Control Constraints Research Articles

Active-set pseudospectral model predictive static programming for midcourse guidance

This paper focuses on the midcourse guidance law for the interceptor to engage the hypersonic gliding vehicles (HGVs). Firstly, an active-set pseudospectral model predictive static programming (APS-MPSP) algorithm is presented, which entails two major improvements: the application of pseudospectral discretion and handling of control constraint using active-set iteration strategy. Except inheriting the high efficiency of the MPSP, this new algorithm can address the control constraint directly. Secondly, the optimal predictive guidance design is established, which embeds the target prediction into the guidance framework by formulating a simultaneous nonlinear optimal problem. Employing the APS-MPSP algorithm to solve this optimal problem efficiently, the predictive guidance command that ensures interceptor reach an appropriate predicted interception point (PIP) can be obtained directly. This strategy has the merits of no need to precompute the PIP or time-to-go and high prediction accuracy. The reliability and efficiency of the proposed methods are verified by numerical examples and comparisons with already methods.

A RERNN-SGO Technique for Improved Quasi-Z-Source Cascaded Multilevel Inverter Topology for Interfacing Three Phase Grid-Tie Photovoltaic System

In this manuscript, a proficient control strategy-based improved Quasi-Z-Source Cascaded Multilevel Inverter (QZS-CMI) topology for interfacing photovoltaic (PV) system is proposed. The control strategy is joint execution of both recalling-enhanced recurrent neural network (RERNN) and Shell Game Optimization (SGO), therefore it is called RERNN-SGO technique. The major intention of proposed system determined the photovoltaic system efficiency and maximize the power extraction. The interface among load and PV dc source is to accomplish by the QZSI. At first, the objective function is determined depending on the control constraint and parameters, like current, voltage, modulation index, power. These parameters are utilized to propose RERNN-SGO method. The RERNN-SGO method is used to improve the power delivery, voltage profile and minimum power oscillation for power distribution with load. The maximal power delivered the load is to ensure the artificial intelligence (AI) strategy with the help of maximal power point tracking (MPPT). To regulate shoot through duty ratio and modulation load, the proposed AI method is used. The proposed system is inspired on MATLAB/Simulink site, and then efficiency is related with existing systems under various load conditions. The efficiency of proposed system in Case 1 and Case 2 is 87.363% and 85.3904%.

Singularity avoidance controller design for spacecraft attitude control using double-gimbal variable-speed control moment gyro

A sub-optimal singularity avoidance large angle rest-to-rest attitude maneuver control of a rigid spacecraft actuated using a double-gimbal variable-speed control moment gyro (DGVSCMG) is proposed for avoiding the control torque saturation and the gimbal lock singularity. To achieve this objective, a modified model predictive static programming (MPSP) algorithm subject to control and state constraints is proposed and developed. A cuboid rigid spacecraft equipped with a single DGVSCMG device for the attitude control is considered to validate the proposed control algorithm. A new MPSP based control architecture for the attitude control of a spacecraft using a DGVSCMG is developed, wherein the desired gimbal angle rates and rotor spin acceleration are taken as the control input and computed directly using the MPSP algorithm without requiring the steering law. The control law ensures attitude stabilization of the spacecraft-DGVSCMG system while successfully avoiding the control input saturation and the gimbal lock singularity of the DGVSCMG, thereby enabling large angle attitude maneuvers. Multiple test cases of rest-to-rest attitude maneuvers are simulated to show the efficacy of the proposed control.

