Control Sequence Research Articles

Robust Model Predictive Control Using a Two-Step Triggering Scheme

This article is concerned with event-triggered robust model predictive control for linear discrete-time systems with bounded disturbances. A two-step scheme involving a tentative verification of a triggering condition and a delayed triggering with a waiting horizon is proposed to reduce the average triggering rate and fully utilize the nominal optimal control sequence minimizing a quadratic cost function. The triggering condition and the waiting horizon are synthesized based on a prediction model of the plant and a robust positively invariant set associated with it. Under mild conditions, recursive feasibility and closed-loop robust stability are guaranteed. Two examples are used to show the effectiveness and merits of the proposed approach.

A study of lateral trajectory tracking control under fused slip rate and road random inputs

To improve the maneuvering stability of uncrewed armored vehicles during emergency steering and obstacle avoidance under high-speed driving conditions in off-road undulating terrain, in this paper, we establish a 7DOF dynamics model based on the time-varying trajectory curvature for the lateral and vertical motion of the spring-loaded mass and the vertical jump of the unsprung mass. Also, we consider two slip motion states with tires in the linear zone and tire slip nonlinear zone based on the Dugoff model and use trip and non-trip rollover under random road input as rollover warning constraint, and conduct flat road DLC test, 20° side slope test, JLTV twisted road respectively by Carsim-Simulink jointly according to NATO double shift line test standard. The improved LTV-MPC algorithm solves the optimal tracking control sequences of 2DOF and 7DOF kinetic models, and the trajectory tracking is completed, compared, and analyzed.

Hierarchical multi-agent control framework for energy efficiency and carbon emission reduction in multi-zone buildings

Centralized optimization approaches to trade-off between comfort, energy and carbon emissions are widely adopted in the control of building heating, ventilation, and air-conditioning (HVAC) systems. However, the high computational complexity in each control horizon, single point of failure risks, and the limited number of zones to control make the centralized approach unattractive. Unlike centralized controls, multi-agent control (MAC) systems are flexible and modular. This paper proposes a scalable multi-agent based distributed approach for optimized control of a multi-zone smart building based on a set of local agents which represent individual zones in the building, coordinated by a central agent. For each control horizon, the coordinator minimizes the overall carbon emissions and assigns an individual energy budget to each local agent. Each local agent minimizes the discomfort in its zone while respecting the energy budget assigned by the coordinator. We propose a heuristic search based on a genetic algorithm to find the optimized control sequences in each zone, and formulate an integer linear programming (ILP) model for the coordinator problem which can be solved using an ILP solver. For a representative winter test day, the proposed methodology gave an energy savings of 8.8% and reduced the carbon footprint by 23.4%.

Reinforcement Learning with Dual Safety Policies for Energy Savings in Building Energy Systems

Reinforcement learning (RL) is being gradually applied in the control of heating, ventilation and air-conditioning (HVAC) systems to learn the optimal control sequences for energy savings. However, due to the “trial and error” issue, the output sequences of RL may cause potential operational safety issues when RL is applied in real systems. To solve those problems, an RL algorithm with dual safety policies for energy savings in HVAC systems is proposed. In the proposed dual safety policies, the implicit safety policy is a part of the RL model, which integrates safety into the optimization target of RL, by adding penalties in reward for actions that exceed the safety constraints. In explicit safety policy, an online safety classifier is built to filter the actions outputted by RL; thus, only those actions that are classified as safe and have the highest benefits will be finally selected. In this way, the safety of controlled HVAC systems running with proposed RL algorithms can be effectively satisfied while reducing the energy consumptions. To verify the proposed algorithm, we implemented the control algorithm in a real existing commercial building. After a certain period of self-studying, the energy consumption of HVAC had been reduced by more than 15.02% compared to the proportional–integral–derivative (PID) control. Meanwhile, compared to the independent application of the RL algorithm without safety policy, the proportion of indoor temperature not meeting the demand is reduced by 25.06%.

