Acceleration Control Research Articles

Intelligent vehicle obstacle avoidance path-tracking control based on adaptive model predictive control

Abstract. In order to solve the problems of low path-tracking accuracy, poor safety, and stability of intelligent vehicles with variable speeds and obstacles on the road, a double-layer adaptive model predictive controller (MPC) is designed. A vehicle point mass model is used in an obstacle avoidance planning controller, and the safety collision distance model is established according to the distance relationship between the vehicle and the obstacle to improve the driving safety of the vehicle. The design of the path-tracking controller is based on the three-degrees-of-freedom dynamics model. According to the relationship between the predictive horizon and vehicle speed in the MPC algorithm, an adaptive path-tracking control strategy which can update the prediction horizon in real time is proposed to improve the accuracy of vehicle path tracking. To increase the vehicle stability, a sideslip angle and an acceleration control variable are added to the vehicle dynamics model as soft constraint conditions. The proposed method is simulated based on a CarSim and MATLAB/Simulink co-simulation platform. The simulation results show that the maximum lateral path deviation and the maximum centroid sideslip angle of the designed controller are 0.13 m and 0.4∘, respectively. Compared with the traditional MPC, the adaptive MPC maximum lateral path deviation and the maximum centroid sideslip angle are reduced by 0.51 m and 1.57∘, respectively, which proves the effectiveness of the proposed method.

Enhancements of Nonlinear Substructuring Control for Shake Table Experiments on Severely Damaged Structures

This study introduces enhancements for nonlinear substructuring control (NLSC) to achieve simultaneous control of acceleration and displacement in shake table substructuring experiments with severely damaged structures. Although shake table control is greatly affected by a specimen placed on its top and its nonlinear characteristics, accurate control, even with nonlinear characteristics, is essential for experimental purposes. The NLSC was developed for a dynamically substructured system (DSS) scheme involving nonlinear substructures, and its first application to a shake table experiment was performed on a one-storey steel frame. In the experiment, NLSC realised simultaneous control of table acceleration and displacement with a slight nonlinear characteristic within the frame, although its performance degraded as the nonlinearity became stronger. To address this degradation issue, this study introduces two techniques for enhancing NLSC shake table substructuring experiments. One is a composite filtering technique to minimise noise-contaminating displacement data fed back to the table control so that the error feedback action in NLSC can be fully utilised. The other involves a new linear model that is assumed to be more highly damped than the actual system to enhance the stability robustness of NLSC against nonlinear characteristics. After a series of numerical examinations, this study experimentally examined the enhancements on actual shake table experiments using a steel frame. Using the NLSC with the enhancements, a substructuring experiment was successfully conducted; moreover, the NLSC realised simultaneous control of the table acceleration and displacement with nearly 100% accuracy, even with severe nonlinear characteristics.

Design and Analysis of an Energy-Saving Exoskeleton for Carrying Torso-Borne Loads

Carrying a load can affect human locomotion and increase the risk of musculoskeletal injuries. In this paper, we proposed a method inspired by the running posture of ungulate species to reduce metabolic energy by controlling load motion. We developed an energy-saving exoskeleton with an active load CoM adjustment system, which enables accurate control of load CoM acceleration using active disturbance rejection control (ADRC) technology. Finally, we tested the energy-saving exoskeleton system, and the results showed an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$8.82 \pm 4.19\%$</tex-math></inline-formula> decrease in the integrated EMG signals of rectus femoris, as well as an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$8.21 \pm 3.85\%$</tex-math></inline-formula> reduction in net metabolic rate. These findings validate the effectiveness of our energy-saving exoskeleton.

Altruistic cooperative adaptive cruise control of mixed traffic platoon based on deep reinforcement learning

AbstractCooperative adaptive cruise control (CACC) realizes efficient, intelligent control of vehicle acceleration, deceleration, and steering, through inter‐vehicle communication and cooperative control. However, the close combination of the platoon makes it difficult for other vehicles to cut‐in, which can lead to severe traffic jams on certain sections of the road. The control effect of the CACC depends on the platoon penetration rate, which is the percentage of connected and autonomous vehicles (CAVs) in the total number of platoon members. There is no quantitative control method for different penetration rates, and it is difficult to quantify the impact of CACC vehicles on traffic. Therefore, this paper proposes an innovative CACC control method based on deep reinforcement learning (DRL). First, the altruism control and the quantitative control of the car‐following strategy are realized by the virtual car‐following distance method to reduce the exclusivity of the CACC platoon or improve the road utilization efficiency. Second, a more appropriate platoon reward function and collision avoidance method are proposed. Finally, the Car Learning to Act (CARLA) simulator is used. The obtained results confirm that the CACC control of CAVs based on DRL can absorb speed oscillation and improve fuel economy.