Joint SIC-Based Precoding and Sub-Connected Architecture Design for MIMO VLC Systems

Densely deployed light emitting diodes (LEDs) typically lead to high spatial correlation in multiple-input multiple-output (MIMO) visible light communication (VLC) systems. Although precoding can effectively alleviate the spatial correlation issue, most existing precoding algorithms require a dedicated baseband chain for each LED, leading to high energy consumption and hardware complexity when a large number of LEDs are used. In this paper, a successive interference cancellation (SIC)-based precoding scheme with sub-connected architecture (SIC-SA) is proposed. In the considered model, each baseband chain is connected to an LED sub-array containing multiple LEDs to reduce the complexity. Since, in this case, SIC-based precoding can only determine the signal of each baseband chain for an LED sub-array, while its target is to mitigate the spatial correlation between individual LEDs, the electrical/optical power of each LED must also be jointly optimized to accomplish the target. This joint SIC-based precoding, power allocation, and direct current offset design problem is formulated as an achievable sum rate maximization problem under dimming control and electrical power constraints. A two-step iterative algorithm is proposed to solve this problem. In the first step, the SIC-based precoding is designed to alleviate the multi-user interference. In the second step, the power allocation of LEDs is optimized by matrix decomposition and convex optimization, and a closed-form solution of DC offset is derived. Furthermore, considering the dynamic scenarios, a SIC-based precoding scheme with dynamic sub-connected architecture (SIC-DSA) is proposed, in which a switch network is used to adaptively adjust the LED sub-array structure based on the channel state information. Simulation results show that the proposed SIC-SA and SIC-DSA respectively achieve 0.1340 bps/Hz/W and 0.1305 bps/Hz/W energy efficiency gains over the zero-forcing precoding scheme with SA, when the signal-to-noise ratio is 30 dB.

An Improved Max-Min Game Theory Control of Fuel Cell and Battery Hybrid Energy System Against System Uncertainty

In this article, a new energy management strategy (EMS) for a smart building with fuel cell and battery hybrid energy storage (FBHES) is proposed based on an improved game theory (GT) with higher system efficiency and extending lifetime for both battery and fuel cell (FC). The building working as a smart energy hub is regarded as one of the key factors for energy-saving and long-term emissions reduction. EMS that determines the optimal power flow for each energy sector is of great importance for smart buildings subject to the increasing renewable generations, various energy storage, and flexible loads. The energy storage system plays a key role to unlock the management regions and control constraints of the EMS; however, the lifetime of the energy storage device is generally less than expected because of the power uncertainty brought by new energy sectors. Therefore, in this article, the FBHES and the uncertainty of the demand power are considered as two aspects in the game, aiming at decoupling the randomness of electricity demand and hybrid energy storage performance. The EMS is developed with an improved max-min GT, where the compensation effect of the battery is developed to keep power balance in the smart building system. Simulation results show that the degradation of the FC obtained by the proposed method, the improved GT, which is 12.64% lower than the traditional GT. Meanwhile, the lifetime of the battery under the proposed method is increased by 23.16% and 82.81% compared with mixed-integer linear programming EMS and rule-based strategy.

Event-Triggered Model Predictive Control for Aero-Engine Transient-State Tracking Problems

Transient-state control is one of the most important parts in aero-engine design. It determines the performance of an aero-engine or even the maneuverability of an aircraft. An optimized transient-state process can stimulate the potential performance of an aero-engine. Thus, model predictive control (MPC) is widely applied in transient-state tracking control with complex control constraints. However, the enormous computation pressure restricts its implementation in aero-engine transient-state tracking control. This paper aims to improve the efficiency of MPC and investigates the event-triggered model predictive control (EMPC) for aero-engine transient-state tracking problems. A new event-triggered mechanism that can exclude the Zeno behavior without an artificial interevent time is designed based on linear parameter-varying (LPV) aero-engine models. The feasibility and stability of the designed EMPC are investigated, and a numerical simulation example is presented to show the effectiveness of the designed method. The simulation result shows that our proposed method can reduce the computation load and time consumption significantly compared with MPC.

