Sequential Control Research Articles

Fault-Tolerant Control of a Variable-Pitch Quadrotor under Actuator Loss of Effectiveness and Wind Perturbations

The actuator fault-tolerant control problem for a variable-pitch quadrotor is addressed under uncertain conditions. Following a model-based approach, the plant nonlinear dynamics are faced with a disturbance observer-based control and a sequential quadratic programming control allocation, where only kinematic data of the onboard inertial measurement unit are required for the fault-tolerant control, i.e., it does not require the measurement of the motor speed nor the current drawn by the actuators. In the case of almost horizontal wind, a single observer handles both faults and the external disturbance. The estimation of the wind is fed forward by the controller, while the actuator fault estimation is exploited in the control allocation layer, which copes with the variable-pitch nonlinear dynamics, thrust saturation, and rate limits. Numerical simulations in the presence of measurement noise show the capability of the scheme to handle multiple actuator faults in a windy environment.

Multi-Stage Sequential Network Energy Control for offshore AC asymmetric fault ride-through of MMC-HVDC system integrated offshore wind farms

Modular Multilevel Converter based High Voltage Direct Current (MMC-HVDC) is the preferred solution to the problem of long-distance transmission from large-scale offshore wind farms (OWFs). When an offshore AC asymmetric fault occurs, OWFs and MMC-HVDC will face the challenge of fault ride-through with power electronic converters on both sides of the fault location. In this paper, a Multi-stage Sequential Network Energy Control (MSNEC) is proposed for offshore AC asymmetric fault ride-through (FRT). Firstly, the constraint relationship among the sequence networks under AC asymmetric faults is revealed. Secondly, the energy control potential of WFMMC is fully explored from the perspective of energy transformation and transfer. Energy control reduces the fault phase current amplitude by putting in submodules at the fault phase. Additional sub-modules are put in the non-faulted phase to realize series voltage dividing effect to actively prevent non-faulted phase fault overvoltage. The MSNEC considers the FRT requirements for multiple stages of fault occurrence, fault steady state, and fault recovery. It combines sequential network control, energy control, and voltage support of the converter station. It realizes AC asymmetric FRT from four aspects: power equivalence, negative sequence suppression, energy control, and voltage stabilization. Finally, a simulation model of OWFs connected to the onshore grid via MMC-HVDC is established in PSCAD/EMTDC. Simulation results verify MSNEC not only improves the AC asymmetric FRT capability of the OWFs and WFMMC, but also promotes the efficient consumption of renewable energy and improves the economics of the OWFs grid-connected system.

Synthesis of Pneumatic Systems in the Control of the Transport Line of Rolling Elements

Abstract This paper presents the synthesis of a pneumatic control system for a selected configuration of the transport path for the delivery of rolling elements to spiral storage in inter-operational transport. The sequential control system sets the state of the manifolds to ensure a flow of workpieces to serve the subsequent storage. The essential module of the control system is the memory block. It is developed based on a storage filling sequence graph. The filling level of the storages can be monitored in one or two points using sensors. The rolling element displacement control sensors work together with appropriately designed systems to execute the delay of the rising and falling edge input signal. By using a two-level control of the filling level of the storages, it is possible to control the emptying status of the storages as a function of the technological time of removal of the items from the storage between the two control points. Control systems were synthesised and verified using Festo’s FluidSim computer programme.

Dual Stabilization of the Multidimensional Regression Object at the Given Level

The statement of the problem of the dual control of the regression object with multidimensional-matrix input and output variables and dynamic programming functional equations for its solution are given . The problem of the dual stabilization of the regression object at the given level is considered . The purpose of control is reaching the given value of the output variable by sequential control actions in production operation mode . In order to solve the problem , the regression function of the object is supposed to be affine in input variables , and the inner noise is supposed to be Gaussian . The sequential solution of the functional dynamic programming equations is performed . As a result , the optimal control action at the last control step is obtained . It is shown also that the obtaining of the optimal control actions at the other control steps is connected with big difficulties and impossible both a nalytically and numerically . The control action obtained at the last control step is proposed to be used at the arbitrary control step . This control action is called the control action with passive information accumulation . The dual control algorithm with passive information accumulation was programmed for numerical calculations and tested for a number of objects . It showed acceptable results for the practice . The advantages of the developed algorithm are theoretical and algorithmical generality .

