Search

A control design framework for the coupled stability problem in robotics is presented. The proposed framework fundamentally generalizes the conventional passivity-based approaches to the coupled stability problem. In particular, it allows for stabilization of not necessarily passive robot-environment interaction where both the manipulator and the environment are dissipative systems with quadratic supply rates. In contrast with existing results, the proposed framework can be used in combination with an arbitrary robot’s tracking control algorithm, and its stabilizing action when in contact with environment does not affect the robot’s trajectory tracking performance in free space. The framework is based on the recently developed nonplanar conic systems formalism and generalized scattering-based stabilization methods. A detailed design example is presented which illustrates the capabilities of the proposed method.

Focusing on improving the operation efficiency and riding comfort of metro rail lines in the peak hours, this article investigates the real-time train regulation and passenger load control problem with respect to frequent disturbances. To better illustrate the relationship between the train timetable and the on-board passengers, the variations of the departure time and the passenger load are elaborated in the form of a state-space model. Based on the Lyapunov stability theory, the problem of minimizing an upper bound on the quadratic performance function is transformed to a dynamic optimization problem with a set of linear matrix inequalities (LMIs), and a predictive control strategy is designed to guarantee the actual train schedule and number of in-vehicle passengers track the nominal timetable and the expected passenger load with a given disturbance attenuation level. With the objective to reduce the computational workloads and cut down the utilization of wireless transmitting resources, an event-triggered strategy is developed to implement the proposed stabilizing feedback controller only when the measurement error exceeds certain threshold, which has better adaptability to the application in large-scale metro networks. Some numerical examples based on the Beijing Yizhuang Metrol Line are provided for illustration of the effectiveness of the proposed scheme.

The ever-growing higher penetration of distributed energy resources (DERs) in low-voltage (LV) distribution systems brings both opportunities and challenges to voltage support and regulation. This paper proposes a deep reinforcement learning (DRL)-based scheduling scheme of energy storage systems (ESSs) to mitigate system voltage deviations in unbalanced LV distribution networks. The ESS-based voltage regulation problem is formulated as a multi-stage quadratic stochastic program, with the objective of minimizing the expected total daily voltage regulation cost while satisfying operational constraints. While existing voltage regulation methods are mostly focused on one-time-step control, this paper explores a day-horizon system-wide voltage regulation problem. In other words, the size of action and state spaces are extremely high-dimensional and need to be delicately handled. Furthermore, in order to overcome the difficulty of modeling uncertainties and develop a real-time solution, a learn-to-schedule feedback control framework is proposed by adapting the problem to a model-free DRL setting. The proposed algorithm is tested on a customized 6-bus system and a modified IEEE 34-bus system. Simulation results validate the effectiveness and near-optimality of voltage regulation by ESS in comparison with a deterministic quadratic program solution.

This paper explores the design of an anti-saturation adaptive finite-time control strategy with the neural network (NN) technique for the space circumnavigation mission. Before executing the controller design, the analytical solutions of the desired angular velocity and its derivative of the active spacecraft are calculated. Since there are uncertain saturation constraints on control forces and moments in the actual propulsion system, an auxiliary system compensated by an adaptive NN is adopted. The modified auxiliary system no longer needs the precise output values of the actuators. Besides, the hyperbolic tangent function is introduced to design the weight update law for the NN compensator, so that the derivative of the weight estimator will not be amplified by the quadratic of states when the system states are large. It is proved that tracking errors of the system states can converge to a residual set of the origin in finite time. Simulation results show that the maximum amplitudes of the control signals are greatly reduced compared to the classical non-singular terminal sliding-mode control scheme, and that the neural-based compensator can significantly weaken the overshoot and chattering.

The results of studies of the effectiveness of the optimal radar altimeter when tracking targets moving along trajectories of varying complexity are presented. The tracking system of the altimeter is obtained in the first part of this article by the local minimization of the quadratic-biquadratic quality functional.

This paper presents a secure chaotic communication scheme using observer-based synchronization of chaotic systems. A class of chaotic systems satisfying incremental quadratic constraints are considered as the transmitter. The original message is encrypted in the noise-like output of the system. A circle criterion-based observer is designed for the system as the receiver by using the linear matrix inequalities technique. The message is recovered at the receiver end from the estimation error of the observer. To design the observer, a general algorithm to calculate incremental multiplier matrices for nonlinearities in chaotic systems is presented. Moreover, the proposed observer-based chaotic secure communication scheme is feasible for many common chaotic systems with ellipsoidal bounds. The simulation results show the good performance of the proposed communication scheme through an example of image transmission.

