Quadratic Programming Algorithm Research Articles

Jamming-Based Covert Communication for Rate-Splitting Multiple Access

We investigate secure communications for a rate-splitting multiple access (RSMA) system. In particular, a jamming-based covert communication (CC) technique is used to effectively prevent a warden from detecting the data communications. As the jamming signals also degrades the data rate of the users, we formulate an optimization problem that maximizes the minimum data rate over all the users subject to the constraints on the security requirement and power budgets of the transmitter and optimize the data rate, transmit power, and jamming power. The max-min problem is non-convex, and we propose a fast exponential algorithm which can produce a global optimal solution to the problem. The simulation results show that our proposed algorithm outperforms the sequential quadratic programming algorithm, a powerful algorithmic tool for solving nonlinear optimization problems, in terms of min-rate and execution time (for a small number of users), given the same security requirement. Additionally, it is observed through simulation that RSMA can achieve up to three times better than non-orthogonal multiple access (NOMA) in terms of min-rate.

Hierarchical collision-free trajectory planning for autonomous vehicles based on improved artificial potential field method

To enable autonomous vehicles to generate smooth and collision-free trajectories and improve their driving performance on structured roads, this paper proposes a hierarchical trajectory planning algorithm based on an improved artificial potential field method. To improve the applicability of the algorithm to complex scenarios, the Frenet coordinate system was established to address these limitations. First, the safety distance model is applied to the risk assessment of the improved artificial potential field method. Then, the hierarchical solution is carried out, and the road solvable convex space and the rough path solution are solved by combining the artificial potential field method. On this basis, the potential field term and the smoothing term cost function are established, and the sequential quadratic programming (SQP) algorithm is used to solve the exact path that meets the requirements of safety and smoothness. Hierarchical planning shortens the solution time by quickly determining the bounds of the convex space. Finally, in the speed planning, in order to take into account the comfort and safety of the occupants, the speed curve is solved by considering the dynamic constraints of the vehicle. The obstacle avoidance effects of the algorithm on static and dynamic obstacles are tested in different simulation scenarios. The results of the simulation experiment show that the proposed algorithm can successfully achieve obstacle avoidance on complex structured roads.

IPRQP: a primal-dual interior-point relaxation algorithm for convex quadratic programming

IPRQP: a primal-dual interior-point relaxation algorithm for convex quadratic programming

Real-Time Nonlinear Control Allocation Framework for Vehicles with Highly Nonlinear Effectors Subject to Saturation

Hybrid Unmanned Aerial Vehicles UAV are vehicles capable of take-off and landing vertically like helicopters while maintaining the long-range efficiency of fixed-wing aircraft. Unfortunately, due to their wing area, these vehicles are sensitive to wind gusts when hovering. One way to increase the hovering wind-rejection capabilities of hybrid UAV is through the addition of extra actuators capable of directing the thrust of the rotors. Nevertheless, the ability to control UAVs with many actuators is strictly related to how well the Control Allocation problem is solved. Generally, to reduce the problem complexity, conventional (CA) methods make use of linearized control effectiveness in order to optimize the inputs that achieve a certain control objective. We show that this simplification can lead to oscillations if it is applied to thrust vectoring vehicles, with pronounced non-linear actuator effectiveness. When large control objectives are requested or actuators saturate, the linearized effectiveness based CA methods tend to compute a solution far away from the initial actuator state, invalidating the linearization. A potential solution could be to impose limits on the solution domain of the linearized CA algorithm. However, this solution only reduces the oscillations at the expense of a lag in the vehicle acceleration response. To overcome this limitation, we present a fully nonlinear CA method, which uses an Sequential Quadratic Programming (SQP) algorithm to solve the CA problem. The method is tested and implemented on a single board computer that computes the actuator solution in real time onboard a dual axis tilting rotor quad-plane. Flight test experiments confirm the problem of severe oscillations in the linearized effectiveness CA algorithms and show how the only algorithm able to optimally solve the CA problem is the presented Nonlinear method.

Temperature uniformity optimization with power-frequency coordinated variation in multi-source microwave based on sequential quadratic programming

Abstract The focus of this study is to regulate the variation in the input parameters of multiple microwave sources in a high-frequency multimode resonant heating system to achieve uniform heating. First, this study deeply expands the theoretical process of frequency change and proposes a frequency-shifting strategy with hot spot alternation to ensure that the temperature difference range of each hot spot does not continuously expand during the heating process. Then, a sequential quadratic programming algorithm is introduced to reconstruct the input power values to improve the heating efficiency according to the different microwave absorption efficiencies of the heated material at different frequencies. Finally, a numerical calculation model for multi-source microwave power-frequency cooperative heating is established based on the finite-element method, and the temperature uniformity index is effectively calculated. Numerical calculations show that the proposed method can improve the uniformity in single-material heating and multi-material heating cases by 56.8–94.3% and 44.4–76.6%, respectively, over that of fixed-frequency heating while achieving improved heating efficiency on the basis of frequency conversion.