Accuracy and reliability of a commercial treatment planning system in nontarget regions in modern prostate radiotherapy.

The currently available treatment planning systems (TPSs) are neither designed nor intended for accurate dose calculations in nontarget regions. The aim of this work is to quantify the accuracy and reliability of nontarget doses calculated by a commercially available TPS. Nontarget doses calculated by the collapsed cone (CC) (v5.2) algorithm implemented in the RayStation (v6) TPS were compared to measured values. Different scenarios were investigated, from simple static fields to intensity modulated radiotherapy (IMRT) and volumetric modulated arc therapy (VMAT) treatment plans. Deviations and confidence limits (CLs) were calculated between results of calculations and measurements-applying both local (δ) and global (Δ) normalization-for various points of interest (POIs). Results were based on a single-institution experience for one clinical test case (prostate) and evaluated against internationally accepted criteria. Overall, the TPS underestimated the nontarget dose by an average of -17.7%±25.3% for IMRT. Quantitatively similar results were obtained for VMAT (-17.6%±21.2%). POIs receiving < 5% of the prescription dose were significantly underestimated by the TPS (p-value<0.05 for both IMRT and VMAT). Dose calculation accuracy was also determined by the contribution of secondary radiation, with measured doses for out-of-field POIs being significantly different from calculated values (p-value<0.01 for both IMRT and VMAT). Although the CLδ in nontarget regions failed the proposed tolerance criteria (40%) for both IMRT (68.8%) and VMAT (52.6%), the CLΔ was within the tolerance limit (4%) for both treatment techniques (1.9% for IMRT and 1.3% for VMAT). No action levels (7%) were exceeded. Based on the currently available benchmarks our TPS is considered acceptable for clinical use, although the dose in some POIs was poorly predicted by the CC algorithm. Some areas were pointed out where TPSs and linear accelerator control systems can be improved.

Fabrication of Automatic Acceleration Control System in Vehicles

In General Indian Road Transporting Conditions is concerned, mischance’s are turning into an everyday cause an endeavor has been made in this venture to diminish such setbacks. In our venture a fast sign is given and programmed braking is connected by removing the fuel supply to the motor when the setup speed is Overpassed. Our Objective is to plan a programmed increasing speed control in activity flag/Sign Board. The setup of the model does not permit abusing the activity rules. This model comprises of an engine with rack and pinion course of action, point of confinement switches and the quickening agent. This venture is basically created to maintain a strategic distance from mishaps because of fast vehicles and furthermore to empower general society to cross the street with no threat from rapid vehicles. Normally the drivers drive the vehicles at rapid Speed without considering general society in speed constrained ranges as well. Despite the fact that the activity controls them we can't accomplish the full reaction from them. Likewise it is impractical to screen those regions at unsurpassed to control their speed. File Terms-Automatic Acceleration, High speed Indication.&#x0D;

Suppression strategy of coupled oscillation and power anti‐regulation of variable speed pumped storage unit with full size converter

AbstractThe ultra‐low frequency oscillation and poor speed regulation performance caused by the fast power control decrease the stability and efficiency of the variable speed pumped storage unit with a full size converter (FSC‐VSPSU). This paper presents a fast frequency control strategy to solve this problem. The small‐signal model of the FSC‐VSPSU in fast frequency mode is established, and the key parameters for the system stability are studied by the eigenvalue method. Aiming at the coupling oscillation mode caused by the governor and generator side converter (GSC), the corresponding control parameters optimisation method is proposed. In order to solve the power anti‐regulation problem, the strategy of combining rotor kinetic energy release, power slope adjustment, and delay acceleration control is proposed. PSCAD and RTDS simulation results show that the proposed strategy can effectively avoid the ultra‐low frequency oscillation caused by the fast power control and significantly improves the frequency and power control performance.