In this paper, we investigate the problem of observer-based time-varying leader-following formation tracking for multi-agent systems with the one-sided Lipschitz and quadratic inner-boundedness nonlinear dynamics. An idea of the observer-based protocol with an edge-based adaptive law is designed for nonlinear systems, which enable agents to achieve a desired formation tracking when it cannot obtain the full state of the nonlinear system. In contrast to the previous nonlinear formation systems, the advantage of the developed method is that it is less conservative and more general for the nonlinear system with large Lipschitz constants. Besides, under the proposed adaptive law, the design of the protocol does not rely on the known communication topology. Finally, the simulation results for one leader and six followers are proposed to show the feasibility and effectiveness of the proposed method in the nonlinear time-varying formation system.

Photovoltaic (PV) system is an attractive option for the energy sector nowadays due to its renewable nature. However, because of the unpredictable nature of weather, it is difficult to determine the generation of the PV system beforehand. Thus, forecasting is essential for the determination of Return of Investment (ROI) of a newly installed PV system. This paper proposes the application of Response Surface Methodology (RSM) to forecast the output power of the 6kW thin-film PV System. Three environmental elements are used; irradiance, module temperature, and ambient temperature. For that, MATLAB RStool which is consisting of four models; multiple linear regression (MLR), interactions, pure quadratic, and full quadratic was used. The 5 minutes sampling size data weather station from the year 2014 of the 3-phase three environmental elements and output power of 6kW thin-film was recorded and used. Whereas, yearly 2015 data of the aforementioned elements were used for validation. Forecasting performance measures such as the determination of coefficient (R2) method and root mean square error (RMSE) approach are presented. The results indicated that a full quadratic model provides the best forecasting model with a resulting R2 value of 0.9981 and gives the least amount of RMSE which is 18.74.

We recently developed the quadratic integration- exploited model predictive current control (QIMPCC) and the binary capacitor voltage control-based modular multilevel converter (BCVC-MMC) with a new hybrid arm design to effectively enhance the DC-AC power-conversion quality-and- efficiency and the closed-loop control performance of MPCC- based multilevel converters in low nominal DC-voltage applications. Due to the technical limits of its predicted cost- function optimization method utilizing a finite three-phase switching combination set, QIMPCC cannot be directly applied to BCVC-MMC; moreover, there is room for improved closed-loop control performance. To address these drawbacks, this paper proposes the voltage-control reference estimation (VCRE)-based QIMPCC exploiting the QI method’s superior accuracy. The proposed algorithm substantially improves the closed-loop control performance of this class of converters. Finally, simulation results demonstrate and quantify the claimed technical advantages.

This paper presents a novel quadratic step-up dc-dc converter with a transistor and three diodes. The converter is suitable for applications where a low step-up difference between the input and output voltage is desired. Comparing the proposed topology to other step-up converters it comes out that the semiconductor current stresses are lower and also the voltage stresses for all the three diodes. For the proposed quadratic converter the operation principle and steady-state analysis are presented in detail, for continuous conduction mode operation. Design equations are provided and the converter is simulated in order to prove the feasibility of the proposed quadratic step-up topology. Finally, a prototype was implemented and measurements were performed to validate the feasibility and theoretical analysis.

The complicated and constrained reconfiguration optimisation for unmanned aerial vehicles (UAVs) is a challenge, particularly when multi-mission requirements are taken into account. In this study, we evaluate the use of the adaptive differential evolution-based centralised receding horizon control approach to achieve the formation reconfiguration along a given formation group trajectory for multiple unmanned aerial vehicles in a three-dimensional (3D) environment. A rolling optimisation approach which combines the receding horizon control method with the adaptive differential evolution algorithm is proposed, where the receding horizon control method divides the global control problem into a series of local optimisations and each local optimisation problem is solved by an adaptive differential evolution algorithm. Furthermore, a novel quadratic reconfiguration cost function with the topology information of UAVs is presented, and the asymptotic convergence of the rolling optimisation is analysed. Finally, simulation examples are provided to illustrate the validity of the proposed control structure.

Calculation of the static voltage stability margin (SVSM) of power system with high wind-power penetration must consider the uncertain fluctuation of power output from wind farms (WFs). On the basis of a continuous power flow (CPF) and improved affine interval (IAI) algorithm that considers quadratic terms, a new method to calculate the SVSM interval of a power system that considers the uncertain fluctuation intervals of WF output is proposed. Considering the high penetration of wind power, the power variation of the active load, and wind-power fluctuation are assigned by all conventional generators, except the swing generator in the CPF calculation model. In the proposed IAI algorithm, the non-linear second-order sensitivities are considered to obtain a more accurate SVSM interval. With the correlation of different WF output intervals described by relative angles, as well as the independent intervals obtained by decorrelation of the correlated WF output intervals, the SVSM interval is calculated by the CPF and IAI algorithms. Take the IEEE 39-bus system and an actual 964-bus provincial power grid as examples, and compared with Monte Carlo method, the results show that the SVSM interval calculated by the proposed method is more accurate, and the computation time is significantly reduced.