Inchworm-Inspired soft robot with magnetic driving based on PDMS, EGaIn and NdFeB (PEN) combination

Soft robot has been widely used in many fields owing to flexible movement, good deformability and strong adaptability. To control soft robots, a variety of driving mechanisms have been explored, including light, electric, gas, and magnetic actuation. Of these mechanisms, magnetic actuation has gained significant importance due to its penetrability, rapid response and non-destructive performance. However, substrates made of soft composite films often suffer from poor toughness and flexibility. To address this issue, we designed a composite film composed of polydimethylsiloxane (PDMS), eutectic gallium indium (EGaIn) and neodymium ferrite boron (NdFeB) (PEN) at an optimal ratio of 10:4:2. The film exhibits exceptional properties, including an elastic modulus of 1.17 MPa, thermal stability of 2.6625e-6 K−1, contact angle of 121.1°, tearing energy of 7.36 KJ/m2, swelling degree of 0.02446 and cross-linking density of 1.52 × 10-3mol/cm3. As a result, it is capable of achieving superior motion performance for inchworm-like robots. With the help of real-time motion analysis, we obtained the motion stage, actuating velocity of 48.3 mm/s and sequential magnetic control time of 20 s for the inchworm-like soft robot. These results indicate a high degree of sensitivity and stability. In conclusion, the developed inchworm-robot based on the novel PEN material exhibits exceptional deformation, flexibility, velocity and magnetic controllability under magnetic driving.

Disjunctive Delimited Control

AbstractDelimited control is a powerful mechanism for programming language extension which has been recently proposed for Prolog (and implemented in SWI-Prolog). By manipulating the control flow of a program from inside the language, it enables the implementation of powerful features, such as tabling, without modifying the internals of the Prolog engine. However, its current formulation is inadequate: it does not capture Prolog’s unique non-deterministic nature which allows multiple ways to satisfy a goal.This paper fully embraces Prolog’s non-determinism with a novel interface for disjunctive delimited control, which gives the programmer not only control over the sequential (conjunctive) control flow, but also over the non-deterministic control flow. We provide a meta-interpreter that conservatively extends Prolog with delimited control and show that it enables a range of typical Prolog features and extensions, now at the library level: findall, cut, branch-and-bound optimisation, probabilistic programming, …

Malaria control and elimination in Kenya: economy-wide benefits and regional disparities

BackgroundMalaria remains a public health problem in Kenya despite several concerted control efforts. Empirical evidence regarding malaria effects in Kenya suggests that the disease imposes substantial economic costs, jeopardizing the achievement of sustainable development goals. The Kenya Malaria Strategy (2019–2023), which is currently being implemented, is one of several sequential malaria control and elimination strategies. The strategy targets reducing malaria incidences and deaths by 75% of the 2016 levels by 2023 through spending around Kenyan Shillings 61.9 billion over 5 years. This paper assesses the economy-wide implications of implementing this strategy.MethodsAn economy-wide simulation model is calibrated to a comprehensive 2019 database for Kenya, considering different epidemiological zones. Two scenarios are simulated with the model. The first scenario (GOVT) simulates the annual costs of implementing the Kenya Malaria Strategy by increasing government expenditure on malaria control and elimination programmes. The second scenario (LABOR) reduces malaria incidences by 75% in all epidemiological malaria zones without accounting for the changes in government expenditure, which translates into rising the household labour endowment (benefits of the strategy).ResultsImplementing the Kenya Malaria Strategy (2019–2023) enhances gross domestic product at the end of the strategy implementation period due to more available labour. In the short term, government health expenditure (direct malaria costs) increases significantly, which is critical in controlling and eliminating malaria. Expanding the health sector raises the demand for production factors, such as labour and capital. The prices for these factors rise, boosting producer and consumer prices of non-health-related products. Consequently, household welfare decreases during the strategy implementation period. In the long run, household labour endowment increases due to reduced malaria incidences and deaths (indirect malaria costs). However, the size of the effects varies across malaria epidemiological and agroecological zones depending on malaria prevalence and factor ownership.ConclusionsThis paper provides policymakers with an ex-ante assessment of the implications of malaria control and elimination on household welfare across various malaria epidemiological zones. These insights assist in developing and implementing related policy measures that reduce the undesirable effects in the short run. Besides, the paper supports an economically beneficial long-term malaria control and elimination effect.

