Predictive Control Mechanism Research Articles

When Metal Nanoclusters Meet Smart Synthesis.

Atomically precise metal nanoclusters (MNCs) represent a fascinating class of ultrasmall nanoparticles with molecule-like properties, bridging conventional metal-ligand complexes and nanocrystals. Despite their potential for various applications, synthesis challenges such as a precise understanding of varied synthetic parameters and property-driven synthesis persist, hindering their full exploitation and wider application. Incorporating smart synthesis methodologies, including a closed-loop framework of automation, data interpretation, and feedback from AI, offers promising solutions to address these challenges. In this perspective, we summarize the closed-loop smart synthesis that has been demonstrated in various nanomaterials and explore the research frontiers of smart synthesis for MNCs. Moreover, the perspectives on the inherent challenges and opportunities of smart synthesis for MNCs are discussed, aiming to provide insights and directions for future advancements in this emerging field of AI for Science, while the integration of deep learning algorithms stands to substantially enrich research in smart synthesis by offering enhanced predictive capabilities, optimization strategies, and control mechanisms, thereby extending the potential of MNC synthesis.

OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud

Autoscaling is a critical mechanism in cloud computing, enabling the autonomous adjustment of computing resources in response to dynamic workloads. This is particularly valuable for co-located, long-running applications with diverse workload patterns. The primary objective of autoscaling is to regulate resource utilization at a desired level, effectively balancing the need for resource optimization with the fulfillment of Service Level Objectives (SLOs). Many existing proactive autoscaling frameworks may encounter prediction deviations arising from the frequent fluctuations of cloud workloads. Reactive frameworks, on the other hand, rely on realtime system feedback, but their hysteretic nature could lead to violations of stringent SLOs. Hybrid frameworks, while prevalent, often feature independently functioning proactive and reactive modules, potentially leading to incompatibility and undermining the overall decision-making efficacy. In addressing these challenges, we propose OptScaler, a collaborative autoscaling framework that integrates proactive and reactive modules through an optimization module. The proactive module delivers reliable future workload predictions to the optimization module, while the reactive module offers a self-tuning estimator for real-time updates. By embedding a Model Predictive Control (MPC) mechanism and chance constraints into the optimization module, we further enhance its robustness. Numerical results have demonstrated the superiority of our workload prediction model and the collaborative framework, leading to over a 36% reduction in SLO violations compared to prevalent reactive, proactive, or hybrid autoscalers. Notably, OptScaler has been successfully deployed at Alipay, providing autoscaling support for the world-leading payment platform.

Design principles of voltage controlled DC-DC boost converters through the integration of PID and model predictive control

This paper presents the design of the transfer function between the control and output variable of a DC-DC boost converter using a combination of Proportional Integral Derivative (PID) and output-based Model Predictive Control (MPC) mechanisms for eradicating the effect of the non-minimum phase (NMP) behavior on the system. This is made possible due to the existence of a right half plane (RHP) zero. It is observed that the MPC outperformed the conventionally designed PID approach by a significant margin. Based on the computed results, it can be emphasized that the combination of output-based MPC and PID controller demonstrated a strong alignment with optimal designs, exhibiting a fast convergence rate than PID and PID-IMC. Simulation results are presented to showcase the fast transient response and a high level of robustness of the proposed control methodology.

Enhancing performance of interleaved KY converter control in single phase grid‐tied PV systems: A hybrid approach

AbstractThis paper proposes a hybrid COA‐QNN approach for an interleaved KY converter with closed‐loop control for a single‐phase grid‐connected photovoltaic (PV) system. The proposed strategy combines both Cheetah Optimizer algorithm (COA) and Quantum Neural Network (QNN), and it is commonly named the COA‐QNN technique. The interleaved KY converter is connected in the converter side. The primary goal of the COA‐QNN technique is to enhance PQ while maximizing PV electricity being transferred to the grid. The proposed COA is utilized to identify the optimal closed‐loop controller enhancements for on‐grid solar photovoltaic systems. The QNN is used to predict the optimal control parameter. The PV‐interleaved KY converter is managed by a predictive control mechanism to carry out both tasks of PQ enhancement. By then the COA‐QNN technique is implemented on the MATLAB platform and compared with existing methods. Finally, the proposed method shows better results in all methods, such as GWO, FF optimization and combined GWO‐FF.