Shake table real-time hybrid testing for shear buildings based on sliding mode acceleration control method

Existing studies involving shake table real-time hybrid testing (ST-RTHT) mainly use displacement-based control methods, which generally have good tracking for displacement but their acceleration tracking may be unsatisfactory. Although three-variable control has been introduced in some cases to improve the tracking performance, this approach cannot consider the influence of system nonlinearities and uncertainties. To expand the applicability of sliding mode control theory in shake table, in this study, a sliding mode acceleration control (SMAC) method is presented to overcome the drawback of traditional sliding mode force control (SMFC), and the information of the force applied by structure to shake table and the unmodeled complex nonlinear force is not required. Based on the proposed control method, the framework of ST-RTHT for shear buildings is developed. In the test system, an inner-loop controller composed of feedforward and feedback controls is designed based on the system transfer function, while the proposed SMAC that has asymptotic stability is integrated as an outer-loop controller. Finally, ST-RTHT for a two-story structure is successfully implemented to demonstrate the effectiveness of SMAC. Experimental results show that SMAC can achieve better synchronization of the boundary conditions between the physical and numerical substructures than SMFC, and thus improves the stability of ST-RTHT and accuracy of structural response reproduction.

Optimal design of SMA damper for vibration control of connected building under random seismic excitation

To alleviate the earthquake induced damages in closely spaced high-rise buildings, linear or non-linear dampers have been frequently employed. Out of different options, the frequently utilized non-linear damper is the yield damper. Yield damper shows good floor displacement control efficiency, but increases peak floor acceleration and leaves large residual deformation at the end of ground motion. Also, it shows degradation in the hysteresis loop; whereas to achieve a good acceleration and displacement control efficiency, it is imperative to have a stable hysteresis loop with negligible residual deformation. In this context, shape memory alloy (SMA) composed damper has been viewed as a potential solution to reduce floor displacement and acceleration, simultaneously. Also, it offers excellent recentering property and good seismic energy dissipation capacity. This research aims to use the above stated superior properties of SMA and investigate the seismic response control efficiency of optimally design SMA damper for connected buildings, and compare with the yield damper. Optimal stochastic responses of connected buildings are estimated under random earthquake, as modeled using the Kanai-Tajimi power spectra. To linearize the non-linear force–displacement behavior of SMA and yield damper, a statistical linearization approach is adopted. The RMS (root mean square) floor acceleration and RMS floor displacement ratios are determined as response outputs for the damper connected buildings. Studies results show that, optimally designed SMA damper is more effective in reducing acceleration of stiff building and displacement of the flexible building than the yield damper. Compared to the yield damper, SMA damper reduces the RMS acceleration ratios by 15 % for the flexible building and 35 % for the stiff building; and RMS displacement by 20 % for the flexible building and 0.5 % for the stiff building. Overall, SMA damper provide much better seismic response control efficiency for connected buildings than yield damper.

Design and implementation of accelerator control monitoring system

Design and implementation of accelerator control monitoring system

Load-side acceleration control based on load-side velocity disturbance observer for geared actuator

This paper focuses on load-side acceleration control of geared actuator that is modeled as a two-inertia system for industrial/non-industrial robot applications. This paper uses a load-side acceleration sensor that is capable of acquiring direct current components. Conventional load-side acceleration control has some high-frequency components due to the noise effect of an acceleration sensor due to the use of two observers that combines acceleration sensor. This paper proposes a load-side acceleration control system based on a load-side velocity disturbance observer and motor-side velocity controller for a geared actuator of an industrial robot with mechanical resonance. The proposed approach employs a motor-side velocity controller and a load-side velocity disturbance observer, which are employed to simplify the plant system, and compensate for the velocity disturbance on the load side, respectively. This approach has a structure in that the noise effect of the acceleration sensor is small compared with the conventional approach. The effectiveness of the proposed load-side acceleration controller is verified via theoretical analysis, numerical simulations, and practical experiments.

Impact angle, speed and acceleration control guidance via polynomial trajectory shaping

An Impact Angle, Speed and Acceleration Control Guidance (IASAG) law against the stationary target is proposed, which is critical for the effectiveness of the air-to-surface guided weapons. It is hard to address multiple terminal constraints problem for unpowered missile, especially including terminal speed constraint, which is uncontrollable state. Based on Line-of-Sight (LOS) angle, a fourth-order polynomial function is designed to make the number of coefficients of the function equal to number of boundary conditions. Through analytic calculation and transformation, the relation between the specified boundary conditions and the coefficients are established. The coefficient equations are reduced to a univariate nonlinear equation whose solution is determined by terminal speed constraint. Based on the characteristic of the nonlinear equation, we propose a Particle Swarm Optimization(PSO) method to find the coefficient that satisfies terminal speed constraint. According to Lyapunov stability theory, an asymptotically stable trajectory tracking controller is designed to track the reference leading angle with respect to range-to-go to guarantee the impact angle, speed and acceleration constraints. The effectiveness of the proposed guidance law is verified through numerical simulations.