Sensor Incipient Fault Impacts on Building Energy Performance: A Case Study on a Multi-Zone Commercial Building

Existing studies show sensor faults/error could double building energy consumption and carbon emissions compared with the baseline. Those studies assume that the sensor error is fixed or constant. However, sensor faults are incipient in real conditions and there were extremely limited studies investigating the incipient sensor fault impacts systematically. This study filled in this research gap by studying time-developing sensor fault impacts to rule-based controls on a 10-zone office building. The control sequences for variable air volume boxes (VAV) with an air handling unit (AHU) system were selected based on ASHRAE Guideline 36-2018: High-Performance Sequences of Operation for HVAC Systems. Large-scale simulations on cloud were conducted (3600 cases) through stochastic approach. Results show (1) The site energy differences could go −3.3% lower or 18.1% higher, compared with baseline. (2) The heating energy differences could go −66.5% lower or 314.4% higher, compared with baseline. (3) The cooling energy differences could go −11.5% lower or 65.0% higher, compared with baseline. (4) The fan energy differences could go 0.15% lower or 6.9% higher, compared with baseline.

Multi-physical cooperative control of plug-in hybrid electric vehicles via cyber hierarchy and interactional network

Cyber hierarchy and interactional network (CHAIN) concept control of autonomous plug-in hybrid electric vehicles is presented to fully release the potential of the vehicle components while guaranteeing the driving safety and stability, which was divided into cyber intelligent driving system (cyber-IDS) on the cyber-end and vehicle intelligent driving system (vehicle-IDS) on the vehicle-end. On the cyber-end, the state information of surrounding vehicles and infrastructures about traffic conditions are uploaded via vehicle-to-everything (V2X) and processed in the cloud to obtain future driving behavior via car following theory, achieving automatic following control. On the vehicle-end, the vehicle-IDS determines the set of its future control variables, and simultaneously provides an optimized control sequence in the prediction horizon by performing approximate global optimization instead of local optimization. The system global optimization needs the high coupling between the macro physical process(cyber-IDS) and the micro physical process(vehicle-IDS), and only the macro planning cannot be directly applied to the micro process control. The nonlinear system at the vehicle-end is decomposed into linear systems around each working point, and the dynamic characteristics between nodes are connected by neural network based on radial basis function of heterogeneous distribution coefficients. By introducing the genetic algorithm for time series, the CHAIN autonomous driving strategy capable of macro and micro coupling can realize the accurate optimal control under the actual driving conditions, which not only eliminates random fluctuations, but also enables a full-lifespan fuel saving of autonomous hybrid vehicles as well as no damage to battery health.

Benchmarking high performance HVAC Rule-Based controls with advanced intelligent Controllers: A case study in a Multi-Zone system in Modelica

The design, commissioning, and retrofit of heating, ventilation, and air-conditioning (HVAC) control systems are crucially important for energy efficiency. However, designers and control contractors adopt ad-hoc control sequences based on diffused and fragmented information; therefore, most of the existing control sequences are diverse and sub-optimal. ASHRAE Guideline 36 (GDL36), High-performance Sequences of Operation (SOO) for HVAC Systems, was thus developed to provide standardized and high-performance rule-based HVAC control sequences with the main focus on maximizing energy efficiency. However, only limited studies verify the performance of these high-performance rules-based control and most of these studies focus on the single-zone systems. In this Modelica-based simulation study, the high-performance rule-based control sequences from GDL36 were compared to the state-of-the-art intelligent controllers (i.e., optimization-based controller (OBC) and deep reinforcement-learning-based controller (DRLC)) in terms of the energy efficiency and thermal comfort. The performance evaluation was conducted in a medium-sized office building with a multi-zone Variable Air Volume (VAV) cooling system. The OBC and DRLC replaced the GDL36 airside supervisory level control loops. In other words, the optimal supply air temperature (SAT) and static differential pressure (DP) setpoints were determined by the optimization problems in OBC and trained control policy by DRLC. The OBC and DRLC were formulated to minimize the HVAC energy consumption and zone air temperature violation. The OBCs with different control intervals and DRLCs with different hyperparameters in the Proximal Policy Optimization (PPO) algorithm were exploited and fine-tuned. The simulation results show that the GDL36 has a comparable energy performance (within a 3% deviation) with DRLC in the cases with high or mild cooling loads. It also has a comparable energy performance (within a 3% deviation) with OBC in the case of a high cooling load. However, the case with GDL36 consumed 7% more energy in the testing shoulder period. For the thermal comfort metric, the GDL36 has slightly more zone air temperature violations in all testing scenarios compared to the two intelligent controllers. From this case study, we can conclude that the GDL36 has demonstrated its comparable performance in terms of energy efficiency and thermal comfort with the two intelligent controllers. The GDL36 airside SOO is good enough considering the complexity and tuning efforts of the intelligent controllers.