Dual‐mode event‐triggered predictive control for nonlinear systems with bounded disturbances

AbstractIn this article, we propose a dual‐mode event‐triggered predictive control method for nonlinear systems with bounded disturbances. The proposed method contains two triggering mechanisms, namely, the hybrid threshold‐based event‐triggered model predictive control (HETMPC) mechanism and the event‐triggered linear quadratic regulator mechanism. The former triggering mechanism is designed based on the error between the real state and the optimal state and also the disturbance information acted on the investigated system. Compared with the traditional fixed triggering threshold, the designed HETMPC has a fewer triggering numbers and reduces the computational burden of online real‐time optimization. This event‐triggered mechanism will be adopted before the states go into the terminal invariant set. The latter event‐triggered mechanism is designed based on the derivation of the system state and it will be adopted after the states enter the terminal invariant set. The feasibility and the input‐to‐state practical stability analysis of the designed strategy is presented. Some simulations, including the application to a mass‐spring‐damper system, are provided to show the correctness and feasibility of the designed algorithms.

Dynamic event-triggered predictive control for DoS attacks-based discrete-time networked linear systems

This paper studies a novel method of the dynamic event-triggered mechanisms and the predictive control scheme for discrete-time networked linear systems subject to network delays and DoS attacks in both sensor-to-controller ([Formula: see text]) and controller-to-actuator ([Formula: see text]) channels. First, by assuming maximum allowable duration of DoS attacks, predictive control scheme is used to proactively offset the adverse impacts of communication constraints. Second, to further save communication resources, dynamic event-triggered mechanisms are configured on sensor and controller sides, respectively. Next, sufficient condition is derived for discrete-time networked linear systems under predictive control and dynamic event-triggered mechanisms, which guarantees the resulting closed-loop system’s asymptotical stability. Finally, a simulation example is used to confirm the availability and applicability of the proposed method.

Different Control Strategies Drive Interlimb Differences in Performance and Adaptation during Reaching Movements in Novel Dynamics.

Humans exhibit lateralization such that most individuals typically show a preference for using one arm over the other for a range of movement tasks. The computational aspects of movement control leading to these differences in skill are not yet understood. It has been hypothesized that the dominant and nondominant arms differ in terms of the use of predictive or impedance control mechanisms. However, previous studies present confounding factors that prevented clear conclusions: either the performances were compared across two different groups, or in a design in which asymmetrical transfer between limbs could take place. To address these concerns, we studied a reach adaptation task during which healthy volunteers performed movements with their right and left arms in random order. We performed two experiments. Experiment 1 (18 participants) focused on adaptation to the presence of a perturbing force field (FF) and experiment 2 (12 participants) focused on rapid adaptations in feedback responses. The randomization of the left and right arm led to simultaneous adaptation, allowing us to study lateralization in single individuals with symmetrical and minimal transfer between limbs. This design revealed that participants could adapt control of both arms, with both arms showing similar performance levels. The nondominant arm initially presented a slightly worst performance but reached similar levels of performance in late trials. We also observed that the nondominant arm showed a different control strategy compatible with robust control when adapting to the force field perturbation. EMG data showed that these differences in control were not caused by differences in co-contraction across the arms. Thus, instead of assuming differences in predictive or reactive control schemes, our data show that in the context of optimal control, both arms can adapt, and that the nondominant arm uses a more robust, model-free strategy likely to compensate for less accurate internal representations of movement dynamics.

Trends of Human-Robot Collaboration in Industry Contexts: Handover, Learning, and Metrics.

Repetitive industrial tasks can be easily performed by traditional robotic systems. However, many other works require cognitive knowledge that only humans can provide. Human-Robot Collaboration (HRC) emerges as an ideal concept of co-working between a human operator and a robot, representing one of the most significant subjects for human-life improvement.The ultimate goal is to achieve physical interaction, where handing over an object plays a crucial role for an effective task accomplishment. Considerable research work had been developed in this particular field in recent years, where several solutions were already proposed. Nonetheless, some particular issues regarding Human-Robot Collaboration still hold an open path to truly important research improvements. This paper provides a literature overview, defining the HRC concept, enumerating the distinct human-robot communication channels, and discussing the physical interaction that this collaboration entails. Moreover, future challenges for a natural and intuitive collaboration are exposed: the machine must behave like a human especially in the pre-grasping/grasping phases and the handover procedure should be fluent and bidirectional, for an articulated function development. These are the focus of the near future investigation aiming to shed light on the complex combination of predictive and reactive control mechanisms promoting coordination and understanding. Following recent progress in artificial intelligence, learning exploration stand as the key element to allow the generation of coordinated actions and their shaping by experience.