Maximum Entropy Optimal Control of Continuous-Time Dynamical Systems

Maximum entropy reinforcement learning methods have been successfully applied to a range of challenging sequential decision-making and control tasks. However, most of the existing techniques are designed for discrete-time systems although there has been a growing interest to handle physical processes evolving in continuous time. As a first step toward their extension to continuous-time systems, this article aims to study the theory of maximum entropy optimal control in continuous time. Applying the dynamic programming principle, we derive a novel class of Hamilton–Jacobi–Bellman (HJB) equations and prove that the optimal value function of the maximum entropy control problem corresponds to the unique viscosity solution of the HJB equation. We further show that the optimal control is uniquely characterized as Gaussian in the case of control-affine systems and that, for linear-quadratic problems, the HJB equation is reduced to a Riccati equation, which can be used to obtain an explicit expression of the optimal control. The results of our numerical experiments demonstrate the performance of our maximum entropy method in continuous-time optimal control and reinforcement learning problems.

Comparison and analysis of predictive control of induction motor without weighting factors

The research and promotion of electric drive systems for new energy transportation equipment is an important link in the realization of low-carbon transportation. Model predictive control is an effective new control strategy for electrical drive systems. Taking the three phase induction motor drive system as the research object, three kinds of model predictive torque control methods are reviewed and analyzed in depth, and a theoretical proof of Pareto optimality of MPC methods without weighting factors is established. Predictive torque control is a classical model predictive control method for induction motor drives. When using it, a trial-and-error method is usually used to set appropriate weighting factors for different control objectives, which strongly depends on the designer’s experience. By changing the structure of the cost function, the sequential model predictive control eliminates the weighting factors and improves the control effect of the stator current. The even-handed model predictive control utilizes the interaction error and considers all the control objectives at the same time, so that the optimization order of the control objectives changes with the working conditions, which further improves the control performance. Through theoretical analysis and experimental test, this paper illustrates the performance differences between traditional predictive torque control, sequential model predictive control and even-handed model predictive control.

Sequential Stochastic Control (Single or Multi-Agent) Problems Nearly Admit Change of Measures with Independent Measurement

Sequential Stochastic Control (Single or Multi-Agent) Problems Nearly Admit Change of Measures with Independent Measurement

Control system of high-voltage pulse power supply for compact torus injector based on EPICS

The Compact Torus Injector (CTI) is a magnetic confinement fusion device feeding system, which injects the excited compact toroidal plasma into the target device at high speed, and controls the fuel injection and injection speed to achieve optimal control of the density pressure profile of the magnetic confinement fusion device. As the important subsystem of the CTI, the CTI High-Voltage Pulse Power Supply (HVPPS) system consists of five sets of power supply. This paper uses EPCIS as a development platform to design the control system of the CTI HVPPS which realizes timing sequential control, status detection, parameter setting and read-back status of the CTI power supplies with different communication interfaces and protocols. The discharge experiments are performed on the CTI. The experimental results prove that the control system meets the real-time control requirements of CTI devices for HVPPS. (Abstract)

A Synthesis of Algorithms Determining a Safe Trajectory in a Group of Autonomous Vehicles Using a Sequential Game and Neural Network

