Adaptive Control Function Research Articles

ISSUES OF OPTIMIZATION OF MONITORING OF WOODLANDS USING LOW-FLYING DRONES

The article is devoted to the optimization of the use of low-flying drones for monitoring forests. The adaptive mode of identification of trees in the forest using low-flying drones has been optimized. A target optimization functional has been formed that displays the average integral value of the frequency of alternation of image elements, i.e. black-and-white pixels in the field of view of the imaging radiometer. The adaptive control function is calculated, indicating the relationship between the flight altitude and the height of trees at which the target functional reaches a minimum. A recommendation is made to avoid this mode in the practice of using drones when monitoring forest areas.

Research on improved partial format MFAC greenhouse temperature control method based on low energy consumption optimization

Temperature is critical to the growth of crops in agricultural greenhouses. Thus, designing a greenhouse temperature controller that maximizes energy savings while maintaining control accuracy is very important. This paper proposes an improved partial format model-free adaptive control method and designs a greenhouse temperature controller based on this method to balance control accuracy and energy consumption. Firstly, a limited energy consumption term is added to the control input cost function of the traditional partial format model-free adaptive control to penalize excessive control input. We derive an improved partial format model-free adaptive control input algorithm and design a greenhouse temperature controller using this algorithm. Then, the controller's sensitive parameters are selected using a Monte Carlo-based parameter sensitivity method. Finally, the whale optimization algorithm is used to optimize the sensitive parameters. The insensitive parameters are set according to experience. This paper, a theoretical study based on simulated experiments, proves the convergent tracking error of the proposed improved partial format model-free adaptive algorithm. Simulation results show that the improved controller minimizes energy consumption while ensuring control accuracy, reducing power consumption by 12.35 % compared to the traditional controller.

Adaptive fault‐tolerant finite‐time control of nonlinear systems based on tuning functions

SummaryThis article discusses the tracking problem of adaptive fault‐tolerant finite‐time control (AFFC) for a class of strict‐feedback nonlinear systems with unmatched disturbances. Unlike most of the existing results, fault‐tolerant control is combined with finite‐time control, adaptive control and tuning functions. The significant virtue of the combination of these schemes lies in the improvement of the system's reliability, rapidity and robustness. This article uses tuning functions to deal with the problem of the over‐parameterization in the traditional backstepping method. The proposed AFFC scheme ensures that the closed‐loop system is semi‐globally practically finite‐time stable in the presence of sensor/actuator faults, and the tracking error converges to a small neighborhood near the origin in finite time. Finally, the effectiveness of the proposed control strategy is verified by the simulation example.

Research on the Adaptive Cleaning System of a Soybean Combine Harvester

This study investigates the adaptive cleaning system of a soybean combine harvester, addressing the issue of low adaptability in matching the cleaning parameters of the air-and-screen cleaning device of domestic combine harvesters to varying soybean extract characteristics. This mismatch results in high cleaning loss and impurity rates during soybean machine harvesting. Through cleaning experiments, we examine the impact on soybean machine harvesting, where the cleaning loss rate accounts for approximately 10.08% of the total loss rate. The weight of the cleaning loss rate is lower than that of the impurity rate. Additionally, we establish a linear relationship between cleaning parameters and the corresponding cleaning loss rate and impurity rate. We design an adaptive control strategy workflow chart and integrate the adaptive cleaning system into the soybean combine harvester. Verification tests confirm the effectiveness of the adaptive control function. Comparative analysis reveals a reduction of 0.19% in cleaning loss rate and 0.98% in impurity rate compared to the air-and-screen cleaning device. The adaptive cleaning system significantly improves cleaning quality during soybean machine harvesting and enhances the intelligent capabilities of the air-and-screen cleaning device. The results provide practical insights and theoretical guidance for the development of high-quality, low-loss cleaning technology in soybean machine harvesting in China.

Adaptive fault-tolerant fixed-time cruise control for virtually coupled train set

In the cruise control of the virtual coupled train set (VCTS), the control algorithm with high stability, high accuracy, and fast convergence speed is the basic requirement for the smooth and efficient operation of the VCTS. To this end, this paper studies the problem of fixed-time cruise control for VCTS. To begin with, by analyzing the dynamic evolution of the VCTS in an actual environment, a second-order VCTS train-following dynamic model is formulated with considering actuator faults, uncertain resistance, unknown disturbances, and control input saturation. Then, an improved barrier functions-based fixed-time sliding model control method is proposed to deal with the state constraints (i.e., safe distance and speed limit). To deal with parametric uncertainty, adaptive fault-tolerant control and core function technologies are integrated into the same framework. Furthermore, by using a function approximation method to handle the control input saturation, an adaptive fault-tolerant fixed-time cruise controller is designed which not only guarantees the fixed-time stability of the VCTS but also copes with parametric uncertainty, state constraints, and input saturation. Finally, the effectiveness of the proposed method is validated in simulation analysis.

