Control Engineering Practice最新文献

Modified uncertainty and disturbance estimator (MUDE) based control shows great control performance and robustness against unexpected dynamics in the presence of time delay. However, there are still some problems that have not been analyzed theoretically but actually hinder its further applications, such as noise amplification and control signal fluctuations under delay mismatch. On the other hand, the trade-off between robustness and control performance is still a critical issue in MUDE based controller design. However, some doubts arise when extending the single-loop sensitivity function analysis method to MUDE (multi-loop) control systems. To this end, this paper comprehensively analyzes the performance of MUDE, including control performance and robustness, and proposes some practical improvements to address its issues (denoted as IUDE). An ambiguity in analysis of sensitivity functions for multi-loop control systems is first clarified. Then, based on the two-degree-of-freedom equivalent control structure and sensitivity functions, the control performance and robustness of the MUDE based control system are analyzed in detail, including noise sensitivity and control signal response. Finally, some practical improvements are proposed and a quantitative tuning rule is also derived to facilitate its application. Simulation results show that the proposed control method effectively mitigates the problems of noise amplification and control signal fluctuation while almost not degrading the control performance. Water-tank control experiments have also been performed to validate the efficacy of the proposed method, which depicts a promising prospect in control practice.

In the field of heavy vehicle stability research, traditional safety requirements are often based on static scenario settings. However, the complexity and variability of actual road environments require safety control strategies that can be adapted to different driving conditions and environmental changes in real-time. To address this challenge, the paper proposes a coordinated control strategy for yaw and roll stability that considers the dynamic safety requirements. First, a quantitative analysis method for vehicle stability is proposed based on the dissipated energy theory, taking into account the lateral-vertical dynamics coupling characteristics. Additionally, a dynamic safety requirements identification model is developed by integrating the vehicle's future driving risk prediction algorithm. To meet dynamic safety requirements, a dynamic weight model predictive control method based on randomized ensembled double Q-learning reinforcement learning is designed. This method adjusts the control weights of yaw and roll stability online to flexibly address various destabilization risks, aiming to achieve more precise and efficient stability control. Through simulation and experimental verification, the results demonstrate that the proposed coordinated control strategy can effectively enhance the stability and safety of heavy vehicles in complex and dynamic driving environments.

Closed-loop stability of control systems can be undermined by actuator faults. Redundant actuator sets and Fault-Tolerant Control (FTC) strategies can be exploited to enhance system resiliency to loss of actuator efficiency, complete failures or jamming. Passive FTC methods entail designing a fixed-gain control law that can preserve the stability of the closed-loop system when faults occur, by compromising on the performance of the faultless system. The use of Passive FTC methods is of particular interest in the case of underwater autonomous platforms, where the use of extensive sensoring to monitor the status of the actuator is limited by strict space and energy constraints. In this work, a machine learning-based method is formulated to systematically synthesise control laws for systems affected by actuator faults, encompassing partial and total loss of actuator efficiency and control surfaces jamming. Differently from other methods in this category, the closed-loop stability is formally certified. The learning architecture encompasses two Artificial Neural Networks, one representing the control law, and the other resembling a Control Lyapunov Function (CLF). Periodically, a Satisfiability Modulo Theory solver is employed to verify that the synthesised CLF formally satisfies the theoretical Lyapunov conditions associated to both the nominal and faulty dynamics. The method is applied to three marine test cases: first, an Autonomous Underwater Vehicle performing planar motion and subjected to full loss of actuator efficiency is investigated. Next, a study is conducted on a hybrid Underwater Glider with a pair of independent twin stern planes jamming at a fixed position. Finally, partial loss of effectiveness is considered. In all three scenarios, the system is able to synthesise stabilising control laws with performance degradation prescribed by the user. Unlike other machine-learning based techniques, this method offers formal stability certificates and relies on limited computational resources rendering it possible to be run on unassuming office laptops. An open-source software tool is developed and released at: https://github.com/grande-dev/pFT-ANLC.

