Closed-loop Performance Research Articles

Meta-learning for model-reference data-driven control

One-shot direct model-reference control design techniques, like the Virtual Reference Feedback Tuning (VRFT) approach, offer time-saving solutions for calibrating fixed-structure controllers. Nonetheless, such methods are known to be highly sensitive to the quality of data, often requiring long and costly experiments to attain acceptable closed-loop performance. These features might prevent the widespread adoption of such techniques, especially in low-data regimes. In this paper, we argue that the inherent similarity of many industrially relevant systems may come at hand, offering additional information from plants that are similar (yet not equal) to the system one aims to control. Assuming that this supplementary information is available, we propose a novel, direct design approach that leverages data from similar plants, the knowledge of controllers calibrated on them, and the corresponding closed-loop performance to enhance model-reference control design. By constructing the new controller as a combination of the available ones, our approach exploits all the available priors following a meta-learning philosophy, ensuring non-decreasing performance. An extensive numerical analysis supports our claims, highlighting the effectiveness of the proposed method in achieving performance comparable to iterative approaches, while retaining the efficiency of one-shot direct data-driven methods like VRFT.

Modified ADRC based on optimized repetitive control for low-frequency vibration and harmonic disturbance suppression: Design and application to inertially stabilized platforms

Mechanical vibrations in low-frequency resonance and harmonic modes severely affect the closed-loop performance of inertially stabilized platforms (ISPs). This article proposes a new vibration control scheme capable of suppressing low-frequency vibration with large amplitude and harmonic disturbances in application to an ISP. Concretely, we first design an optimized repetitive control (ORC) framework with a plug-in optimization function, where the ORC retains the suppression effect of vibration in tip-tilt modes and mitigates the waterbed effect. To enhance vibration suppression, by introducing the ORC-based inner calibration network (ICN), a modified ADRC framework with the reduced-order extended state observer (ESO) design is established. Subsequently, substantial comparison experiments have been verified on an ISP to evaluate the superiorities of the proposed method, and the experiment results verify the effectiveness of the hybrid vibration control method, attenuating vibration in resonance mode to −51.94 dB.

Demand-Driven Resilient Control for Generation Unit of Local Power Plant Under Unreliable Communication

The resilient control issue for the generation unit (GU) in a local power plant with unreliable communication is addressed in this article, where the communication may be jammed by denial-of-service (DoS) attacks. Based on the GU model of voltage and current at the point of common coupling, a demand-driven network communication protocol is proposed to decrease the number of scheduling signal transmissions, and an observer-based prediction method is provided to replenish the lack of dispatching data during transmission intervals when the demand has not changed. The closed-loop performance is analyzed for the GU system in the input-to-state stable framework with or without attack. According to the DoS attack model, which is described by the assumptions of frequency and duration, the conservativeness of the tolerable DoS attack index is reduced by using the thought of robustness to the maximum disturbance-induced error. Simulation examples are provided to verify the effectiveness of the approach proposed in this article.

Performance Enhancement of MRAC via Generalized Dynamic Inversion

Model Reference Adaptive Control (MRAC) guarantees closed loop stability and desired steady state performance of dynamical systems without undue dependence upon their mathematical models. However, the applicability of MRAC may not be suitable for systems that are crucial to safety due to its poor transient response. A modified MRAC design is presented in this paper for the purpose of enhancing the transient closed loop performance of MRAC by utilizing generalized dynamic inversion (GDI) and nullspace control. Two adaptive control actions take place under the proposed control design. The first control action is responsible for enforcing the reference model dynamics, and the second control action works to enhance the transient performance of MRAC. The two control actions do not interfere with each other because they act on two orthogonally complement control subspaces. The GDI-based MRAC law forces the uncertain dynamical system to follow the reference model, and it also restricts the undesirable oscillations intensity of the closed loop transient system response. Simulations are conducted on a flying wing aircraft model to demonstrate the efficacy of the proposed design.

Strong and tough bio-based biomimetic-multiphase composite polyesters with superior barrier and chemically closed-loop performances

A full-natural bionics-multiphase composite polyester with mechanical robustness, high gas barrier and chemically closed-loop properties was designed and prepared using bio-based monomers and single-layered mica to achieve a replacement for plastic.