Neural Network Trajectory Tracking Control on Electromagnetic Suspension Systems

A new adaptive-like neural control strategy for motion reference trajectory tracking for a nonlinear electromagnetic suspension dynamic system is introduced. Artificial neural networks, differential flatness and sliding modes are strategically integrated in the presented adaptive neural network control design approach. The robustness and efficiency of the magnetic suspension control system on desired smooth position reference profile tracking can be improved in this fashion. A single levitation control parameter is tuned on-line from a neural adaptive perspective by using information of the reference trajectory tracking error signal only. The sliding mode discontinuous control action is approximated by a neural network-based adaptive continuous control function. Control design is firstly developed from theoretical modelling of the nonlinear physical system. Next, dependency on theoretical modelling of the nonlinear dynamic system is substantially reduced by integrating B-spline neural networks and sliding modes in the electromagnetic levitation control technique. On-line accurate estimation of uncertainty, unmeasured external disturbances and uncertain nonlinearities are conveniently evaded. The effective performance of the robust trajectory tracking levitation control approach is depicted for multiple simulation operating scenarios. The capability of active disturbance suppression is furthermore evidenced. The presented B-spline neural network trajectory tracking control design approach based on sliding modes and differential flatness can be extended to other controllable complex uncertain nonlinear dynamic systems where internal and external disturbances represent a relevant issue. Computer simulations and analytical results demonstrate the effective performance of the new adaptive neural control method.

Art Installation Design and Algorithm Research Oriented to Heterogeneous Computing Architecture and Particle Swarm Algorithm

Traditional high-performance computers generally use commercial general-purpose processors. When constructing large-scale parallel computing systems, they will face many challenges in system efficiency, power consumption, system maintenance, and cost. Heterogeneous architectures have begun to become the key to constructing supercomputer systems. In order to improve the optimization efficiency of the particle swarm algorithm, based on the simplified particle swarm algorithm, an improved strategy for fusion of population information is proposed. Only using the optimal position of the individual particle and the optimal position of the population to update the particle position makes the algorithm description simpler, construct a random term that depends on the population information to increase the diversity of the population, and design an adaptive control function equation to balance the algorithm's global detection and local detection. This article aims to study the design and algorithm of art installations for heterogeneous computing architecture and particle swarm algorithm. Aiming at the two heterogeneous system components of GPU and FPGA in heterogeneous computing systems to solve the dual heterogeneous problem between various computing components and applications, a task flow model for heterogeneous computing systems is proposed, which adopts heterogeneous systems. The structure focuses on the deterministic and nondeterministic calculation methods of particle swarm algorithm and carries out parallel research algorithms of particle transport data to analyze the design of art installations. The experimental results show that by making full use of the GPU hardware storage structure to improve memory access speed and reduce cache failure, the combination of particle swarm algorithm and heterogeneous computing system enables the calculation of the carrier components to obtain 3 times the acceleration effect; through the energy analysis model, Heterogeneous computing system components can obtain at least 1.5 times performance improvement.

Optimal Control of BESS for Improved Grid Integration of the Hybrid DFIG/PV System Using Dual-Layer Adaptive Control

In view of the stochastic nature of wind and solar energy, maintaining quality power in grid-connected hybrid energy system is crucial for efficient energy utilization, especially in the presence of non-linear loads. This paper deals with the control and performance assessment of grid-integrated hybrid energy system that comprises of doubly fed induction generator (DFIG) and photovoltaic (PV) system including battery energy storage (BES) as a stand-by supply to meet utility and load demand. In order to maintain power balance and address various power quality issues encountered with hybrid renewable integration along the source and the grid/load end, a dual-layer variable-step size adaptive control is implemented for the grid-interfaced converter of the proposed system. The variable step-size based dual-layer least mean square (LMS) adaptive control functions by extracting weights of various control signals (stator and load current) where the step-size parameter adjusts according to the conditions that limit the maladjustment error and increase the rate of convergence. The DC-link voltage control realized by bidirectional BES control reduces the steady-state error and fluctuations in the voltage. An artificial bee colony (ABC) optimization is adopted for optimal gain tuning of the DC-link voltage controller as a result of which the battery performance and grid-side converter performance is enhanced with the increased power exchange to the grid. The system model is developed in MATLAB/Simulink and controller performance is evaluated for diverse operating conditions including steady-state, unbalanced non-linear load conditions, wind-speed and solar irradiation variation and specified grid power modes.