Distributed nonlinear model predictive control for cobalt removal process in zinc hydrometallurgy considering error compensation modelling

AbstractTo address the strong nonlinearity, uncertainty, and mutual coupling in the cobalt removal process of zinc hydrometallurgy, an algorithm based on an improved genetic algorithm (GA) backpropagation (BP) neural network combined with distributed nonlinear model predictive control (NMPC) is proposed. This method was applied to improve the quality of the purification solution and reduce the consumption of zinc powder, overcoming the challenges faced by the current cobalt removal process. First, a synergistic continuously stirred tank reactor (SCSTR) model was constructed for the dynamic cobalt removal process. Second, aiming at the problem that a single SCSTR model has difficulty describing the process accurately, based on the highly nonlinear mapping ability of data‐driven models, a method that organically integrates the SCSTR model and an error compensation model based on the GA‐BP neural network was proposed (GA‐BP‐SCSTR) to provide a more accurate online prediction of the process indicators. Then, a distributed NMPC architecture was developed using the GA‐BP‐SCSTR model, control vector parameterization (CVP) technique, and sequential quadratic programming (SQP) algorithm to achieve the coordinated control of the cobalt removal process. Finally, simulation results of an actual site showed that the prediction accuracy of the GA‐BP‐SCSTR model was higher than those of other models. The proposed predictive control method can maintain the outlet cobalt ion concentrations at the set values while achieving accurate control of the zinc powder addition. This approach can provide guidance for on‐site production and eliminate the blindness of manual experience control.

Seismic Bearing Capacity Solution for Strip Footings in Unsaturated Soils with Modified Pseudo-Dynamic Approach

In engineering mathematics, the unsaturated nature of soil has a significant impact on the seismic bearing capacity solution. However, it has generally been neglected in the published literature to date. Based on the kinematic approach of limit analysis, the present study proposes a method for calculating the bearing capacity of shallow strip footings located in unsaturated soils, taking four common types of soils as examples. The modified pseudo-dynamic (MPD) approach is used to calculate the seismic forces varying with time and space, and the layerwise summation method is used to derive the power generated by the seismic forces. In the calculation of internal energy dissipation, this paper introduces the effective stress based on the suction stress to derive the cohesion expression at different depths. The analytical formula of bearing capacity is obtained by the principle of virtual work, and its value is optimized by the Sequential Quadratic Programming (SQP) algorithm. In order to verify the validity of the proposed method, the present results are compared with the solutions published so far and a good agreement is obtained. Finally, a parametric study is performed to investigate the influence of different types of parameters on the bearing capacity.

Design of a Highly Compatible Underwater Wireless Power Transfer Station for Seafloor Observation Equipment

Conventional cabled seafloor observatories (CSOs) power in-situ instruments via wet-mated or dry-mated direct electrical contact (DEC) connectors to achieve long-term and real-time observation. However, the DEC connectors have high risks of water leakage and short circuits in power feeding, especially under high water pressure. This paper proposes a highly compatible underwater station based on inductive wireless power transfer (IPT) technology to address the above reliability issue. A novel energy transmitter with runway-structure coils is applied to the proposed underwater station to cover a sufficient power feeding area so that various in-situ equipment can be powered with desirable misalignment tolerance. The magnetic field distribution is analyzed by both derivation and finite element analysis (FEA) methods, and the principal parameters of the transmitter are further optimized and compared with both the mixed-integer sequential quadratic programming (MISQP) algorithm and the evolutionary algorithm (EA) for better performance. The same results show a reliable optimization process. The WPT circuit characteristics are also investigated to power different loads and improve the power transmission efficiency. The output power decreases, and the transmission efficiency rises and then decreases as the load increases. In addition, receivers with higher mutual inductance have better transmission performance. A prototype of the underwater station has been tested both in air and in water, and the experimental results have proven it can power multiple seafloor observation instruments stably and achieve compatibility requirements. The maximum output power of the station prototype for testing is 100 W, and the maximum overall transmission efficiency is 61%.