Simultaneous stable control of temperature field distribution uniformity and consistency for multi-temperature zone systems

As a key factor characterizing the control accuracy of multi-temperature zone systems (MTZSs), the stable control of temperature field distribution uniformity and consistency is of critical importance for MTZSs, and it largely determines the product quality and production efficiency. Due to the complicated multiple input and output properties, as well as the various external variations in practice, however, it is extremely difficult to monitor the temperature field distribution in production process. To address the uniform and consistent temperature field distribution problem in MTZSs, a multi-variable dynamic matrix control (DMC)-based predictive control mechanism is proposed in this paper. Specifically, we first establish a finite element-based heat transfer model to analyse heat transfer within the multi-temperature zone, and then propose a multi-variable DMC-based decoupling design method to decompose the entire system into multiple subsystems with single-input single-output for temperature uniformity distribution control in MTZS. By utilizing the ANSYS tools to analyse the transient field temperatures, we obtain both time and space distribution characteristics of the transient temperature field with the proposed control method, and also compare such results with those obtained using the PID control method. Finally, we apply the proposed multi-variable DMC control mechanism onto a multi-temperature sintering furnace of a practical industrial product line for verification. Results show that, with the proposed control mechanism adopted, the difference between the highest and lowest temperature of any workpiece could be maintained within 5°C in the heat rising up period, which convincingly verifies the effectiveness of the proposed predictive control algorithm in different cases.

Runge-Kutta Model Predictive Speed Control for Permanent Magnet Synchronous Motors

Permanent magnet synchronous motors (PMSMs) have commonly been used in a wide spectrum ranging from industry to home appliances because of their advantages over their conventional counterparts. However, PMSMs are multiple-input multiple-output (MIMO) systems with nonlinear dynamics, which makes their control relatively difficult. In this study, a novel model predictive control mechanism, which is referred to as the Runge-Kutta model predictive control (RKMPC), has been applied for speed control of a commercial permanent magnet synchronous motor. Furthermore, the RKMPC method has been utilized for the adaptation of the speed of the motor under load variations via RKMPC-based online parameter estimation. The superiority of RKMPC is that it can take the constraints on the inputs and outputs of the system into consideration, thereby handling the speed and current control in a single loop. It has been shown in the study that the RKMPC mechanism can also estimate the load changes and unknown load disturbances to eliminate their undesired effects for a desirable control accuracy. The performance of the employed mechanism has been tested on a 0.4 kW PMSM motor experimentally for different conditions and compared to the conventional Proportional Integral (PI) method. The tests have shown the efficiency of RKMPC for PMSMs.

Prefrontal engagement during sequential manual actions in children at early adolescence compared with adults

In everyday behavior, we perform numerous goal-directed manual tasks that contain a sequence of actions. However, knowledge is limited regarding developmental aspects of predictive control mechanisms in such tasks, particularly with regard to brain activations supporting sequential manual actions in children. We investigated these issues in typically developing children at early adolescence (11–14 years) compared with previously collected data from adults. While lying in a magnetic resonance imaging (MRI) scanner, the participants steered a cursor on a computer screen towards sequentially presented targets using a hand-held manipulandum. The next target was either revealed after completion of the ongoing target (one-target condition), in which case forthcoming movements could not be planned ahead, or displayed in advance (two-target condition), which allowed the use of a predictive control strategy. The adults completed more targets in the two- than one-target condition, displaying an efficient predictive control strategy. The children, in contrast, completed fewer targets in the two- than one-target condition, and difficulties implementing a predictive strategy were found due to a limited capacity to inhibit premature movements. Brain areas with increased activation in children, compared with the adults, included prefrontal and posterior parietal regions, suggesting an increased demand for higher-level cognitive processing in the children due to inhibitory challenges. Thus, regarding predictive mechanisms during sequential manual tasks, crucial development likely occurs beyond early adolescence. This is at a later age than what has previously been reported from other manual tasks, suggesting that predictive phase transitions are difficult to master.