Miniaturizing devices and working in real-time in a non-invasive manner are prerequisites for studying microfluidic processes in Lab-on-a-chip. In this study, we present a novel system which uses integrated optical technology for the real-time control of a two-phase microfluidic process as a proof of concept for system-on-chip development. The integrated system is composed of a micro-optofluidic device specifically designed to have direct optical access to flow detection, avoiding discrete opto-mechanical components, and a microcontroller to manage actuation and sensing devices. A two-phase process, i.e. a sequence of two immiscible fluids, flows inside the microchannel and represents the presented application example. The objective is to control the fluids’ intermittency by imposing proper input flow rates. A linearized model of the process has been determined based on a data-driven identification approach and subsequently validated. Constrained Model Predictive Control (MPC) has been selected over Proportional Integral Derivative (PID) and Linear Quadratic Regulator (LQR) to regulate the input flows. Numerical simulations proved MPC’s better capabilities of balancing high accuracy and variations in the input commands throughout the control process. The combination of two extensive experimental campaigns show the presented approach’s validity and the integration of the entire framework into a simple and portable system suitable for various chemical and biomedical applications.

Changing operating conditions is a common phenomenon in the distillation unit (DU), which poses difficulties to production operations. To fully cope with varying operating conditions and further improve production performance, an interactive operation optimization strategy (OOS) with operating condition supervision is proposed in this work. In this strategy, the operating condition supervision module perfectly cooperates with the interactive optimization strategy to reduce the two major performance losses incurred after changes in operating conditions. The bidirectional interaction between the optimization layer and the control layer during operation can eliminate losses and hazards caused by delayed response and inter-layer mismatches, ultimately achieving optimal closed-loop performance. Through detailed analysis of the specific industrial behavior, it provides professional support for the production operations under varying operating conditions. It is worth mentioning that a convolutional neural network (CNN) process model based on parameter transfer is established. It can be fine-tuned online. Experimental results show the proposed strategy can flexibly and effectively handle changes in operating conditions. The proposed OOS improves the product qualification rate and has broad application prospects in industrial processes.

In robotic orthopedic surgeries, the intraoperative optical tracking system (OTS) is widely used to obtain the spatial relationship between the patient's lesion and surgical instrument via the use of positioning tools. Usually, the OTS remains stationary during surgery. If the line of sight (LoS) occlusion occurs, the surgery will be interrupted. To address this issue, this paper proposes a novel optimization scheme to proactively adjust the OTS viewpoint before occlusion occurs, guaranteeing that the LoS remains unobstructed by the occluder. To simplify the analysis process, the excessive number of LoSs from the OTS to the positioning tools are replaced by visual cone (VC) models. Subsequently, an optimization scheme is formulated to generate a desired viewpoint for the OTS and an occlusion-free adjusting trajectory for the navigation robot. Ultimately, the effectiveness of the proposed method is validated via simulations and experiments. Experimental results show that the proposed method is capable of maintaining the occlusion-free between VCs and the occluder as the occluder approaches the VCs. This active navigation technique is promising for the safe operations of robotic orthopedic surgeries.

A control scheme for the multi-gated perimeter traffic flow control problem of monocentric cities is presented. The proposed scheme determines feasible and optimally distributed input flows for various gates located at the periphery of a protected network area. A parsimonious model is employed to describe the traffic dynamics of the protected network. To describe traffic dynamics outside of the protected area, the basic state-space model is augmented with additional state variables to account for vehicle queues at store-and-forward origin links at the periphery. The multi-gated perimeter flow control problem is formulated as a convex optimisation problem with finite horizon, and constrained control and state variables. This scheme aims to equalise the relative queues at origin links and to maintain the vehicle accumulation in the protected network around a desired set point, while the system’s throughput is maximised. For real-time control, the optimal control problem is embedded in a rolling-horizon scheme using the current state of the whole system as the initial state as well as predicted demand flows at origin/entrance links. Furthermore, practical flow allocation policies for single-region perimeter control strategies without explicitly considering entrance link dynamics are presented. These policies allocate a global perimeter-ordered flow to a number of candidate gates at the periphery of a protected network area by taking into account the different geometric characteristics of origin links. The proposed flow allocation policies are then benchmarked against the multi-gated perimeter flow control. A meticulous study is carried out for a 2.5 square mile protected network area of San Francisco, CA, including fifteen gates of different geometric characteristics. The results showed that the proposed approach is able to manage excessive queues outside of the protected network area and to optimally distribute the input flows, which confirms its efficiency and equity properties. Similar policies are expected to be utilised for dynamic routing and road pricing.