This paper presents a solution to the problem of providing an autonomous vehicle with a safe control task when moving around many other autonomous vehicles. This is achieved by developing an appropriate computer control algorithm that takes into account the possible risk of a collision resulting from both the impact of environmental disturbances and the imperfection of the rules of maneuvering in situations where many vehicles pass each other, giving the control process a decisive character. For this purpose, three types of algorithms were synthesized: kinematic and dynamic optimization with neural domains, as well as sequential game control of an autonomous vehicle. The control algorithms determine a safe trajectory, which is implemented by the actuators of the autonomous vehicle. Computer simulations of the control algorithms in the Matlab/Simulink software allow for their comparative analysis in terms of meeting the criteria for the optimality and safety of an autonomous vehicle when passing a larger number of other autonomous vehicles. For this purpose, scenarios of multidirectional and one-way traffic of autonomous vehicles were used.

A cooperative game approach to synthesizing a sequential distributed model predictive controller

A cooperative game approach to synthesizing a sequential distributed model predictive controller

Impact of Sequential Model Predictive Control on Induction Motor Performance: Comparison of Converter Topologies

Finite Set Model Predictive Control (FS-MPC) is a widely used technique in power electronic converter applications. One challenge in FS-MPC implementation is selecting appropriate weighting factors, as there is currently no established methodology for finding the best values. An alternative approach is to consider cost functions without weighting factors, as used by the Sequential Model Predictive Control (SMPC). In this paper, the performance of SMPC applied to induction motors is analyzed. The SMPC strategy involves sequentially evaluating simple cost functions by considering a limited number of available switching states for the power electronic converter. This number is the control parameter of the SMPC. The parameter’s domains and a selection criteria based on THD were established in this investigation. The power converter topologies studied include the Voltage Source Inverter (VSI) and the Neutral Point Clamped three-level (3L-NPC). Simulations performed in PLECS software and Hardware-in-the-Loop (HIL) tests using an RT Box for valid parameters satisfy the characteristics of the classical predictive control, such as good control variables tracking and high dynamic response. For a VSI converter, increasing the control parameter results in reduced harmonic distortion, while for an NPC converter, optimal results are achieved with control parameter values within a specific range.

Sequential Control of Cellular Interactions Using Dynamic DNA Displacement.

Intercellular interactions play a significant role in various complex biological processes, and their dysregulation promotes disease progression. To reveal the mechanisms of intercellular interactions without destroying basic life processes, it is necessary to mimic multicellular behaviors in vitro. However, the precise control of multicellular systems remains technically challenging owing to dynamic interactions. Here, we used DNA as a molecular lock and key to sequentially assemble and disassemble different cell clusters in a programmed way, regulating intercellular interactions. Tagging the surface of live cells with cholesterol-modified DNA enabled dynamical intercellular assemblies. By consecutively adding corresponding metaphorical locks (attaching DNA strands) and keys (detaching DNA strands), clusters of different cells could be sequentially formed. This strategy improved the capability of natural killer NK-92 cells to target tumor cells, improving the antitumor therapy efficacy. Our suggested approach allows dynamic regulation of intercellular interactions in complex cell systems and increases understanding of intercellular communication networks.

Model predictive control for path tracking in cable driven parallel robots with flexible cables: collocated vs. noncollocated control