AI-Driven Process Design for Closed-Loop Manufacturing

This study investigates the potential of artificial intelligence (AI) in enhancing closed-loop manufacturing (CLM) systems, which emphasize sustainability through recycling, reuse, and optimized resource utilization. CLM faces significant challenges in minimizing waste, improving operational efficiency, and optimizing resource usage. Although AI holds promise in addressing these challenges, its application in CLM systems, especially in dynamic decision-making and resource optimization, requires further exploration. This research aims to explore how AI technologies can improve material flows, reduce waste, and enhance decision-making to achieve both sustainability and operational efficiency in manufacturing systems. To understand the impact of AI on CLM, the study conducted a comprehensive review of case studies and literature across various industries. It focused on AI techniques, such as machine learning (ML) and reinforcement learning (RL), and their roles in optimizing resource usage, refining recycling processes, and improving production schedules. Statistical methods, including regression and sensitivity analysis, were employed to evaluate the effectiveness of AI-driven models in enhancing closed-loop manufacturing performance. The review revealed promising results, highlighting AI's ability to optimize resource consumption and waste management strategies across diverse manufacturing environments. The study’s findings demonstrated significant improvements in resource efficiency, waste reduction, and cost savings across multiple industries. AI-driven models optimized material consumption, resulting in reductions of up to 12% in raw material use. Additionally, AI-enhanced recycling and waste management strategies led to a 25% increase in material recovery, while operational costs were reduced by up to 15%. AI models also displayed a strong ability to adapt to fluctuations in demand and production conditions, ensuring sustained operational efficiency even amid dynamic market changes. These outcomes highlight AI’s transformative potential in achieving sustainable manufacturing practices and advancing circular economy goals.

On Adaptive Fractional Dynamic Sliding Mode Control of Suspension System

This paper introduces a novel adaptive control method for suspension vehicle systems in response to road disturbances. The considered model is based on an active symmetry quarter car (SQC) fractional order suspension system (FOSS). The word symmetry in SQC refers to the symmetry of the suspension system in the front tires or the rear tires of the car. The active suspension controller is generally driven by an external force like a hydraulic or pneumatic actuator. The external force of the actuator is determined using fractional dynamic sliding mode control (FDSMC) to counteract road disturbances and eliminate the chattering caused by sliding mode control (SMC). In FDSMC, a fractional integral acts as a low-pass filter before the system actuator to remove high-frequency chattering, necessitating an additional state for FDSMC implementation assuming all FOSS state variables are available but the parameters are unknown and uncertain. Hence, an adaptive procedure is proposed to estimate these parameters. To enhance closed-loop system performance, an adaptive proportional-integral (PI) procedure is also employed, resulting in the FDSMC-PI approach. A comparison is made between two SQC suspension system models, the fractional order suspension system (FOSS) and the integer order suspension system (IOSS). The IOSS controller is based on dynamic sliding mode control (DSMC) and a PI procedure (DSMC-PI). The results show that FDSMC outperforms DSMC.

Generalized Malmquist orthogonal functions based model predictive control

The computational burden could be an issue of traditional model predictive control (MPC) in real-time applications. There are different procedures to cope with this problem such as using explicit MPC or approximation of the control inputs using orthogonal-basis functions. This paper presents the design procedure and experimental validation of a generalized discrete-time Malmquist orthogonal functions-based model predictive control (MbMPC) whose usage can decrease calculation concerns. To the authors’ knowledge, this is the first time the Malmquist functions are introduced into the MPC design. The discrete-time orthogonal Malmquist functions properties are used within this approach for approximating the future control input increments with a smaller number of parameters than in traditional MPC approaches. It is shown that the satisfactory controller and closed-loop system performances can be achieved using smaller number of tuning parameters. The performance of the proposed MbMPC is shown and compared with discrete-time Laguerre orthogonal functions based MPC (LbMPC) applied to a DC motor servo system. The simulation and experimental results demonstrate better IAE, ITAE, ISE and ITSE controller performance indices as well as faster average calculation time in comparison with the LbMPC approach. Additionally, the advantage of using the proposed method is emphasized by showing how the length of the prediction horizon affects the computational burden in comparison to the traditional MPC approaches.