The dynamic response of control valves directly affects the safe and efficient operation of industrial control loops. Valve stiction is a common and persistent issue that causes oscillations in control loops. The stiction behavior of valves has received widespread attention from researchers worldwide in the past two decades. The developed valve stiction models have been widely applied in the detection, quantification, and compensation research of sticky control valves. However, how to more accurately characterize stiction behavior still requires efforts. Most data-driven models do not consider the effects of dynamic response on the stiction behavior. In this paper, the inconsistency of the representative stiction models is discussed during the unidirectional motion of the valve stem, and potential improvements are revealed. An experimental device for valve stiction has been designed. This device can replicate valve stiction caused by tight packing, and measure valve position and friction through smart positioner and force sensor. The second-order dynamic system for the sticky valve is constructed by the physical model, and the response time of the valve is calculated and verified combined with the experiment. The effects of sampling interval on stiction behavior are discussed. On this basis, an improved valve stiction model considering the dynamic response of the valve is proposed, which supplements the situation of stiction during the unidirectional motion of the valve stem. The proposed model can be applied in control systems with fixed sampling intervals that involve sticky valves. It can also be extended to control systems with variable sampling intervals. This work contributes to evaluating the performance of control systems with sticky valves and providing reference value for the detection, quantification, and compensation of valve stiction.

Fault diagnosis is important for maintaining safety in industrial scenarios. Due to the complex operating conditions, there is usually a domain shift between training (source) data and testing (target) data. Recent years have witnessed the emergence of numerous transfer learning methods dealing with the domain shift. However, existing methods often fail to deal with the situation where target data are unavailable. Moreover, the majority of these methods require aggregating data distributed across various users (nodes) for model training, raising privacy concerns. Despite existing federated learning methods can protect privacy, they mostly rely on a central server, which may lead to a single point of failure. To address the above issues, we propose a fully decentralized Federated Domain Generalization with Cluster Alignment (FDG-CA), which deals with the domain shift problem without accessing target data and eliminates the need of a central server while protecting privacy. During the training phase, the proposed FDG-CA learns domain-invariant representations by aligning clusters statistics of different source nodes through information exchange. Subsequently, during the testing phase, we propose an ensemble strategy based on a learner filter and a voting scheme to get the prediction results. Experiments demonstrate that our proposed method is superior to existing methods, achieving higher accuracy while addressing privacy concerns.

This paper considers the problem of optimal coordination of trajectory tracking performance and handling stability for autonomous equipped with distributed drive electric vehicle. Therefore, a hierarchical frame for multi-mode chassis dynamics torque vector allocation strategy is proposed, which aimed to solve the contradictory issues between vehicles’ trajectory tracking accuracy and handling stability under extreme working conditions. Firstly, in a hierarchical architecture, the upper-level trajectory tracking controller is designed by using model predictive control theory, which is used to solve the front wheel angle and the additional yaw moment of the vehicle. Secondly, the lower-level multimode torque distribution controller severs the sum of tire force utilization in every wheel as the objective function, and designs three distribution modes of chassis dynamic torque vectors based on the response of the longitudinal force and yaw moment obtained from the upper-level controller. Thirdly, the switching mechanism between the three chassis torque vector distribution modes is set according to the road adhesion condition and the requirements of the upper-level controller. Then, an analysis is conducted on the computational time complexity and robustness of the algorithm, confirming the potential for real-world application of the algorithm. Finally, Simulink/CarSim co-simulation test and hardware-in-the-loop test platform are carried out. And a vehicle trajectory tracking controller with single-mode torque vectors distribution by MPC is used as the baseline algorithm. The test results show that the proposed method show better trajectory tracking performance and handling stability than the baseline algorithm under the conditions of low adhesion surfaces and split-friction surfaces. Therefore, this study provides a solution for the safe driving of autonomous vehicles under extreme working conditions.