On algebraic Stein operators for Gaussian polynomials

The first essential ingredient to build up Stein’s method for a continuous target distribution is to identify a so-called Stein operator, namely a linear differential operator with polynomial coefficients. In this paper, we introduce the notion of algebraic Stein operators (see Definition 3.4), and provide a novel algebraic method to find all the algebraic Stein operators up to a given order and polynomial degree for a target random variable of the form Y=h(X), where X=(X1,…,Xd) has i.i.d. standard Gaussian components and h∈K[X] is a polynomial with coefficients in the ring K. Our approach links the existence of an algebraic Stein operator with null controllability of a certain linear discrete system. A MATLAB code checks the null controllability up to a given finite time T (the order of the differential operator), and provides all null control sequences (polynomial coefficients of the differential operator) up to a given maximum degree m. This is the first paper that connects Stein’s method with computational algebra to find Stein operators for highly complex probability distributions, such as H20(X1), where Hp is the p-th Hermite polynomial. Some examples of Stein operators for Hp(X1), p=3,4,5,6, are gathered in the Appendix and many other examples are given in the Supplementary Information.

Cold Start of a PEMFC using Alternating Hydrogen Pump: Part II. Optimization and Comparison with Rapid Self-Heating

The growing interest in deploying fuel cell vehicles in heavy duty sectors and cold regions has imposed more stringent requirements for cold start methods. The alternating hydrogen pump (AHP) method enables efficient cold starts while avoiding failure and degradation caused by freezing of super-cooled water. Herein, the sensitivity of parameters such as the alternating frequency and gas flow rate, as well as the effects of H2 supply methods and power control sequences, are explored for increasing the heating power using the model developed in Part I. With the optimized values, the heating power of AHP is tripled, enabling PEMFCs with graphite bipolar plates to start-up from −30°C to 0°C within 30 s. In addition, the superiority of AHP in terms of capability, degradation, and practical convenience is demonstrated through the comparison with state-of-the-art rapid self-heating method.

Optimally Band-Limited Noise Filtering for Single-Qubit Gates

We introduce a quantum control protocol that produces smooth, experimentally implementable control sequences optimized to combat temporally correlated noise for single-qubit systems. The control ansatz is specifically chosen to be a functional expansion of discrete prolate spheroidal sequences, a discrete time basis known to be optimally concentrated in time and frequency, and quite attractive when faced with experimental control hardware constraints. We leverage the filter function formalism to transform the control problem into a filter design problem, and show that the frequency response of a quantum system can be carefully tailored to avoid the most relevant dynamical contributions of noise processes. Using gradient ascent, we obtain optimized filter functions and exploit them to elucidate important details about the relationship between filter function design, control bandwidth, and noise characteristics. In particular, we identify regimes of optimal noise suppression and, in turn, optimal control bandwidth directly proportional to the size of the frequency bands where the noise power is large. In addition to providing guiding principles for filter design, our approach enables the development of controls that simultaneously yield robust noise filtering and high-fidelity single-qubit logic operations in a wide variety of complex noise environments.

The Categorical Deviation Effect May Be Underpinned by Attentional Capture: Preliminary Evidence from the Incidental Recognition of Distracters

ABSTRACT The performance of a visual focal task is appreciably disrupted by an unexpected change (or deviation) in the properties of a task irrelevant auditory background. A vast amount of evidence suggests that a change in the acoustic properties of sound disrupts performance via attentional capture. However, an emerging body of evidence suggests that the disruption of task performance by a change in semantic category within a stream of sounds does not behave the same and is therefore not produced by attentional capture. This preliminary study aimed to further investigate whether the disruption produced by a categorical deviant was underpinned by attentional capture. In a single experiment, participants were presented with an irrelevant sound stream while they memorized a categorized list for free recall. We examined whether free recall performance was disrupted by an unexpected change in category within the sound and later investigated, via a surprise recognition test, whether participants had superior memory for deviant items as compared to items from the same positions in control sequences. Results revealed that the categorical deviation effect manifested in poorer free recall performance. Additionally, post-study, participants demonstrated better recognition memory for deviant items compared to control items. On the assumption that explicit recognition requires attentional encoding of deviant items, our results yield evidence that the categorical deviation effect may indeed be produced via attentional capture.