This paper proposes a novel control scheme for precise path tracking control in cable driven parallel robots (CDPRs) with axially-flexible cables, with particular focus to the challenging case of cable suspended parallel robots (CSPRs). To handle model nonlinearities while ensuring small computational effort, a controller made by two sequential control actions is developed. The first term is a position-dependent, model predictive control (MPC) with embedded integrator to compute the optimal cable tensions ensuring accurate path tracking and fulfilling the feasibility constraints; bounds on the feasible tensions are also included. The second control term transforms the optimal tensions into the commanded motor torques, and hence currents, that are evaluated through the kinetostatic model of the electric motors used for winding and unwinding the cables. Control design is performed through the robot dynamics model, formulated with the assumption of rigid cables. Moreover, the proposed control strategy is presented in two different architectures, collocated control and noncollocated control. Flexibility is handled by penalizing large tension variations in the cost function adopted in the controller design, plus some hard constraints on the maximum tension derivatives. These features, together with the embedding of the integrator within the MPC formulation, ensure smooth control tensions that allow handling the axial flexibility of the cables, although it is not explicitly considered in the controller design.To assess the performances of the proposed control algorithm, a kinematically-determined robot with a suspended, lumped end-effector is simulated by also adopting very flexible cables. Additionally, a simplified dynamic model of the electrical dynamics and the sensor quantization are included to provide a realistic representation of the real environments. The results, together with the fair comparison with a benchmark, corroborate the effectiveness of the proposed approach, its robustness, and its feasibility in real-time controllers due to the wise reduction of the computational effort.

A Clinically Viable Approach to Cell Programming

A Clinically Viable Approach to Cell Programming

Bridging Direct and Indirect Data-Driven Control Formulations via Regularizations and Relaxations

In this article, we discuss connections between sequential system identification and control for linear time-invariant systems, often termed indirect data-driven control, as well as a contemporary direct data-driven control approach seeking an optimal decision compatible with recorded data assembled in a Hankel matrix and robustified through suitable regularizations. We formulate these two problems in the language of behavioral systems theory and parametric mathematical programs, and we bridge them through a multicriteria formulation trading off system identification and control objectives. We illustrate our results with two methods from subspace identification and control: namely, subspace predictive control and low-rank approximation, which constrain trajectories to be consistent with a nonparametric predictor derived from (respectively, the column span of) a data Hankel matrix. In both cases, we conclude that direct and regularized data-driven control can be derived as convex relaxation of the indirect approach, and the regularizations account for an implicit identification step. Our analysis further reveals a novel regularizer and a plausible hypothesis explaining the remarkable empirical performance of direct methods on nonlinear systems.

Adaptive control system for the water temperature at the outlet of the waste boiler

The experience of developing an adaptive system for controlling the temperature of network water at the outlet of a waste heat boiler is considered. When developing an adaptive control system, a number of sequential control problems were solved: creating a nonlinear model of a waste heat boiler based on a recurrent neural network, studying the frequency characteristics of a neural network model, identification structure and parameters of a control object, synthesizing a typical controller, creating an algorithm for adapting the parameters of a typical controller based on a fuzzy logic. A waste heat boiler is considered as a control object. The control object is represented by a multidimensional thermodynamic system with non-linear characteristics. Training and verification of the neural network model was carried out on the data obtained during the operation of the boiler. Identification of the structure and parameters of the control object is carried out according to the frequency response of the neural network model of the boiler. The algorithm for adapting the parameters of a typical controller contains sets of parameters of a typical controller for different states of the system with different bandwidths. The article presents a description of the stages of synthesis of an adaptive control system, the results of studying a neural network model in the frequency domain, the results of identifying a control object, calculating the parameters of a typical controller for different system states, describing a fuzzy adaptation algorithm, and modeling results.

Analysis and Design of Integrated Intelligent Anti-error System for Substation

A new comprehensive intelligent anti-error system that meets the requirements of smart substations is proposed. This new system can overcome the problem that the microcomputer anti-error system lacks operation error prevention technology for secondary equipment and also lacks operation and maintenance operations under the integrated control operation mode. Firstly, this paper analyzes the characteristics of the anti-error system, and the principle function of the judgment model. And then, an anti-misoperation process system is established for work scenarios such as information collection and monitoring, remote control operation error prevention, sequential control operation error prevention, local operation error prevention, secondary equipment, operation, and maintenance. This paper finally builds a realtime online anti-error system that can be intelligently analyzed for the whole business process to ensure the safety of intelligent substation equipment operation.