Mode Switching-Based Symmetric Predictive Control Mechanism for Networked Teleoperation Space Robot System

Bilateral synchronization control problem for networked teleoperation space robot system (NTSRS) with unknown gravity and asymmetric, random delay as well as communication intermittent interruption is discussed in this paper. To make full use of limited operation window time, improve telepresence, and realize continuous teleoperation, a special mode switching-based symmetric predictive control mechanism is designed, where the mode switching idea is to make full use of limited operation time. Meanwhile, among this mechanism, the symmetric predictive control structure is to improve telepresence and realize continuous teleoperation, besides the same function that mode switching idea provides. The structure of this mechanism for the master side is composed of a predictor and corresponding controller, and symmetric configurations for slave side. Combining with the homomorphic model idea and utilizing the predictive and estimation capability of neural network (NN), novel predictor design method is proposed. In designing the predictor, unlike the traditional model predictive control where predictor is coupled with the controller, this method is independent with the corresponding controller in structure, which is helpful for parameter adjustment and analysis. For unknown gravity, estimation ability of NN is utilized to solve it. With the adaptive method, estimation errors can be kept at a lower level. Subsequently, an analogous proportional-derivative controller with gravity compensation term is designed to realize asymptotical stability and synchronization control of the NTSRS. Finally, effectiveness of the proposed method is verified by some simulation examples.

Efficient Energy-Management System Using A Hybrid Transactive-Model Predictive Control Mechanism for Prosumer-Centric Networked Microgrids

With the development of distributed energy resources (DERs) and advancements in technology, microgrids (MGs) appear primed to become an even more integral part of the future distribution grid. In order to transition to the smart grid of the future, MGs must be properly managed and controlled. This paper proposes a microgrid energy management system (MGEMS) based on a hybrid control algorithm that combines Transactive Control (TC) and Model Predictive Control (MPC) for an efficient management of DERs in prosumer-centric networked MGs. A locally installed home energy management system (HEMS) determines a charge schedule for the battery electric vehicle (BEV) and a charge–discharge schedule for the solar photovoltaic (PV) and battery energy storage system (BESS) to reduce residential customers’ operation cost and to improve their overall savings. The proposed networked MGEMS strategy was implemented in IEEE 33-bus test system and evaluated under different BEV and PV-BESS penetration scenarios to study the potential impact that large amounts of BEV and PV-BESS systems can have on the distribution system and how different pricing mechanisms can mitigate these impacts. Test results indicate that our proposed MGEMS strategy shows potential to reduce peak load and power losses as well as to enhance customers’ savings.

Humans adjust their grip force when passing an object according to the observed speed of the partner\u2019s reaching out movement

The way an object is released by the passer to a partner is fundamental for the success of the handover and for the experienced fluency and quality of the interaction. Nonetheless, although its apparent simplicity, object handover involves a complex combination of predictive and reactive control mechanisms that were not fully investigated so far. Here, we show that passers use visual-feedback based anticipatory control to trigger the beginning of the release, to launch the appropriate motor program, and adapt such predictions to different speeds of the receiver’s reaching out movements. In particular, the passer starts releasing the object in synchrony with the collision with the receiver, regardless of the receiver’s speed, but the passer’s speed of grip force release is correlated with receiver speed. When visual feedback is removed, the beginning of the passer’s release is delayed proportionally with the receiver’s reaching out speed; however, the correlation between the passer’s peak rate of change of grip force is maintained. In a second study with 11 participants receiving an object from a robotic hand programmed to release following stereotypical biomimetic profiles, we found that handovers are experienced as more fluent when they exhibit more reactive release behaviours, shorter release durations, and shorter handover durations. The outcomes from the two studies contribute understanding of the roles of sensory input in the strategy that empower humans to perform smooth and safe handovers, and they suggest methods for programming controllers that would enable artificial hands to hand over objects with humans in an easy, natural and efficient way.

Application of predictive control to the Hegselmann‐Krause model

The aim of this paper is to analyze the behavior of Hegselmann‐Krause–type models. General sufficient conditions for achieving consensus are provided. Model predictive control is applied to the Hegselmann‐Krause system in the context of time scales. Numerical simulations show that the predictive control mechanism has the ability to steer the system to attain a consensus. Moreover, faster consensus speed is observed when compared with the classical model. Finally, in the case of the discrete time scale and all agents being neighbors at the initial time, conditions guaranteeing an average consensus are given.