From Au11 to Au13: Tailored Synthesis of Superatomic Di-NHC/PPh3-Stabilized Molecular Gold Nanoclusters

Herein, we report a new method to synthesize molecular gold nanoclusters (AuNCs) stabilized by phosphine (PR3) and di-N-heterocyclic carbene (di-NHC) ligands. The interaction of di-NHC gold(I) complexes, with the general formula [(di-NHC)Au2Cl2] with well-known [Au11(PPh3)8Cl2]Cl clusters provides three new classes of AuNCs through a controllable reaction sequence. The synthesis involves an initial ligand metathesis reaction to produce [Au11(di-NHC)(PPh3)6Cl2]+ (type 1 clusters), followed by a thermally induced rearrangement/metal complex addition with the formation of Au13 clusters [Au13(di-NHC)2(PPh3)4Cl4]+ (type 2 clusters). Finally, an additional metathesis process yields [Au13(di-NHC)3(PPh3)3Cl3]2+ (type 3 clusters). The electronic and steric properties of the employed di-NHC ligand affect the product distribution, leading to the isolation and full characterization of different clusters as the main product. A type 3 cluster has been also structurally characterized and was preliminarily found to be strongly emissive in solution.

Constrained event-driven policy iteration design for nonlinear discrete time systems

This paper studies an adaptive critic design (ACD) solution based on the event-driven mechanism for the constrained state and input system with external disturbance. At first, the barrier function is presented to construct the transformation of the constrained state system, and then, a suitable value function with non-quadratic utility function is designed according to the auxiliary system to acquire the control sequence in the infinity domain. After that, the comprehensive problem is transited into solving the Hamilton–Jacobi-Isaacs (HJI) equation. Then, the optimal control policy pair is obtained by policy iteration (PI) via double event-driven scheme. The three critic, action and disturbance neural networks (NNs) are built up to manipulate the data online concurrently to approximate the control pair sequence, and the ultimately uniformly bounded (UUB) proof of the estimation error generated by NNs is given. The last but not least, two sets of contrast simulations are analyzed to demonstrate the effectiveness of the proposed algorithm, which makes the model reduce the data transmission density and decrease the update times of control policies under the premise of system stability and ideal performance.

TC-Driver: A Trajectory Conditioned Reinforcement Learning Approach to Zero-Shot Autonomous Racing

Autonomous racing is becoming popular for academic and industry researchers as a test for general autonomous driving by pushing perception, planning, and control algorithms to their limits. While traditional control methods such as model predictive control are capable of generating an optimal control sequence at the edge of the vehicles’ physical controllability, these methods are sensitive to the accuracy of the modeling parameters, such as tire modeling coefficients. As model mismatch is inevitable in reality, the heuristic nature of Reinforcement Learning (RL) offers a viable approach to modeling robustness. This paper presents TC-Driver, an RL approach for robust control in autonomous racing. In particular, the TC-Driver agent is conditioned by a trajectory generated by any arbitrary traditional high-level trajectory planner. The proposed TC-Driver architecture addresses the tire parameter modeling inaccuracies by exploiting the learning capabilities of RL while utilizing the reliability of traditional planning methods in a hybrid fashion. We train the agent under varying tire conditions, allowing it to generalize to different model parameters, aiming to increase the racing capabilities of the system in practice. Experimental results demonstrate that the proposed hybrid RL architecture of the TC-Driver improves the generalization robustness of autonomous racing agents when compared to a previous state-of-the-art end-to-end-based architecture. Namely, the proposed controller yields a 29-fold improvement in crash ratio when facing model mismatch and can zero-shot transfer its behavior on unseen tracks which present completely new features, while the end-to-end baseline fails. When deployed on a physical system, the proposed architecture demonstrates zero-shot Sim2Real capabilities that outperform end-to-end agents 10-fold in terms of crash ratio while exhibiting similar driving characteristics in reality as in simulation.

Engineered elastin-like polypeptides: An efficient platform for enhanced cancer treatment.

Drug delivery systems (DDSs) have recently gained widespread attention for improving drug loading and delivery efficiency in treating many cancers. Elastin-like polypeptides (ELPs) are synthetic peptides derived from a precursor of elastin (tropoelastin), reserving similar structural and physicochemical properties. ELPs have gained a variety of applications in tissue engineering and cancer therapy due to their excellent biocompatibility, complete degradability, temperature-responsive property, controllable sequence and length, and precisely tuned structure and function. ELPs-based drug delivery systems can improve the pharmacokinetics and biodistribution of therapeutic reagents, leading to enhanced antitumor efficacy. In this review, we summarize the recent application of ELPs in cancer treatment, focusing on the delivery of functional peptides, therapeutic proteins, small molecule drugs, and photosensitizers.

Development of Automatic Dual Sequence Control Temporary Immersion Bioreactor Systems for Micropropagation Coelogyne pandurata Lindl.