A Large-Scale Identification Approach for Thermal Parameters of Multichips IGBT Modules Based on LLSO-SQP Algorithm

This paper proposes a large-scale optimization approach for identifying thermal parameters of multi-chip IGBT modules. State-space equation, in which the coefficient matrix comprises the thermal resistance and capacitance, is provided to represent the compact three-dimensional thermal network model. Then, the level-based learning swarm optimization (LLSO) algorithm is firstly utilized to identify large-scale thermal parameters. Additionally, to solve the inefficient convergence problem, the optimization results obtained from the LLSO is provided as the initial value of the sequential quadratic programming (SQP) algorithm to find the global optimal solution. Hence, the proposed LLSO-SQP algorithm can identify the large-scale thermal parameters efficiently and accurately. The training dataset for the algorithm is derived from the transient temperature response of a finite element model (FEM) of the IGBT module under power step excitation. Since only one-time simulation is in-demand, this approach needs less computational effort than others. The identified thermal network model is utilized to estimate junction temperature profiles taking a two-level inverter as a case study. In comparison to that of the experiment and FEM, the results validate the feasibility and accuracy of the junction temperature estimation method based on the compact three-dimensional thermal network model.

Upper Limit Analysis for the Ultimate Bearing Capacity of a Multilayer Slope with Horizontal Stratification

The ultimate bearing capacity is one of the most important mechanical parameters in the mining and geotechnical fields and has a vital influence on the stability of slopes and the safety of constructions and structures. The determination of the ultimate bearing capacity has become a controversial issue in slope engineering. This study proposes a way to obtain the ultimate bearing capacity of a multilayer slope with horizontal stratification. According to limit analysis theory, three typical multiblock sliding failure modes for a multilayer slope with horizontal stratification are established. The upper limit solution of the ultimate bearing capacity of a multilayer slope, considering the influence of horizontal stratification, is derived. The optimization procedure is carried out to obtain the optimal value of the ultimate bearing capacity by using the sequential quadratic programming algorithm. The accuracy of the present method is proven based on a comparison of calculation results using other analytical methods and the numerical simulation method. The influence analysis of the parameters, involving the slope angle, soil strength, and distance of the foundation from the slope shoulder, is performed. The results indicate that the error between the calculated results of the present method and those of numerical simulation, as well as the error between the calculated results of the present method and those of other existing methods, is less than 10%. The ultimate bearing capacity linearly decreases with increasing slope angle, while it linearly increases with increasing distance of the foundation from the slope shoulder. The analytical method provides a method to analyze the ultimate bearing capacity of a multilayer slope, which effectively solves the problem of heterogeneity of soil layers observed in the natural slope. The results from this study can be used as a guide to estimate the stability of the slope and design of the foundation on a slope considering the load action near the slope.

Optimal energy management of hybrid electric vehicles considering pollutant emissions during transient operations

The reduction of fossil-fuel vehicles’ pollutant emissions has been improved with exhaust aftertreatment systems and, more recently, with electric hybridization. Indeed, hybrid electric vehicles (HEVs) can shift engine operating points toward low fuel consumption and pollutant emissions regions. Also, engine transient operations have already been shown to impact pollutant emissions negatively. As a result, the electric motor can be additionally used to reduce the engine transient operations. The engine optimal control, i.e., minimizing fuel consumption and pollutant emissions, must consequently consider the engine transient dynamics. Therefore, this paper introduces a parametric approximation of the optimal engine control variable based on a weighted sum of smooth basis functions to directly consider transient engine dynamics and guarantee smooth engine operations. Two approaches are presented and compared to find the parametric approximation of the engine control, minimizing fuel consumption and pollutant emissions. The proposed direct and indirect approaches rely on polynomial approximations of the vehicle components’ models, leading to efficient quadratic programming algorithms. In this paper, a high-fidelity simulation platform of a heavy-duty HEV is employed to calibrate the controller model and then compare it to classical control methods. The results from the proposed minimization approaches are shown to be close to the dynamic programming optimality, yet being much faster alternatives.