Smoothing Analysis of Two Robust Multigrid Methods for Elliptic Optimal Control Problems

In this paper we study and compare two multigrid relaxation schemes with coarsening by two, three, and four for solving elliptic sparse optimal control problems with control constraints and combined and cost functional. First, we perform a detailed local Fourier analysis (LFA) of a well-known collective Jacobi relaxation (CJR) scheme for the unconstrained case with only cost functional, where the optimal smoothing factors are derived. This insightful analysis reveals that the optimal relaxation parameters depend on both the mesh step size and the regularization parameter , which was not investigated in literature. Second, we propose and analyze a new mass-based Braess--Sarazin relaxation (BSR) scheme, which is proven to provide smaller smoothing factors than the CJR scheme when for a small constant . Finally, these schemes are successfully extended to control-constrained cases through the semismooth Newton method, where the corresponding Jacobian systems are treated by the proposed multigrid schemes. The nonstandard coarsening by three or four with BSR is competitive with the standard coarsening by two. Numerical examples are presented to validate our theoretical outcomes. The proposed inexact BSR (IBSR) scheme, where two preconditioned conjugate gradients (PCG) iterations are applied to solve the Schur complement system, yields a better computational efficiency than the CJR scheme in the conducted numerical comparison.

Grouping consistency control strategy based on DMPC and energy storage unit constraints

This paper proposes a distributed packet consistency control strategy to solve the distributed coordinated control problem among multiple battery storage units. In order to ensure that the distributed power allocation results meet the power constraints of each energy storage unit and reduce the communication requirements of distributed control, this paper proposes a weighted consistency algorithm based on distributed model predictive control and state constraints. A grouping control strategy of energy storage units is proposed. The weight of energy storage unit is set according to SOC information to improve consistency of SOC of energy storage unit. Meanwhile, based on the proposed consistency algorithm, an inter-group coordination control strategy and an efficiency improvement strategy of energy storage units are developed to improve the regulation ability of energy storage system, extend the life of energy storage system and improve the energy conversion efficiency. Finally, the proposed algorithm and control strategy are simulated and analyzed by building a microgrid system with eight battery storage units in Matlab. The simulation results show that the proposed algorithm and control strategy have certain advantages in improving convergence speed, optimizing control effect, prolong energy storage life and improve the operation efficiency of the energy storage system.

Integrating Nonlinear Controllability into a Multidisciplinary Design Process

Even in multidisciplinary aircraft design, most aspects of nonlinear controllability are only considered in later design phases. Hence, control considerations are not used to inform the overall design of the airframe. This paper presents a control-aware controller-agnostic design optimization framework that integrates nonlinear controllability constraints, which are informed by the solutions to optimal control problems, into the multidisciplinary design process. To implement the optimization, procedures to evaluate the sensitivity of the controllability constraint to variations in the design variables are defined. A supersonic aircraft case study illustrates the methodology and the steps involved in the process. The proposed approach enables the integration of control-related constraints into gradient-based design optimization.

Formation Coordination Control of Leaderless Multi-AUV System with Double Independent Communication Topology and Nonconvex Control Input Constraints

In this paper, the formation coordination control of discrete-time distributed leaderless multiple autonomous underwater vehicle (AUV) system with double independent position–velocity communication topology and control inputs on a nonconvex set is studied. Firstly, the problem of formation coordination control of multi-AUV system is transformed into the problem of formation consensus of multi-AUV system, and the consistent state of leaderless multi-AUV system formation was defined. Secondly, considering the existence of bounded communication delay and nonconvex control input constraints for multi-AUV system formation under weak communication conditions, a formation consistent constraint controller algorithm for discrete-time leaderless multi-AUV system with double independent communication topology is proposed by introducing constraint operators. By using the properties of graph theory, random matrix and SIA matrix, and selecting appropriate controller parameters, the multi-AUV system formation can reach the defined consensus state. Furthermore, the unbounded communication delay of multi-AUV system formation is studied. Finally, the simulation results show that the proposed controller constraint algorithm is effective.