Non-singular fast terminal sliding mode trajectory tracking control of cantilever piezo-electric stack actuator based on asymmetric hysteresis compensation

Abstract Piezoelectric actuators are widely employed in micro-precision applications due to their fast response and high resolution. This paper investigates the trajectory tracking control of a cantilever piezoelectric stack actuator (CPSA) under external perturbations and hysteresis. A control scheme is proposed that effectively compensates for hysteresis by employing an asymmetric Bouc–Wen model, integrated with a non-singular fast terminal sliding mode control (NFTSMC) featuring a variable convergence law. This methodology guarantees finite-time convergence and robustness, thereby enhancing the overall control performance of the CPSA. Experimental results reveal that the proposed control algorithm significantly improves control accuracy and speed, achieving stable closed-loop system performance and maintaining bounded closed-loop signals within a finite time frame. The effectiveness and superiority of the NFTSMC method are validated through comprehensive experimental studies.

Model Reference Control for Reducing Pilot-Induced Oscillation Tendencies

Lags between pilot inputs and aircraft responses may lead to a pilot–vehicle system entering into a pilot-induced oscillation. A model reference control element can compensate for these lags, avoiding pilot-induced oscillations and improving tracking performance. Neural network compensation drives the combined controller–plant system to exhibit a closed-loop response similar to that of an idealized system, immune to the factors that trigger pilot-induced oscillations. Actuator rate limiting has been a historically ubiquitous cause of pilot-induced oscillations; meanwhile, aeroelastic effects pose a threat to future lightweight, flexible aircraft designs. Model reference control can reduce pilot-induced oscillation tendencies caused by either of these factors. This paper presents the methods used to train the model reference controller, including conditions for system stability under model reference control. It then showcases results from a pair of simulation experiments applying the control design to compensate for rate limiting and aeroelasticity, respectively. Results demonstrate that the model reference control scheme reduces pilot-induced oscillation tendencies and improves closed-loop tracking performance.

An efficient tuning method for networked control systems

Sensors, controllers, and actuators in a networked control system collaborate to execute a distributed closed-loop feedback control system. Currently, NCS includes network components that remain unidentified, such as heightened latency and packet loss, which may be persistent or fluctuate over time. The current predicament stems from the increased complexity of inspection and control systems, which is attributed to the vast expansion of communication networks. Implementing proactive strategies to mitigate the effects of communication network disruptions on control systems is crucial. A comprehensive study has identified various techniques for controller development. Initiatives are underway to mitigate the impact of network-wide random delays and improve the efficiency of the NCS system. To reach this goal, this study uses a meta-heuristic optimization method called the Whale Optimization Algorithm (WOA) along with a fuzzy-PID controller for a Networked Control System (NCS) plant. The proposed work will be evaluated under two operational scenarios of the NCS plant: one incorporating a random delay condition and the other excluding it. The effectiveness of the proposed method is evaluated by measuring and comparing its closed-loop performance to a traditional PID controller. The outcomes for both NCS plant scenarios illustrate the efficacy of the proposed initiative regarding closed-loop performance.

Robust controller design strategies with improved performance for MSBR core

Designing controllers for power regulation of nuclear reactors is a challenging task because of its non-linear behavior, and fluctuation of system parameters over time in response to changes in power levels. Motivated by the aforementioned fact, proportional-integral (PI) controllers are designed in this manuscript to regulate the core power of the molten salt breeder reactor (MSBR) by employing the all stabilizing region and quantitative feedback theory (QFT) technique. In the first approach, the entire stability region is plotted in the (Kp,Ki)-plane. In order to ensure safe operation, the reactivity of control rods must lie in the specified range. Hence, the control effort constrained stability region (CECSR) is obtained within the all stability region to design the PI controller. A novel PI controller and a pre-filter based on gain-phase shaping on Nichols chart are also proposed. Robustness, disturbance rejection and relative stability of the system are addressed by obtaining the performance bounds on Nichols chart. Simulation results illustrate that the closed-loop system is exhibiting a noteworthy improvement in the closed loop performance over the latest reported control strategies.

Observer-Based Fixed-Time Consensus Tracking for a Class of Nonlinear Multi-Agent Systems

In this paper, the problem of fixed-time consensus tracking for a class of nonlinear multi-agent systems (MASs) with uncertainties and external disturbances is addressed. To solve this problem, a new fixed-time consensus tracking protocol with disturbance rejection abilities is proposed that consists of a fixed-time distributed observer (FTDO), a fixed-time disturbance observer (FDO), and a nonsmooth backstepping controller with lumped disturbance compensation (NBCDC). The fixed-time stabilities are analyzed correspondingly. Firstly, an FTDO is designed to estimate a leader’s output by using the topology of communication networks for each follower. Second, an FDO is developed to observe the lumped disturbances composed of model uncertainties and external disturbances. Finally, an NBCDC is proposed for improving the closed-loop performance. The simulation results show that the proposed method is effective.