Effective algorithms for separable nonconvex quadratic programming with one quadratic and box constraints

Effective algorithms for separable nonconvex quadratic programming with one quadratic and box constraints

Coordinated control algorithm of lateral stability of hybrid electric commercial vehicle

Focusing on the problem of mutual interference between the vehicle controller and the lateral stability control system in the stability control of hybrid electric vehicles (HEV), this study proposes a coordinated control algorithm for hybrid power systems and lateral stability, which can meet the real-time control requirements of the vehicle stability. First, the overall architecture of coordinated control between the hybrid power system and the vehicle lateral stability control system is designed. The judgment logic of lateral stability control system intervention and exit is determined on this basis. Second, in the upper layer of the control algorithm, the model predictive control algorithm is used to calculate the additional yaw moment required to maintain stability control under lateral instability of the vehicle with the vehicle two-degrees-of-freedom model as a reference. The motor output torque control and braking force distribution are then carried out in the lower layer of the control algorithm. The target braking wheel is determined according to the oversteering and understeering state of the hybrid vehicle. The additional yaw torque distribution control is formulated based on the quadratic programing algorithm. The strategy is to perform dynamic compensation for unstable or critically unstable vehicles to solve the optimal control problem under multiple constraints. Finally, through Matlab/Simulink and Trucksim co-simulation and hardware-in-the-loop test, the validity and real-time performance of the control algorithm proposed are verified to solve the interference problem in the lateral stability control process of HEVs and further improve the stability of vehicle rideability and active safety.

Colocated MIMO Radar Waveform-Array Joint Optimization for Sparse Array.

Colocated multiple-input multiple-output (MIMO) radar can transmit a group of distinct waveforms via its colocated transmit antennas and the waveform diversity leads to several advantages in contrast to conventional phased-array radar. The performance depends highly on the degrees available, and element spacing can be deemed as another source of degrees of freedom. In this paper, we study the joint waveform and element spacing optimization problem. A joint waveform and array optimization criterion is proposed to match the transmit beampattern, the suppression range, and the angular sidelobes, under the constraints of minimal element spacing and total array aperture. Meanwhile, the effect of receive beamforming on suppressing mutual correlation between returns from different spatial directions is also incorporated into the optimization criterion. The optimization problem is solved by the sequential quadratic programming algorithm. Numerical results indicate that with more degrees of freedom from array spacings, colocated MIMO radar achieves a better transmit beampattern matching performance and a lower sidelobe level, compared with a fixed half-wavelength spaced array, but the benefits from additional degrees of freedom from array spacing optimization have a limit.

Optimization and experimental validation of an extractive distillation system with solvents mixture as separating agent

This work aims to develop an economic optimization and experimental validation of ethanol separation by extractive distillation using an ethylene glycol - glycerol mixture as separating agent with and without thermal integration. An anhydrous ethanol production plant with a capacity of 300 000 L/day was simulated through a rate-based approach in Aspen Plus V10®. The optimization problem was solved, firstly, through a rate-based simulation, using a Sequential Quadratic Programming (SQP) algorithm available in Aspen Plus® and secondly, through a stochastic algorithm (genetic) available in Matlab R2019a®. US$ 0.1034 was the optimized TAC/kg of anhydrous ethanol, this parameter reduced by US$ 0.0004 to US$ 0.0006 when thermal integration was included and differed by less than US$ 0.0038 between both methodologies, where the lowest value was found using the stochastic algorithm. Experimental validation was conducted on a pilot scale extractive distillation to verify the results from the process optimization. The use of the 60–40 mol% ethylene glycol - glycerol mixture showed a 40.1% reduction in the total energy consumption of the process compared to the case where only ethylene glycol is used as entrainer. This allowed to confirm the synergistic effect of the solvents mixture and the possibility of employing them.

Approaching and pointing tracking control for tumbling target under motion constraints

On-orbit servicing for the failed spacecraft involves two necessary stages: on-orbit observing and approaching. For these two stages, a novel approaching and pointing tracking control scheme under motion constraints and input saturation is proposed in this paper. For the security of approaching, a relative position guidance law for specific surface hovering is proposed. Then, a model predictive controller based on quadratic programming algorithm is designed. For the continuous observation task, the optimal maneuver angular velocity under the motion constraint is planned through model predictive control. In order to reduce the dimensionality of the optimization problem to reduce the computational effort, only the attitude kinematics equation is used as the process model. Thereafter, the adaptive anti-saturation attitude controller is designed to track the optimal maneuver angular velocity. The anti-saturation auxiliary system and the adaptive update law are used to cope with the control torque saturation and the integrated uncertainties, respectively. Finally, the stability of the closed-loop system is demonstrated by Lyapunov method. Simulation results demonstrate that the proposed control scheme accomplishes the approaching and pointing tracking task, and finally service spacecraft maintains hovering above the tumbling target and observing its specific surface.