New threat on formal verification for neural networks: example and fault tolerance

This article details a new threat to NN formal verification that is well known in the formal verification of classical systems: errors in the learned model of a NN could cause the NN to pass formal verification on a property while violating the same property in real life. The solution to this threat for classical systems (which is expert reviews) is inadequate for NN due to their lack of explainability. Here, we propose a detection and recovery mechanism to tolerate it. This mechanism is based on a mathematical diversification of the system's model and the online verification of the formal safety properties. It was successfully implemented and validated on an application example, which, to our knowledge, is one of the most concrete NN formal verification in the literature: the Adaptive Cruise Control function of an autonomous car.

Connected and Autonomous Vehicle Cohort Speed Control Optimization via Neuroevolution

Predictive Energy Management (PrEM) research is at the forefront of modern transportation’s energy consumption reduction efforts. The development of PrEM optimization algorithms has been tailored to selfish vehicle operation and implemented in the form of vehicle dynamics and/or adaptive powertrain control functions. With the progress in vehicle automation, this paper focuses on extending PrEM into the realm of a System of Systems (SoS). The proposed approach uses the shared information among Connected and Automated Vehicles (CAV) and the infrastructure to synthesize a reduced energy speed trajectory at the cohort level within urban environments. Neuroevolution is employed to incorporate a generalized optimum controller, robust to the emergent behaviors typical of multi-agents SoS. The authors demonstrated the use of heuristics and systems engineering processes in abstracting and integrating the resulting neural network within the control architecture, which enables novel added-value features such as green wave pass/fail classification and e-Horizon velocity prediction. The resulting controller is faster than real-time and was validated with a multi-agent simulation environment and on a real-world closed-loop track at the American Center for Mobility (ACM). The GM Bolt and Volt CAV mixed cohort testing at ACM demonstrated energy reductions from 7% to 22% depending on scenarios.

Graphene perfect absorber with loss adaptive Q-factor control function enabled by quasi-bound states in the continuum

Numerous device structures have been proposed for perfect absorption in monolayer graphene under single-sided illumination, all of which requires the critical coupling condition, i.e., the balance between the loss of graphene and the leakage rate of the device. However, due to the difficulty of the precise control of the quality of synthesized graphene and unwanted doping in graphene transferred to the substrate, the loss of graphene is rather unpredictable, so that the perfect absorption is quite difficult to achieve in practice. To solve this problem, we designed a novel perfect absorber structure with a loss adaptive leakage rate control function enabled by the quasi-bound states in the continuum (BIC) and numerically demonstrated its performance. Our designed device is based on a slab-waveguide grating supporting both the quasi-BIC and the guided-mode resonance (GMR); the quasi-BIC with an adjustable leakage rate controlled by an incident angle is responsible for absorption, while the GMR works as an internal mirror. Since the proposed device scheme can have an arbitrarily small leakage rate, it can be used to implement a perfect absorber for any kind of ultrathin absorbing media. Due to the simple structure avoiding an external reflector, the device is easy to fabricate.

Model-free adaptive nonlinear control of the absorption refrigeration system

Based on the model-free adaptive control (MFAC) theory, the temperature tracking control problem of single-effect LiBr/H2O absorption chiller is explored. Due to the complex nonlinearity and strong coupling characteristics of the absorption refrigeration system, model-free adaptive control strategy is designed for its temperature tracking control. Nevertheless, the traditional model-free adaptive control has a slow tracking speed and poor denoising ability. In order to improve its control effect, output error rate is added to the objective function and new control laws of model-free adaptive control with output error rate (MFAC-OER) have been derived through an exhaustive convergence and stability analysis. The input information and output information of the absorption refrigeration system, namely the hot water pump frequency and chilled water outlet water temperature, are combined. The data model of the absorption refrigeration system is subsequently deduced using a compact format dynamic linearization method. Next, based on the single-effect absorption chiller experimental platform in our laboratory, its sixth-order dynamic model is built. Finally, the effectiveness and practicability of the improved control strategy are illustrated by numerical simulations and experimental operating data from our laboratory as well as by the dynamical model of the absorption chiller.