Deep reinforcement learning method for turbofan engine acceleration optimization problem within full flight envelope

In order to solve the multidimensional restricted optimization problem of aeroengine acceleration process and improve the acceleration performance of full envelope, a design method of aeroengine full envelope acceleration controller based on deep reinforcement learning is proposed. Firstly, the thermo-aerodynamic principle of the engine acceleration process is analyzed, and the mathematical analytical model of the multidimensional restricted optimization problem of the turbofan engine acceleration process is established. On this basis, the design method of acceleration controller based on twin delayed deep deterministic policy gradient is studied. The proportional integral controller is designed to pursue the asymptotic stability at the acceleration destination and stable control is achieved through switching. Additionally, the flight envelope is divided into regions by clustering to reduce the span of data changes, so as to enhance the network convergence ability of deep reinforcement learning. Finally, based on the similarity conversion method of equal engine inlet total temperature, the acceleration controller of full flight envelope is realized. The digital simulation results demonstrate that compared with the traditional proportional integral differential controller, the acceleration controller based on twin delayed deep deterministic policy gradient shortens the acceleration time up to 48.33%, and the control effect in a large flight envelope (H=0∼10 km, Ma=0∼1.6) has been verified.

Polarization and phase control of electron injection and acceleration in the plasma by a self-steepening laser pulse

We describe an interplay between two injection mechanism of background electrons into an evolving plasma bubble behind an intense laser pulse: one due to the overall bubble expansion, and another due to its periodic undulation. The two mechanisms occur simultaneously when an intense laser pulse propagating inside a plasma forms a shock-like steepened front. Periodic undulations of the plasma bubble along the laser propagation path can either inhibit or conspire with electron injection due to bubble expansion. We show that carrier-envelope-phase (CEP) controlled plasma bubble undulation induced by the self-steepening laser pulse produces a unique electron injector—expanding phase-controlled undulating bubble (EPUB). The longitudinal structure of the electron bunch injected by the EPUB can be controlled by laser polarization and power, resulting in high-charge (multiple nano-Coulombs) high-current (tens of kilo-amperes) electron beams with ultra-short (femtosecond-scale) temporal structure. Generation of high-energy betatron radiation with polarization- and CEP-controlled energy spectrum and angular distribution is analyzed as a promising application of EPUB-produced beams.

Optimal Acceleration or Braking of Massive Flywheels at the Strength Limit

The article poses and solves the problem on time-optimal control of the quasi-static acceleration or deceleration of a massive flywheel by taking into account the strength properties of the material. The dependence of optimal regimes on parameters is investigated and features of optimal regimes are revealed. For disk and long cylindrical flywheels, universal optimal modes of acceleration or deceleration that can be used in the design or operation of inertial energy storage devices are built.

Research and numerical analysis of floating offshore wind turbine based on a novel extended tuned mass damper

Offshore wind turbines will be developed from shallow water to deep water to meet the rapid growth of wind power generation. Floating offshore wind turbine (FOWT) faces complex load challenges, which endanger its safety and service life. Hence, it is urgent to develop a novel damping device to improve the stability of FOWT. In this paper, a novel extended tuned mass damper (ETMD) is proposed. On this basis, a linear quadratic regulator is added to realize the design and simulation of the extended active tuned mass damper (EATMD) control system to reduce the surge response of FOWT. Numerical analysis shows that under the control of ETMD, the surge response of the tower is reduced by 73%, and the frequency modulation width is increased by 55%. Here, under the control of EATMD, the displacement, velocity, and acceleration of the surge response of the tower are decreased by 47.0%, 30.7%, and 24.2%, respectively.

