking anti-swing control strategy is proposed to estimate uncertain system parameters online. Subsequently, the asymptotic stability of the equilibrium point of the closed-loop system is verified using the Lyapunov techniques and the Barbalat lemma. Finally, through simulations and experiments, it is demonstrated that the proposed control strategy not only ensures precise positioning of the trolley and bridge but also effectively suppresses oscillations of the hook and load, exhibiting excellent control performance. Even in scenarios where system parameters undergo changes or external disturbances are introduced, the proposed control strategy exhibits strong robustness and holds significant practical potential.

Generalized predictive control application scheme for nonlinear hydro-turbine regulation system: Based on a precise novel control structure

Generalized predictive control (GPC), which has excellent control performance and robustness, is only applicable to the control object that can be expressed in the transfer function and is difficult to be directly applied to the hydraulic turbine regulation system (HTRS) that contain extremely complex nonlinearity. Therefore, considering the practical application of GPC in a hydropower station containing the Francis turbine, the double models, real-time feedback linearization module, and a nonlinear characteristic compensator are designed, and a generalized predictive control strategy combining these modules (GPC-DM-RFLM-NCC) is proposed. Firstly, the salp swarm algorithm (SSA) is improved based on the chaotic mapping and Levy flight, an accurate nonlinear hydraulic turbine model is constructed based on the backpropagation neural network (BPNN) and improved SSA (ISSA), and an HTRS simulation platform with nonlinear characteristics is built by combining modules such as water diversion system, servo system, and generator. Then, the irrationality of the commonly used linear hydraulic turbine models in HTRS dynamic process simulation is verified through quantitative calculation. Furthermore, by combining the real-time feedback linearization method and ISSA-based nonlinear characteristic compensator, the GPC-DM-RFLM-NCC is proposed. Finally, the HTRS simulation platform, which consists of GPC, double models, real-time feedback linearization module, and nonlinear characteristic compensator, is built, and the applicability, reliability, and excellent robustness of the proposed GPC-DM-RFLM-NCC are verified by the real data of a hydropower station.

Load Frequency Active Disturbance Rejection Control Based on Improved Particle Swarm Optimization

A load frequency control (LFC) system based on active disturbance rejection control (ADRC) is designed to solve the problem of frequency modulation caused by large-scale renewable energy grid connection. Traditional parameter-tuning methods are inefficient and often fail to achieve desired control outcomes. To overcome this, an improved Particle Swarm Optimization (PSO) algorithm is introduced, incorporating Levy flight and chaotic mapping. This enhanced algorithm combines the long step–length search capability of Levy flight with the rapid exploration of initial solution space using Tent chaotic mapping, enhancing PSO’s global search ability and addressing premature convergence issues. Simulation results demonstrate that the ADRC controller optimized by the improved algorithm exhibits greater robustness and smaller deviation compared to the original algorithm, showcasing its excellent control performance.

Robustness improvement of optimal control in terms of RBFNN with empirical model reduction and transfer learning

This paper proposes a method to compute solutions of optimal controls for dynamic systems in terms of radial basis function neural networks (RBFNN) with Gaussian neurons. The RBFNN is used to compute the value function from Hamilton–Jacobi–Bellman equation with the policy iteration. The concept of dominant system is introduced to create initial coefficients of the neural networks to stabilise unstable systems and guarantee the convergence of policy iteration. Model reduction and transfer learning techniques are introduced to improve robustness of RBFNN optimal control, and reduce computational time. Numerical and experimental results show that the resulting optimal control has excellent control performance in stabilisation and trajectory tracking, and much-improved robustness to disturbances and model uncertainties even when the system responses move to a domain of the state space that is larger than the domain where the neural networks are trained.

Modeling, identification, and high-speed compensation study of dynamic hysteresis nonlinearity for piezoelectric actuator

Hysteresis nonlinearity widely exists in the piezoelectric actuator (PEA), and the hysteresis nonlinearity has strong dynamic characteristics that lead to deterioration of tracking performance. To decrease the positioning error caused by hysteresis nonlinearity, a generalized Bouc-Wen (GBW) hysteresis model and its compensation method are proposed in this paper. First, based on the Bouc-Wen hysteresis model, two asymmetric terms and a second-order IIR filter are applied to describe the asymmetric hysteresis and high-frequency phase lag characteristics of PEA. Then, the model parameters with strong relevance to frequency variation are modified as frequency-dependent parameters. Meanwhile, based on the particle swarm optimization (PSO) algorithm, a novel parameter identification algorithm is designed for identifying the parameters of GBW hysteresis model. Then, an inverse feedforward controller is constructed based on the GBW hysteresis model, and a composite compensation control algorithm combining PID controller and repetitive controller is developed to reduce the unmodeled dynamics errors and unknown external disturbances. Finally, the comparison experiment results show that the accuracy and performance of the GBW model proposed in this paper are much better than the classical Bouc-Wen (CBW) model and the enhanced Bouc-Wen (EBW) model, and the developed compensation controller has excellent control performance and robustness.

Robust attitude tracking control for a variable‐pitch quadrotor with uncertainties

SummaryVariable‐pitch quadrotors have demonstrated their capability to enhance control performance and overcome limitations compared to conventional fixed‐pitch quadrotors, while still facing challenges such as nonlinearity, couplings, uncertainties and so on. This article aims to address the robust attitude control problem of variable‐pitch quadrotors afflicted with parametric uncertainties and unpredictable external disturbances. Initially, a practical actuator allocation strategy for the motor‐propeller is proposed, followed by the development of a nonlinear robust attitude tracking controller capable of addressing all uncertainties. The effectiveness of the proposed controller in ensuring exponential stability of the closed‐loop system, thereby driving the attitude tracking error to a small range within a finite time, has been substantiated. To evaluate the control performance, a hardware‐in‐the‐loop testbed is constructed to conduct real‐time experiments. The experimental results from attitude control case, comparative case, and position control case demonstrate that the designed motor‐propeller allocation method and robust controller are effective in achieving a fast response and excellent control performance.