Adaptive-model predictive control of electronic expansion valves with adjustable setpoint for evaporator superheat minimization

In many refrigeration and air-conditioning systems, the automatic controller in electronic expansion valves have been employed as a component responsible for controlling the valve opening so that the superheat at the outlet of the evaporator remains within the desired limits. In some of these controllers, the control parameters are tuned once for a certain operating point and remain unaltered, even when the operating conditions change, unless the operator changes it manually. For a strongly nonlinear plant with a dramatically time varying characteristics, linear time invariant (LTI) prediction accuracy might degrade significantly that the performance of traditional Model Predictive Controller (MPC) becomes unacceptable. This work presents an Adaptive-Model Predictive Control (AMPC) mechanism to address this degradation where the parameters are tuned continuously through recursive estimation and update approaches, making the MPC insensitive to prediction errors and to achieve the optimal superheat response. Moreover, an adaptive setpoint hunting algorithm is implemented so that the system achieves stability and improves energy efficiency simultaneously.

Integrated Rudder/Fin Control With Disturbance Compensation Distributed Model Predictive Control

For the ship sailing in the seaway, usually the rudder is used to control the heading and the fin stabilizer is used to reduce rolling. However, there are interactions between their control effects, which usually deteriorate the control performance. The interactions can be used to enhance the control performance, if rudder and fin stabilizer are controlled to work in coordination, and this is usually called the integrated rudder/fin control. In this paper, a new control method based on distributed model predictive control (DMPC) and disturbance compensation mechanism is proposed to achieve the integrated rudder/fin control. MPC controllers are designed for rudder and fin stabilizer respectively to fulfill their different control requirements. Communication between the MPC controllers is established to make the rudder and fin stabilizer work in coordination. An extended state observer is adopted as the disturbance observer to estimate the lumped disturbance of roll motion which consists of model mismatch and external disturbance. The MPC controllers calculate control actions with the lumped disturbance to improve the control performance. They also avoid actuator saturations by limiting the control actions, which makes the control more robust in sever sea conditions. Simulation study shows that, with the proposed control method, the roll stabilization performance is enhanced and the heading control capability is preserved. The proposed control method also shows a more desirable control performance in roll stabilization compared with a well-tuned LQR controller.

Predictive feedforward compensator for dead-time processes

This paper deals with a predictive disturbance compensator for processes characterized by input-output and disturbance-output time delays. The proposed scheme provides a solution for configurations where the application of classical feedforward approach results in poor performance. The analyzed technique merges the advantages of classical feedforward control scheme, which can be combined with any feedback controller, and the use of future disturbance estimation in the predictive control mechanism. The predictive compensator is based on the GPC (Generalized Predictive Control) algorithm exploiting its prediction capabilities. The evaluation of the proposed disturbance rejection scheme is performed through simulation and compared to common techniques in order to highlight the advantages and drawbacks. The obtained results show that the proposed approach can significantly improve the disturbance attenuation in process control tasks.

Modelling and simulation of predictive handover control mechanisms for fast moving mobile nodes in wireless networks

When a mobile node (MN) is moving at a high speed from one network to another network, internet connectivity is degraded as MN cannot complete the handover within the required time owing to delayed triggering of handover request. To ensure seamless connectivity without performance degradation, traditional handover mechanism needs to be enhanced. In this paper, a media independent handover (MIH)-based handover triggering scheme is proposed based on the mobility prediction of MN. Handover triggering is predicted by calculating link expiration time that depends on velocity, direction and distance of MN from the access point. Scanning process required for traditional MIH networks is eliminated using information server (IS) that estimates the received signal strength (RSS) of candidate networks based on the location of MN. Simulation results show that the proposed scheme reduces the handover latency and improves the throughput in homogenous networks at varying velocities of MN.

Predictive control and stabilization of nonholonomic formations with integrated spline-path planning

A path planning in the space of multinominals integrated into a model predictive control mechanism for driving formations of autonomous mobile robots is presented in this paper. The proposed approach is designed to stabilize the formations in desired shapes, and to navigate the group into a final position in a partly known environment with dynamic obstacles. In addition, the system provides inter-vehicle coordination and collision avoidance in the event of failure of a team member. The method is aimed at reaching the final position of the formation in the desired shape, but it enables to change temporarily this shape if it is enforced by the environment (in narrow corridors, on response to an impending collision with obstacles and faulty team members, etc.). This autonomous emergent behaviour increases the robustness of the system and its usability. It enables a proper compromise to be found between the formation driving requirement and the effort to fulfil the mission objective, i.e., to move the group from the current state into the required position. In this paper, the convergence of the method and the requirements for stability are shown on the basis of the results of the Lyapunov theorems of stability. These theoretical achievements imply constraints on the applicability of the system, which are verified in numerical simulations and in various tests with real autonomous robots. The performances of the entire system and of independent sub-systems in various formation driving scenarios are also shown in these tests.