GNSS-technologies in testing automotive ADAS-system

In this article modern technologies are reviewed, they are used in testing ADAS systems, the principle of which is based on the use of global navigation systems (GNSS) and the using of differential corrections. The specialists of the Nizhny Novgorod State Technical University n. a. R. E. Alekseev have developed a schematic diagram in which Racelogic measuring equipment is used. The performance of the testing system was verified during tests of a bus based on a light commercial vehicle GAZelle Next while simulating the operation of the adaptive cruise control function. In the conclusion, graphs of a number of measured parameters are presented, the nature of the change in time confirms the correct functioning of the measuring equipment. Keywords: GNSS, Glonass, GPS, ADAS, testing, radiocommunication, RTK

Empirical study on car-following characteristics of commercial automated vehicles with different headway settings

A recent study (Li, 2020) analytically predicted tradeoffs between automated vehicle (AV) following characteristics on safety, mobility, and stability using a parsimonious linear car-following model. This work aimed to verify the key theoretical findings in the above study with empirical experiments using commercial AVs, e.g., vehicles with adaptive cruise control (ACC) functions. We collect high-resolution trajectory data of multiple commercial AVs following one another in a platoon with different headway settings. Parsimonious linear AV-following models that capture the first-order parameters on safety, mobility, and stability aspects are estimated with the data. The estimation results of the key parameters validate several theoretical predictions predicted by Li (2020). Specifically, it was found that as the time lag setting increases, the corresponding safety buffer decreases, indicating that AV safety could be improved with less pursuit of AV mobility or, conversely, AV mobility improvement may come at a cost of more stringent safety requirements. Also, as the time lag setting increases, AV string stability increases, indicating that stop-and-go traffic potentially could be dampened by compromising AV mobility. With this, one possible explanation to the observed string instability of commercial AV following control (i.e., ACC function) is that automakers may prefer to ensure a relatively short headway (and thus better user experience on vehicle mobility) at a cost of compromising string stability. It was also found that as the time lag increases, the cycle period of traffic oscillations gets longer, and the oscillation amplification gets smaller, which supports the tradeoff between mobility and stability. On the other hand, field experiments revealed issues beyond the predictivity of a simple linear model. That is, vehicle control sensitivity factors vary across different speed and headway settings, and the model estimation results for key parameters are not consistent over different speed ranges. This opens future research needs for investigating nonlinearity and stochasticity in the AV following modeling.

Estimation of the Closest In-Path Vehicle by Low-Channel LiDAR and Camera Sensor Fusion for Autonomous Vehicles.

In autonomous driving, using a variety of sensors to recognize preceding vehicles at middle and long distances is helpful for improving driving performance and developing various functions. However, if only LiDAR or cameras are used in the recognition stage, it is difficult to obtain the necessary data due to the limitations of each sensor. In this paper, we proposed a method of converting the vision-tracked data into bird’s eye-view (BEV) coordinates using an equation that projects LiDAR points onto an image and a method of fusion between LiDAR and vision-tracked data. Thus, the proposed method was effective through the results of detecting the closest in-path vehicle (CIPV) in various situations. In addition, even when experimenting with the EuroNCAP autonomous emergency braking (AEB) test protocol using the result of fusion, AEB performance was improved through improved cognitive performance than when using only LiDAR. In the experimental results, the performance of the proposed method was proven through actual vehicle tests in various scenarios. Consequently, it was convincing that the proposed sensor fusion method significantly improved the adaptive cruise control (ACC) function in autonomous maneuvering. We expect that this improvement in perception performance will contribute to improving the overall stability of ACC.

A Modified Adaptive Improved Mapped WENO Method

We propose a new family of mapped WENO schemes by using several adaptive control functions and a smoothing approximation of the signum function. The proposed schemes introduce the adaptivity and admit an extensive permitted range of the parameters in the mapping functions. Consequently, they have the capacity to achieve optimal convergence rates, even near critical points. Particularly, one of these new schemes with fine-tuned parameters illustrates a significant advantage when solving problems with discontinuities. It produces numerical solutions with high resolution without generating spurious oscillations, especially for long output times.

