Control Engineering Practice最新文献

Energy-efficient trajectory planning aims to optimize the economic performance for autonomous vehicles on the premise of ensuring driving safety, which excavate the energy saving potential and further improve the driving mileage. In this research, a curve splicing energy-efficient trajectory planning method based on surrounding vehicles trajectory prediction is presented. The long short-term memory (LSTM) neural network is adopted to construct the trajectory prediction model, and the hyperparameters of the LSTM are optimized by particle swarm optimization (PSO). To make the energy-efficient decision, the energy-efficient estimation model with motor MAP is developed by the correlation between vehicle driving energy consumption and motor efficiency, and the energy-efficient decision function was designed based on the average efficiency of behavior switching and the target behavior efficiency. Furthermore, a trajectory planning method with hierarchical planning of guide line and vehicle speed is presented based on B-spline curve and rolling dynamic programming (RDP). Via the traversal test, the dynamic adjustment of the guide line structure parameters is realized, and the RDP speed optimization objective function is designed with the goal of energy-efficiency. To precisely and rapidly control the EVs to track the reference trajectory, a model predictive control (MPC) with the goal of traceability was proposed. Eventually, the effectiveness of the energy-efficient trajectory planning algorithm is verified in the urban and the expressway condition respectively. The results show that the energy-efficient performance of the algorithm application is obvious in the expressway condition, and the average energy consumption improving rate is 11.11%.

Accurate model development is essential for effective model-based control of Reactivity Controlled Compression Ignition (RCCI) engines. However, due to the intricate nature of engine combustion process, achieving a precise model that can capture the complex dynamic behavior and ensure high control performance poses a significant challenge. In this paper, we propose an uncertainty-aware output feedback model predictive control approach for efficient combustion management in RCCI engines. In contrast to the previously developed approaches, this method adopts a data-driven approach within the linear parameter-varying (LPV) framework for model development. To address the model mismatch between the LPV model and the real system/data, Bayesian Neural Networks (BNNs) are employed which provide the probability distribution of the uncertainties. The BNNs enable the formation of a scenario tree, effectively characterizing the range of potential uncertainties in the system. Through the implementation of scenario-based model predictive control, our approach ensures high tracking performance for the RCCI engine in the presence of modeling uncertainties and measurement noise. Extensive simulations and experimental validations demonstrate the superiority of our uncertainty-aware model predictive control over traditional control strategies.

It is shown how nonlinear joint stiffness in industrial robots can be determined quickly and accurately through a combination of statistical linearization and optimized data acquisition configurations. The statistical linearization is carried out using the histogram of the measured motor torques. The result of this linearization is used in a criterion that is minimized to determine optimal configurations for data collection. The proposed approach is validated using data from both simulations and experiments with a medium-size industrial robot. In both cases, there is a significant improvement in accuracy compared to both using conventional linearization and collecting data in a larger but random set of configurations.

Sensor fault and output quantization are common issues acting on vehicle platoon, and they may lead to performance deterioration, instability and even insecurity of the platoon. Therefore, this paper investigates the fault-tolerant control (FTC) problem of vehicle platoons with regard to the above two issues. Considering the probabilistic sensor fault and quantized measurement signals, an unknown input observer (UIO) based fault detection algorithm with adaptive threshold is developed for sensor health status monitoring. Then, an augmented vehicle platoon model is constructed by introducing a low-pass output filter, and a robust UIO is established for state reconstruction. Based on the above results, a fault-tolerant control scheme is exploited by employing the back-stepping control method and adaptive radial basis function neural network (RBF NN) approximation technique, which is proved to be capable of achieving the time-domain string stability (TSS) of vehicle platoons in the presence of sensor fault and output quantization. Simulation results demonstrate the effectiveness of the proposed algorithms.

In this paper, the problem of adaptive fixed-time fault-tolerant tracking control is investigated for rotary steerable drilling tool systems (RSDTSs). Markov jump system (MJS) is used to describe the RSDTS with varying parameters which are induced by the changing environment. By employing the smooth projection operator technique, adaptive laws are established to estimate the faults. Based on the fault compensation strategy, a new adaptive fixed-time fault-tolerant tracking control scheme is proposed to ensure that the RSDTS is globally stochastically practically fixed-time stable. In addition, to reduce the computational burden in the backstepping framework, the derivatives of the virtual control are directly derived using command filters. Finally, the experiment performed on the rotary steerable drilling tool systems prototype is exploited to demonstrate the feasibility and effectiveness of the proposed method.

The cooperative transportation problem for multiple wheeled mobile robots (WMRs) via formation control is investigated in this paper. A formation control algorithm based on an embedded technique is proposed for cooperative transportation of a shared object using $n$-WMRs. Instead of relying on the conventional design philosophy directly based on formation errors, the proposed algorithm is divided into two parts by considering the communication topology and WMRs’ dynamics “separately”. The first part involves a distributed signal generator that generates desired trajectories for the WMRs based on their initial positions, the formation vector, and the desired trajectory of the object. The second part consists of tracking controllers to enable the WMRs to track their desired trajectories. The proposed algorithm is distributed and differs from the existing cooperative transportation algorithms, as it eliminates the requirement for all WMRs to know the object’s position. Moreover, it exhibits remarkable compatibility and features a concise modular design. With the proposed algorithm, WMRs achieve formation in finite time. Theoretical proof supports the effectiveness of the proposed algorithm, which is further validated through several conducted experiments.