Time series forecasting methods and their applications to particle accelerators

Particle accelerators are complex facilities that produce large amounts of structured data and have clear optimization goals as well as precisely defined control requirements. As such they are naturally amenable to data-driven research methodologies. The data from sensors and monitors inside the accelerator form multivariate time series. With fast preemptive approaches being highly preferred in accelerator control and diagnostics, the application of data-driven time series forecasting methods is particularly promising. This review formulates the time series forecasting problem and summarizes existing models with applications in various scientific areas. Several current and future attempts in the field of particle accelerators are introduced. The application of time series forecasting to particle accelerators has shown encouraging results and promise for broader use, and existing problems such as data consistency and compatibility have started to be addressed.

Efficient OpenCL system integration of non-blocking FPGA accelerators

OpenCL functions as a portability layer for diverse heterogeneous hardware platforms including CPUs, GPUs, FPGAs, and hardware accelerators. However, OpenCL programs utilizing multiple of these devices in the same computing platform suffer from poor coordination between OpenCL implementations of different hardware vendors. This paper proposes a vendor-independent open source method for integrating custom FPGA accelerators into a common OpenCL platform. The accelerators are wrapped in a common hardware interface to enable efficient synchronization and data sharing between devices on the same chip. The provided software connects the accelerator to OpenCL runtime and enables the control of diverse FPGA accelerators with OpenCL command queues.The benefits of the integration methodology are demonstrated by creating FPGA accelerators with different development tools and integrating them together on two different types of FPGA devices while showing minimal integration overhead. Direct memory access of the accelerator to external memory is shown to increase the performance by a factor of 8. Non-blocking execution enabled by the on-chip synchronization between devices is shown to remove a 250 μs overhead from dependent kernel launches. Additionally, as a proof of concept and a case study, a fully OpenCL-controllable computing platform with two devices is implemented on an FPGA to compute CNN inference on a real-world input signal.

Monotone Inclusions, Acceleration, and Closed-Loop Control

We propose and analyze a new dynamical system with a closed-loop control law in a Hilbert space [Formula: see text], aiming to shed light on the acceleration phenomenon for monotone inclusion problems, which unifies a broad class of optimization, saddle point, and variational inequality (VI) problems under a single framework. Given an operator [Formula: see text] that is maximal monotone, we propose a closed-loop control system that is governed by the operator [Formula: see text], where a feedback law [Formula: see text] is tuned by the resolution of the algebraic equation [Formula: see text] for some [Formula: see text]. Our first contribution is to prove the existence and uniqueness of a global solution via the Cauchy–Lipschitz theorem. We present a simple Lyapunov function for establishing the weak convergence of trajectories via the Opial lemma and strong convergence results under additional conditions. We then prove a global ergodic convergence rate of [Formula: see text] in terms of a gap function and a global pointwise convergence rate of [Formula: see text] in terms of a residue function. Local linear convergence is established in terms of a distance function under an error bound condition. Further, we provide an algorithmic framework based on the implicit discretization of our system in a Euclidean setting, generalizing the large-step hybrid proximal extragradient framework. Even though the discrete-time analysis is a simplification and generalization of existing analyses for a bounded domain, it is largely motivated by the aforementioned continuous-time analysis, illustrating the fundamental role that the closed-loop control plays in acceleration in monotone inclusion. A highlight of our analysis is a new result concerning [Formula: see text]-order tensor algorithms for monotone inclusion problems, complementing the recent analysis for saddle point and VI problems. Funding: This work was supported in part by the Mathematical Data Science Program of the Office of Naval Research [Grant N00014-18-1-2764] and by the Vannevar Bush Faculty Fellowship Program [Grant N00014-21-1-2941].

Automated control and optimization of laser-driven ion acceleration

Abstract The interaction of relativistically intense lasers with opaque targets represents a highly non-linear, multi-dimensional parameter space. This limits the utility of sequential 1D scanning of experimental parameters for the optimization of secondary radiation, although to-date this has been the accepted methodology due to low data acquisition rates. High repetition-rate (HRR) lasers augmented by machine learning present a valuable opportunity for efficient source optimization. Here, an automated, HRR-compatible system produced high-fidelity parameter scans, revealing the influence of laser intensity on target pre-heating and proton generation. A closed-loop Bayesian optimization of maximum proton energy, through control of the laser wavefront and target position, produced proton beams with equivalent maximum energy to manually optimized laser pulses but using only 60% of the laser energy. This demonstration of automated optimization of laser-driven proton beams is a crucial step towards deeper physical insight and the construction of future radiation sources.