A novel power allocation strategy considering multi-objective comprehensive optimization for hybrid electric vehicles

This research introduces a multi-objective comprehensive optimization power distribution strategy (PDS), specifically designed for a hybrid electric vehicle (HEV) that utilizes a combination of fuel cell (FC) and battery technology. As hybrid power systems face challenges such as high operating costs and limited stack durability, which hinder the widespread adoption of FCs, this study proposes a strategy that focuses on optimizing both the operational cost and service life of the stack. To enhance the longevity of the stack, this research investigates the coupling relationship between the operational lifespan of the fuel cell and its output power. In addition, this study formulates a mathematical formula to depict the relationship between the FC’s output power and the loss of its lifespan. Furthermore, the lifetime loss of the energy storage system, as well as limitations on the fluctuation of its state of charge (SOC), are also taken into account. Additionally, this study aims to enhance fuel efficiency by optimizing the hydrogen consumption of the system. Based on the above considerations, a cost function has been formulated for the purpose of multi-objective optimization. To expedite the convergence speed when solving the cost function, a sequential quadratic programming (SQP) algorithm has been employed. Based on the hardware-in-the-loop (HIL) bench, the effectiveness of the presented multi-objective comprehensive optimization PDS is validated. The results demonstrate that the proposed strategy has advantages over other benchmark PDSs in many aspects, including operating cost, hydrogen consumption, battery SOC fluctuation range, and power sources operating life loss.

Research on optimization of structural parameters for airfoil fin PCHE based on machine learning

The thermal hydraulic performance of the printed circuit heat exchanger (PCHE) is critical to the efficiency of the supercritical carbon dioxide (S-CO2) Brayton cycle. Therefore, in this paper, four parameters (maximum thickness, maximum thickness location, transverse pitch and staggered pitch of the airfoil fin) were selected to carry out optimization research on the airfoil PCHE combined with CFD simulations, machine learning and optimization algorithms. Firstly, the simulation of the airfoil PCHE with different channel configurations was carried out to analyze the effects of four parameters on its performance. Then, the thermal hydraulic parameters were trained and predicted using the ANN model, from which correlations integrating the structural and arrangement parameters of the airfoil PCHE were obtained. Finally, the sequential quadratic programming (SQP) algorithm and non-dominated sorting genetic algorithm II (NSGA-II) were used to carry out the optimization studies of PCHE, respectively. The results showed that increasing the value of the maximum thickness location can improve the comprehensive performance of PCHE. After optimization, the performance coefficient ηh was improved by about 6.2 % compared to the basic structure when Re = 45000.

Energy-efficient predictive control for trams incorporating disjunctive time constraints from traffic lights

Tram operations are often blocked by traffic lights, leading to frequent decelerations and re-accelerations that increase operational energy consumption. This paper focuses on tram energy-efficient control problem incorporating time constraints from traffic lights that have multiple feasible green time windows (GTWs). We formulate the problem as a mixed-integer nonlinear program (MINLP), where binary variables are assigned to model disjunctive time constraints of the GTWs. To address computational challenge of solving the MINLP, we reformulate it as a tractable nonlinear program (NLP). Specifically, an equivalent NLP is first presented by replacing the integrality constraint with nonlinear constraints, and then the nonlinear constraints are relaxed and penalized into cost functions. To recover a solution of the MINLP, we propose a computationally efficient sequential quadratic programming algorithm in a shrinking horizon model predictive control framework, which updates the penalty parameter and quadratic programming subproblems in parallel. The solution obtained from the subproblem is feasible in each iteration, and convergence of the feasibility iterations can be enforced by the updated penalty. The performance of the proposed approach is investigated on different scenarios using real-life tram data. Results show that the method is able to generate energy-efficient driving trajectories in a dynamic environment, while crossing traffic lights in effective GTWs without unnecessary decelerations and re-accelerations.

Optimal Noise-Boosted Estimator Design Via Adaptive Stochastic Resonance

In signal estimation, an optimal estimator is frequently unachievable because its closed form may not be analytically tractable or is too complex to implement. Alternatively, one can turn to suboptimal yet easily implementable estimators for practical signal estimation tasks. In this paper, an optimal noise-boosted estimator is designed and the adaptive stochastic resonance method is implemented to simultaneously exploit the beneficial role of the injected noise as well as the learning ability of the estimator parameter. Aiming to effectively improve the estimation performance, we use the kernel function method to find an approximate solution for the probability density function (PDF) of the optimal injected noise. During this process, the noise PDF and the estimator parameter establish a finite-dimensional non-convex optimization space for maximizing the estimation performance, which is adaptively searched by the sequential quadratic programming (SQP) algorithm at each iteration. Two representative estimation problems are explored. The obtained results demonstrate that this adaptive stochastic resonance method can improve the performance of the suboptimal estimators and bring it very close to that of the optimal estimator.