ANALISA PENGENDALIAN INTERN SISTEM INFORMASI AKUNTANSI PENGGAJIAN KARYAWAN OUTSOURCING PADA PT SWABINA GATRA

This study aims to determine the implementation of internal control of the outsourced employee payroll accounting information system and what are the constraints of the internal control of the outsourced employee payroll accounting information system. The data used in this study were obtained from interviews, literature studies, and documentation. The method used in this study is a qualitative descriptive method using data reduction analysis techniques, data presentation, and data inference. The result of this research is that the implementation of internal control of the accounting information system is in accordance with the provisions of the company. Starting from a clear segregation of duties, internal control environment, required supporting documents, accounting procedures, to payment terms. The obstacle experienced by the company was that there had been a discrepancy between the salaries received by outsourced employees and evidence of attendance records in the system caused by errors in the attendance application system.

A Technique for Constrained and Quantized Control of Nonlinear Systems Using Second-Order Cone Programming

Abstract This paper presents a novel technique for control of systems with bounded nonlinearity, convex state constraints, and control constraints. The technique is particularly useful for problems whose control constraints may be written as convex sets or the union of convex sets. The problem is reduced to finding bounding solutions associated with linear systems, and it is shown that this can be done with efficient second-order cone program (SOCP) solvers. The nonlinear control may then be interpolated from the bounding solutions. Three engineering problems are solved. These are the Van der Pol oscillator with bounded control and with quantized control, a pendulum driven by a DC motor with bounded voltage control, and a lane change maneuver with bounded rotational control acceleration. For each problem, the resulting second-order cone program solves in approximately 0.1 s or less. It is concluded that the technique provides an efficient means of solving certain control problems with control constraints.

Das Entsendegesetz: Suendenfall Oder Loesung Des Arbeitslosigkeitsproblems?

In March 1996, the Bundestag introduced a minimum wage law for the German construction business in order to protect native workers from low wage competition by posted workers from other EU countries. This Entsendegesetz (Posted Workers Act) was backed formally by the EU Posted Workers Directive of December 1996. This paper analyzes the economic and political background of the law as well as its impact on the German construction business according to economic theory and provisional empirical evidence from similar protective measures taken in the U.S. The effectiveness of the law is seriously limited by practical and conceptional problems, like resource constraints in control, shifts from legal to illegal employment, or compatibility with basic EU law and principles. Moreover, since the central problems of the German construction business are structural, one cannot expect much of a relief for the labor market of native workers. Since the Entsendegesetz seems therefore only justifiable in the short run in order to smoothen structural adjustment and to avoid political unrest and xenophobia, it should not be extended.

On a boundary control problem for a pseudo-parabolic equation

<abstract><p>Previously, boundary control problems for parabolic type equations were considered. A portion of the thin rod boundary has a temperature-controlled heater. Its mode of operation should be found so that the average temperature in some region reaches a certain value. In this article, we consider the boundary control problem for the pseudo-parabolic equation. The value of the solution with the control parameter is given in the boundary of the interval. Control constraints are given such that the average value of the solution in considered domain takes a given value. The auxiliary problem is solved by the method of separation of variables, and the problem under consideration is reduced to the Volterra integral equation. The existence theorem of admissible control is proved by the Laplace transform method.</p></abstract>

Strong stationarity for a highly nonsmooth optimization problem with control constraints

<p style='text-indent:20px;'>This paper deals with a control constrained optimization problem governed by a nonsmooth elliptic PDE in the presence of a non-differentiable objective. The nonsmooth non-linearity in the state equation is locally Lipschitz continuous and directionally differentiable, while one of the nonsmooth terms appearing in the objective is convex. Since these mappings are not necessarily Gâteaux-differentiable, the application of standard adjoint calculus is excluded. Based on their limited differentiability properties, we derive a strong stationary optimality system, i.e., an optimality system which is equivalent to the purely primal optimality condition saying that the directional derivative of the reduced objective in feasible directions is nonnegative.</p>

Constrained Control for a Class of TS Fuzzy Systems

This article investigates handling control constraints in the controller design phase for a class of Takagi–Sugeno (TS) fuzzy systems. Line integral Lyapunov function (LILF) is used to reduce the conservativeness compared to common quadratic Lyapunov function (CQLF) and fuzzy Lyapunov function (FLF). Two cases, namely unsaturating and saturating control, are studied. First, an inclusion condition is set to guarantee the behavior in a region where control constraints violation does not occur. For the second case, the saturation may take effect and is allowed. Thanks to the saturation function, convex writing to outrun the nonlinearity inconvenience. For both cases, stabilizing design procedures are proposed such that closed-loop stability and its linked performances are not affected. Furthermore, the proposed conditions enlarge the domain of stability as possible. Hence, comparisons are made with other approaches to show the effectiveness of our approach.