Coelogyne pandurata Lindl. is an endemic orchid species of Indonesia from East Kalimantan and Papua. It is locally known as the “black orchid” due to the distinctive black coloration on its labellum with intricate green and hairy lines. Unfortunately, the population of this orchid has significantly declined in its natural habitat, rendering it a rare endangered species. Temporary immersion bioreactor systems have been proven to be efficient for endemic plant micropropagation with performance enhancements and innovations. This study aims to develop an optimized bioreactor design and innovate the automatic control performance of temporary immersion bioreactor systems based on previous research. The control system developed consists of three modules, namely Graphical User Interface (GUI) module, Sequence Control (SC) module, and Hardware Interface (HI) module. The GUI module receives information regarding time and duration of immersion and gas exchange, then the information is synchronized by the SC module, which plays the role of starting and stopping the processes, while HI module executes the order of the automatic control system in the immersion and gas exchange process. The developed bioreactor design and control system offer convenience, require less labor, and ensure precise control over the optimum conditions for black orchid micropropagation.

Genetic Algorithm for Searching Bipolar Single-Flux-Quantum Pulse Sequences for Qubit Control

Nowadays most of superconducting quantum processors use charge qubits of a transmon type. They require implementation of energy efficient qubit state control scheme. A promising approach is the use of superconducting digital circuits operating with single-flux-quantum (SFQ) pulses. The duration of SFQ pulse control sequence is typically larger than that of conventional microwave drive pulses but its length can be optimized for the system with known parameters. Here we introduce a genetic algorithm for unipolar or bipolar SFQ control sequence search that minimize qubit state leakage from the computational subspace. The algorithm is also able to find a solution in the form of a repeating subsequence in order to save memory on the control chip. Its parallel implementation can find the appropriate sequence for arbitrary system parameters from a practical range in a reasonable time. The algorithm is illustrated by the example of the rotation gate around the Y-axis by an angle $$\pi/2$$ with fidelity over 99.99 $$\%$$ . In this paper, we present the results for a single-qubit system, but in the future we will apply the developed approach to study a system of two qubits.

Online Optimal Event-Triggered H∞ Control for Nonlinear Systems With Constrained State and Input

Taking safety and performance into consideration, the state and control input of the actual engineering system are often constrained. For this kind of problem, this article puts forward an online dual event-triggered (ET) adaptive dynamic programming (ADP) optimal control algorithm for a class of nonlinear systems with constrained state and input. First, the original system is transformed into another system through the barrier function, after that, a suitable value function with a nonquadratic utility function is designed to obtain the optimal control pair. In addition, on the premise of the asymptotic stability of the system, the trigger condition is devised, and the intersampling time analysis is proved that the algorithm can avoid the Zeno phenomenon. What is more, the critic, action, and disturbance neural networks (NNs) are trained to approximate value function and control sequences, subsequently, the approximation error is proved to be uniformly ultimately boundedness (UUB). Finally, two comparative experiments based on the robot arm model are simulated to verify that the algorithm can make control policies update only when the system has the requirement and keep satisfactory control effect, which can effectively decrease the number of data transfers and reduce the calculation burden.

Closed-form and robust expressions for data-driven LQ control

This article provides an overview of certain direct data-driven control results, where control sequences are computed from (noisy) data collected during offline control experiments without an explicit identification of the system dynamics. For the case of noiseless datasets, we derive several closed-form data-driven expressions that solve a variety of optimal control problems for linear systems with quadratic cost functions of the state and input (including the linear quadratic regulator problem, the minimum energy control problem, and the linear quadratic control problem with terminal constraints), discuss their advantages and drawbacks with respect to alternative data-driven and model-based approaches, and showcase their effectiveness through a number of numerical studies. Interestingly, these results provide an alternative and explicit way of solving classic control problems that, for instance, does not require the solution of an implicit and recursive Riccati equation as in the model-based setting. For the case of noisy datasets, we show how the closed-form expressions derived in the noiseless setting can be modified to compensate for the bias induced by noise, and perform a sensitivity analysis to reveal favorable asymptotic robustness properties of the derived data-driven controls. We conclude the paper with some considerations and a discussion of outstanding questions and directions of future investigation.

Machine Learning Based Recognition of Elements in Lower-Limb Movement Sequence for Proactive Control of Exoskeletons to Assist Lifting

Machine Learning Based Recognition of Elements in Lower-Limb Movement Sequence for Proactive Control of Exoskeletons to Assist Lifting