Model Predictive Control algorithms have been recently developed for controlling grid-tied converters. However, the inclusion of the converter switching constraints in the optimization problem and the high computational burden are some of the main challenges of these algorithms. In this way, this paper proposes a Fast Finite Control Set Model Predictive Control algorithm with a low computational burden for a three-phase Neutral Point Clamped inverter considering its switching constraints. Initially, the vector with the unconstrained solution in the line-to-line voltage coordinates is obtained to minimize the current tracking error. Then, it is limited to ellipses as an intermediate step to ensure that the selected voltage vector is feasible and to restrict the switching transitions. The constrained vector is rounded to the nearest inverter line-to-line voltage vector to be implemented in the next sampling period. The NPC redundant phase-voltage vectors are generated online to avoid the potentially destructive switching transitions. The neutral point is balanced by minimizing a cost function, considering the obtained redundant phase voltage vectors, and is evaluated at most twice in each sampling period. As both control objectives are treated in a cascaded sequence, the proposed Fast FCS-MPC avoids the design of weighting factors and has the advantages of low computational burden, fast transient response, and good steady-state performance. Finally, Hardware-in-the-Loop results are presented to compare the proposed Fast FCS-MPC to other strategies presented in the literature, and the effectiveness of the proposed algorithm is also demonstrated by means of an experimental prototype.

Refrigeration devices designed for use in industrial environments are typically equipped with universal control algorithms, which require a minimal number of signals and parameters to ensure satisfactory device operation. These algorithms are typically of the PID type. This study elaborates upon the impact of using a dedicated hybrid controller, which was designed with specific consideration of the operating conditions of a given refrigeration device. The identified nonlinear Drycooler characteristics were used to support the controller at steady-state operating conditions. The system dynamics were supervised by a dedicated, experience-based designed hybrid fuzzy logic Mamdani type controller for both freecooling and compressor modes. The switchable configuration of the MISO control architecture results in a reduction in overshoots and oscillations in the system, a decrease in the time necessary for stabilization, a reduction in CO${}_2$, and an increase in control quality and energy efficiency.

The solution purification process includes multiple continuous reactors. Setting the key technical indicators of each reactor through global optimization is the prerequisite for realizing the optimal operation of the entire process. Affected by fluctuations in inlet conditions, adjustments of operating parameters, and random disturbances, the operating status of the solution purification process will change accordingly, causing the optimal value settings based on global optimization to become no longer applicable. To ensure the applicability of the optimal value settings as the process changes and considering that the production data collected at different time scales contain different process information, this study proposes a dynamic correction method for the optimal value settings of the solution purification process at multiple time scales. First, considering the low-frequency testing data that can reflect the operation effect, the low-frequency correction is realized by combining mechanism knowledge and expert experience. Second, based on the characteristic that the high-frequency detection data can reflect the changing operating status in time, a supervised self-organizing map method is proposed to classify the changing trends in the operating status. Finally, an integrated, spatiotemporal, just-in-time learning method (with multiple changing trends in the operating status) is proposed to realize high-frequency correction. The experimental results show that the proposed method can dynamically correct the optimal value settings and reduce resource consumption while ensuring product quality.

This paper employs a deep reinforcement learning algorithm named Twin Delayed Deep Deterministic algorithm into autonomous navigation in intelligent transportation systems. It trains a fully connected neural network model in a simulation environment, which outputs the expected linear and angular velocity of the vehicle based on real-time data measured by embedded sensors. Through continuous epochs of training, the model gradually navigates the vehicle to reach a provided destination by making rational motion decisions at each discrete time instant without knowing global environment information. Especially, to improve the model’s generalization ability across various scenes, an input preprocessing function is proposed to eliminate the singularity and uniformity of raw input data. A large number of simulation tests are carried out, where the proportion that the vehicle moves from a start position to a destination without collision within a specified limited time exceeds 90%. The remaining failures are mainly due to the vehicle’s inability to approach the destination immediately adjacent to obstacles for its safety. Furthermore, traditional mapless navigation algorithms suffer from locally optimal solutions in the face of U-shaped obstacles. This paper introduces a virtual obstacle mechanism designed to prevent the vehicle from entering the U-shaped region, effectively addressing the aforementioned issue. Finally, the model trained from the simulation environment can be directly loaded onto a physical vehicle without considering the different processor architectures. Large quantities of experiments show that the model improves the autonomous navigation capability of vehicles when global environment information cannot be obtained by the system, which optimizes the functions of the navigation module in intelligent transportation systems.