Analysis of proportional-resonant damping factors in the parallel operation of UPSs

Accurate load-sharing and circulating current mitigation is a particular problem in parallel-connected inverters without intercommunication. This paper analyzes how using multiple-resonant controllers in the voltage regulation loop impacts the parallel operation of uninterruptible power supplies (UPSs) with droop control. It is shown how adding damping factors of all modes of the resonant controller reduces the circulating current between the UPSs, also improving the power-sharing closed-loop stability and performance. Moreover, the proper choice of these damping coefficients can replicate the effects of adding virtual resistance loops to the droop controller structure. An analysis of such coefficients on the stability margins of the voltage regulation system is carried out and the methodology is validated by experimental results considering the parallelism of two 3.5 kVA UPSs with parametric differences in their LC filters.

Valid RBFNN Adaptive Control for Nonlinear Systems With Unmatched Uncertainties.

In this article, an adaptive tracking controller based on radial basis function neural networks (RBFNNs) is proposed for nonlinear plants with unmatched uncertainties and smooth reference signals. The concept of valid RBFNN adaptive control is introduced where all closed-loop arguments of the involved RBFNNs should always remain inside their corresponding compact sets. Considering the local approximation capacity of RBFNNs, validity requirements are necessary for ensuring reliable closed-loop approximation accuracy and stability. To obtain valid RBFNN adaptive controllers, a novel iterative design method is proposed and embedded into the traditional backstepping approach. In the initial iteration, an ideal RBFNN and its online estimated version are introduced in each step where the initial compact set guarantees the validity requirement for only a finite time interval. Then, by carefully investigating the dependence among different signals and introducing some auxiliary variables, the compact sets are redesigned for prolonging the time interval satisfying validity requirements to infinity as the iteration goes on. Consequently, a closed-loop system model can be formulated during the entire control process, which underlies a rigorous proof on closed-loop stability and some guidelines on practical implementation. Meanwhile, rigorous analysis from validity requirements reveals, for the first time, a new feature of RBFNN adaptive controllers in the presence of unmatched uncertainties: excessively large scales of RBFNNs in intermediate steps may impair the closed-loop performance. Finally, simulation results are provided to illustrate the efficiency and feasibility of the obtained results.

Multi-agent distributed control of integrated process networks using an adaptive community detection approach

This paper focuses on developing an adaptive system decomposition approach for multi-agent distributed model predictive control (DMPC) of integrated process networks. The proposed system decomposition employs a refined spectral community detection method to construct an optimal distributed control framework based on the weighted graph representation of the state space process model. The resulting distributed architecture assigns controlled outputs and manipulated inputs to controller agents and delineates their interactions. The decomposition evolves as the process network undergoes various operating conditions, enabling adjustments in the distributed architecture and DMPC design. This adaptive architecture enhances the closed-loop performance and robustness of DMPC systems. The effectiveness of the multi-agent distributed control approach is investigated for a benchmark benzene alkylation process under two distinct operating conditions characterized by medium and low recycle ratios. Simulation results demonstrate that adaptive decompositions derived through spectral community detection, utilizing weighted graph representations, outperform the commonly employed unweighted hierarchical community detection-based system decompositions in terms of closed-loop performance and computational efficiency.

Adaptive control of magnetic levitation system based on fuzzy inversion

A novel adaptive tracking controller for magnetic levitation systems (MLS) is developed. The controller is based on a special adaptive control scheme incorporating fuzzy model of electromagnetic acceleration enabling fast and accurate fuzzy inversion. The controller ensures accurate tracking of any smooth desired position signal, despite unknown MLS parameters. The closed-loop system stability, in the sense of uniform ultimate boundedness (UUB) of error trajectories, is proved using Lyapunov approach. The closed-loop system performance is investigated during numerical experiments. Finally the proposed controller is verified by successful implementation on a DSP board controlling a typical magnetic levitation ball system. Performed tests and experiments demonstrate that the proposed control technique is robust against discretization and unknown MLS model parameters, provides high tracking accuracy, is easily implementable, and simple to tune.