Algorithmic Support of Adaptive Automatic Control Systems with Data Compression

Introduction. The exponential growth of measurement information caused by ongoing complication of technical and production facilities necessitates the development of improved or brand new information and measurement systems, including those performing adaptive automatic control functions. Automatic criteria-based selection and reduction of measurement information continuously supplied by multi-parameter sources characterizing the objects under study require algorithms ensuring reconfiguration of automatic control systems during operation. In comparison with automatic control systems based on time-division channelling, the considered adaptive systems provide timely information on the pre-emergency and emergency operation of a facility.Aim. To develop an algorithmic support for adaptive automatic control systems using asynchronous-cyclic and parallel-sequential operating algorithms, as well as to compare the proposed algorithms in terms of their, control reliability, compression ratio, operation speed and the error associated with multi-channelling.Materials and methods. The algorithms proposed for supporting the operation of adaptive systems were developed on the basis of queuing theory and simulation modelling using the MatLab/Simulink programming languages, C++.Results. The developed algorithmic support for automatic control systems based on asynchronous-cyclic analysis of deviations allows the amount of redundant information to be reduced by more than 4 times and the operation speed to be increased by 1.5 times. The developed algorithmic support for automatic control systems based on parallel-sequential analysis of deviations allows the error associated with multi-channelling to be reduced by 1.4 times, thereby bringing the control reliability of such systems closer to that of continuous-control systems. An analysis of the graphs of the error associated with multi-channelling showed that the automatic control systems based on parallel-sequential operational algorithms are invariant to the law of distribution of input quantities, compared to the systems based on asynchronous-cyclic operational algorithms.Conclusions. The proposed algorithmic support can significantly decrease the redundancy of information and improve the metrological characteristics of automatic control systems. The use of the developed algorithms in automatic control systems based on time-division channelling render their control reliability comparable with that of continuous-control systems.

Energy-Optimal Adaptive Cruise Control for Electric Vehicles Based on Linear and Nonlinear Model Predictive Control

This paper presents a novel function of energy-optimal adaptive cruise control (EACC) for electric vehicles based on model predictive control (MPC), which plans the host car's speed trajectory in real time for higher energy efficiency by taking look-ahead traffic information and road conditions into consideration. After linear MPC (LMPC) is formulated in time domain, a nonlinear MPC (NMPC) formulation in space domain is proposed in this paper to overcome the drawbacks of LMPC in time domain. To cope with computational complexity of NMPC in space domain, the nonlinear equality constraints are relaxed to inequality constraints to yield a convex optimization problem. Moreover, it is proven that the relaxed optimization problem can be recast as a second-order cone programming (SOCP) problem, for which the efficient numerical optimizers exist. The performance of NMPC is evaluated in simulation, which is compared with the time domain LMPC and a theoretically optimal receding horizon dynamic programming (RH-DP) solution. Results indicate that the proposed space domain NMPC outperforms LMPC in time domain and the optimal solution of NMPC is very close to the result of RH-DP.

Adaptive Finite-Time Stabilization of Stochastic Nonlinear Systems Subject to Full-State Constraints and Input Saturation

In this article, the adaptive finite-time tracking control is studied for state constrained stochastic nonlinear systems with parametric uncertainties and input saturation. To this end, a definition of semiglobally finite-time stability in probability (SGFSP) is presented and a related stochastic Lyapunov theorem is established and proved. To alleviate the serious uncertainties and state constraints, the adaptive backstepping control and barrier Lyapunov function are combined in a unified framework. Then, by applying a function approximation method and the auxiliary system method to deal with input saturation respectively, two adaptive state-feedback controllers are constructed. Based on the proposed stochastic Lyapunov theorem, each constructed controller can guarantee the closed-loop system achieves SGFSP, the system states remain in the defined compact sets and the output tracks the reference signal very well. Finally, a stochastic single-link robot system is established and used to demonstrate the effectiveness of the proposed schemes.

Primary frequency control with BESS considering adaptive SoC recovery

In this article, a control method which combines adaptive droop control with adaptive state of charge (SoC) recovery control is proposed for battery energy storage system (BESS) to participate in primary frequency regulation (PFR), aiming to improve the system frequency dynamics and contribute to the long-term performance of BESS during PFR. Firstly, based on the frequency operation characteristics of power grid and the economic operation requirements of BESS, an adaptive droop control function of BESS participating in PFR is constructed. Secondly, the recovery power of BESS is determined by regarding SoC as feedback signal. Then, according to the frequency signal of power grid with the operation characteristics of conventional generator units (CGUs), the limiting coefficient of recovery power for BESS is designed. Furthermore, based on the recovery power and the recovery power limit coefficient, the adaptive recovery power of BESS is obtained, which avoids the negative impact of BESS on the operation of CGUs when restoring SoC. Finally, according to the adaptive droop control of BESS with the adaptive SoC recovery control, a comprehensive control method is designed to maintain the proper SoC during the whole operation of BESS, which indirectly reduces the frequency adjustment burden of CGUs. The performance of the proposed method is validated through case studies under conditions of various power variation.