Virtual Element Discretization of Optimal Control Problem Governed by Brinkman Equations

In this paper, we discuss virtual element method (VEM) approximation of optimal control problem governed by Brinkman equations with control constraints. Based on the polynomial projections and variational discretization of the control variable, we build up the virtual element discrete scheme of the optimal control problem and derive the discrete first order optimality system. A priori error estimates for the state, adjoint state and control variables in L2 and H1 norm are derived. The theoretical findings are illustrated by the numerical experiments.

Inverse Optimal Control for Dynamic Systems with Inequality Constraints

Inverse optimal control (IOC) algorithms can be used to reveal underlying objectives. Existing algorithms commonly estimate the objectives by assuming that the cost function can be represented as a weighted sum of features, and use optimality criteria to estimate the weights. However, the existing literature rarely discusses the recovery of cost functions in the presence of state or control constraints, which often exist due to the limited ranges of actuators and sensors. In this work, an optimisation problem is formulated to find the best values of weights and Lagrange multipliers of constraints to satisfy the optimality conditions, given a segment of an optimal trajectory. The maximum and minimum observed state and control variables are hypothesised as potential box constraints and validated by the associated Lagrange multipliers. In addition, this paper also introduces a method to dynamically choose the window size of the observation, or identify that not enough information was provided for an accurate estimation. The proposed approach is validated using simulated results generated with a two link serial arm. The results show the proposed approach can recover the cost function when box constraints are active, and the Lagrange multiplier value can indicate when and which constraints are present.

Redesigning industrial composite production planning and control for energy awareness

The European Union's requirement for eliminating greenhouse gas emissions from all value chains by 2050 affects most of the manufacturing activities all over the continent. The industrial production of composite parts, favored for new designs throughout the aerospace industry, does not constitute an exemption. For securing global competitiveness, CFRP manufacturers need to adapt to increasing fluctuations in energy supply and prices, induced by a growing share of renewable energy sources. Besides employing more efficient production technologies, decoupling of labor and energy intensive tasks will play a key part. Thereby, energy intensive tasks can be performed while general energy supply is high and demand is low, leading to comparatively lower prices. The perspective of energy supply and price becomes a crucial part in the design of modern production planning and control systems, not only for smokestack businesses, but for small and medium load energy consumers as well. In this contribution, a mathematical representation for modeling industrial scale composite manufacturing is proposed. Beside manufacturing's fundamental production planning and control constraints, CRFP manufacturing's very own peculiarities are outlined and considered as well. It is shown how the virtual mathematical representation of a CFRP manufacturing environment can assist in its energy aware redesign and optimization.

NMPC-Based Cooperative Strategy to Lure Two Attackers Into Collision by Two Targets

This letter presents a cooperative target defense strategy using nonlinear model-predictive control (NMPC) framework for a two–target two–attacker (2T2A) problem. Each attacker needs to capture a designated target individually. The objective of the two targets is to cooperate such that they lure the two attackers into a collision. We assume that the targets do not have perfect knowledge of the attacker states, and hence they estimate the attacker states using EKF. The NMPC scheme computes closed-loop optimal control commands for the targets while satisfying state and control constraints. Theoretical analysis is carried out to determine escape regions that will lead to the targets’ survival or capture. Numerical simulations are carried out to evaluate the performance of the proposed NMPC-based strategy for different scenarios validating the theoretical results.