Measuring instability in chronic human intracortical neural recordings towards stable, long-term brain-computer interfaces.

Intracortical brain-computer interfaces (iBCIs) enable people with tetraplegia to gain intuitive cursor control from movement intentions. To translate to practical use, iBCIs should provide reliable performance for extended periods of time. However, performance begins to degrade as the relationship between kinematic intention and recorded neural activity shifts compared to when the decoder was initially trained. In addition to developing decoders to better handle long-term instability, identifying when to recalibrate will also optimize performance. We propose a method, "MINDFUL", to measure instabilities in neural data for useful long-term iBCI,without needing labels of user intentions. Longitudinal data were analyzed from two BrainGate2 participants with tetraplegia as they used fixed decoders to control a computer cursor spanning 142 days and 28 days, respectively. We demonstrate a measure of instability that correlates with changes in closed-loop cursor performance solely based on the recorded neural activity (Pearson r = 0.93 and 0.72, respectively). This result suggests a strategy to infer online iBCI performance from neural data alone and to determine when recalibration should take place for practical long-term use.

Adaptive fault-tolerant containment control for heterogeneous uncertain nonlinear multi-agent systems under actuator faults

As modern control systems involve a growing number of actuators, the consequent number of faults occurrence, which may deteriorate the closed-loop system performance, is increasing. Focusing on the containment control problem of generic nonlinear high-order heterogeneous uncertain Multi-Agent Systems, this study proposes a novel distributed adaptive fault-tolerant Proportional–Integral–Derivative control protocol to improve the reliability and the resilience of the whole networked systems against actuator failures. The Lyapunov theory, combined with the Barbalat lemma, provides the design of suitable adaptive mechanisms that adjust the PID control actions to ensure the asymptotic convergence of the closed-loop containment errors for the overall network, hence implying the convergence of each agent state toward the convex region shaped by the multiple leaders, despite the occurrence of actuators faults. The fulfillment of the fault-tolerant containment objective is ensured by requiring a lower computational burden with respect to alternative solutions, hence resulting in more attractive for wider practical engineering applications. Numerical simulation results, also including a comparison analysis with a state-of-the-art controller, corroborate the efficiency and the advantages of the proposed distributed PID controller.

Real-sea validation of a model predictive controller's inherent robustness for medium-scale unmanned trimaran heading

The development of medium-to large-scale and high-performance unmanned surface vehicles (USVs) is a burgeoning trend in intelligent marine systems. The heading controller is crucial for USVs to execute diverse missions, especially given their inherent underactuation characteristics and constraints. Recent efforts have focused on enhancing the robustness of controllers against external disturbances and employed two main strategies: one converges the control error to a bounded residual set through robust modifications, while the other eliminates the error by modelling disturbances. Notably, these enhancements have primarily catered to small USVs, where disturbances significantly impact their manoeuvrability, necessitating such robust control strategies. This focus has somewhat overshadowed the inherent robustness of closed-loop control systems. Compared with small USVs, medium-to large-scale USVs are less affected by external disturbances, despite undertaking more complex missions. Leveraging the intrinsic robustness of controllers presents an opportunity to simplify controller design, thereby reallocating computational resources towards enhancing mission capabilities. Model Predictive Control (MPC) has attracted significant attention recently, and its receding horizon and optimality theoretically provides a new level of inherent robustness, which remains under-explored in real sea. This paper focuses on the inherent robustness of MPC in managing the heading of a medium-scale unmanned trimaran subjected to the thrust angle and angular velocity constraints. A model predictive controller considering the constraints is designed based on the identified Nomoto model and the asymptotic stability is ensured with a terminal cost. Conducted real-sea experiments and comparative analyses with a Proportional-Integral-Derivative (PID) controller, the most widespread and dominant control algorithm in practical USV engineering, underscore the superiority of MPC in maintaining satisfactory closed-loop performance. Furthermore, the MPC controller is also successfully applied to real-sea path-following missions and demonstrated good tracking performance in various sea conditions ranging from level 3 to 5 and wind speeds spanning from level 6 to 8. This validation opens up new avenues for motion control strategies in the evolving landscape of larger-scale USVs.