It is known that the dynamic properties of industrial objects in control channels usually change significantly with a change in their mode of operation. Therefore, the indicators of quality of regulation with constant settings of automatic regulators also change, and, as a rule, they deteriorate during the operation of the units. In this connection, the problem arises of periodically adjusting the settings of regulators in the circuits of operating automatic control systems (ATS). This problem can be solved only by the actual dynamic parameters of the object being operated, therefore, the work considers the task of estimating these parameters, and according to the actual data of the operating closed system. In this case, a step perturbation is used either on the instructions or on the part of the regulator (on the load), the experienced reaction of the system to disturbances of any kind along the indicated channels can also be used. The control object is described by a second-order differential equation with delay. The parametric identification criterion is quadratic, either modular or minimax. The identification task is solved with the help of a program that implements the method of coordinate descent, the resulting one-dimensional minimization problems are solved with the help of a subroutine using the golden section method. For the numerical integration of the object equation, the Runge-Kutta method was used with an error proportional to the fifth power of the time step. In this paper, we tested the developed programs for parametric identification based on the experimental transient characteristics of ATS pressure in a metallurgical furnace. The numerical-analytical procedures for adjusting the regulators in the SAR by the found parameters of the control object are indicated.

The application of modern control methods to automated driving of following vehicles on a highway can make the advanced transportation systems much more safe and efficient. This paper focuses on a computational approach to optimal control and stability analysis of the adjacent vehicles following in a single traffic lane of a highway in order to ensure the functional safety and assistance of automated driving systems. These vehicles are entirely automated as they have the ability to follow a line path at a high speed and reduce the distance that separates them while adjusting their responses automatically without intervention from humans (i.e. drivers). To meet such requirements for driving safety, this paper explores a new approach to string stability of following vehicles that travel in a straight line of a highway, using optimal control and Lyapunov stability theories. Consequently, sufficient conditions for string stability of a vehicle traffic using continuous-time Lyapunov function and matrix Riccati equation are presented. The search for a quadratic Lyapunov function is then formulated as a convex optimisation problem in terms of a linear invariant system towards a quadratic optimal criterion. The analysis of these methods for minimising the performance criterion is also given. Finally, the paper ends with a conclusion and perspective for future work.

This paper deals with the development of a mixed active-passive microvibration mitigation solution capable of attenuating the transmitted vibrations generated by reaction wheels to a satellite structure. A dedicated simulation environment, provided by the European Space Agency and Airbus Defence and Space industries, serves as a support for testing the proposed solution at satellite level. This paper covers modeling, control system design, and worst case analysis for a typical satellite observation mission that requires high pointing stability. Combined with a novel disturbance model for the reaction wheel perturbations, the pointing performance and stability requirements are reformulated as bounds on the worst-case L2 system gains. Subsequently, the active microvibration controller is tuned to manage the conflicting design goals and optimize different trade-offs between robustness and performance. Finally, robust stability margins and worst-case performance bounds with respect to various system uncertainties, time-varying reaction wheel spin rates, actuator saturation, and time delays are obtained using the structured singular value, integral quadratic constraints, and time-domain nonlinear simulations.

This chapter applies the concepts presented in the previous ones to design a MPC for generating orbital rendezvous trajectories on board the chaser spacecraft, considering elliptical target orbits. We first present a naive formulation with a state quadratic Lagrangian cost and the receding-horizon strategy and demonstrate through simulation that it is not suitable for this application. This is iteratively improved until we reach two fuel-optimal formulations, known as Finite-Horizon MPC and Variable-Horizon MPCs, where the former has fixed final time and is formulated as a LP, and the latter has free final time and is formulated as a MILP. The passive safety constraint is approached, which makes the problem non-convex, and a new method for efficiently solving it via sequential linear programming is presented. We also cover the topic of robustness to disturbances and perturbations and discuss different techniques for granting robustness, including two contributions in the present work. Finally, we perform several simulations demonstrating the above methods, including in a high-fidelity industrial simulator, with emphasis on illustrating that they may be feasibly implemented in real-time and on-board spacecraft.

In recent years, penetration of distributed generation (DG) has increased rapidly in the power distribution network, which demands novel and optimal power flow solutions for improving the power system reliability, resiliency and better-operating conditions. In this paper, we propose a branch flow-based SOCP model for the optimal power flow of radial networks. In the proposed model, the voltage and current angles are relaxed and a quadratic equation is converted to a conic form for convexification. Though the angles are relaxed, a convex equation is also proposed to recover the angle which makes the solution efficient. In the simulation, we have shown that for radial networks the relaxation is exact when there are no upper bounds on loads. Also, theoretical analysis for the branch flow model is deduced for the convexification of the model. Finally, the proposed model was tested on two radial networks (modified 32 bus network and IEEE 123 bus network) and the simulation results are verified with the OpenDSS and MatPower software packages.

The work on this paper aims to improve the efficiency of a photovoltaic energy system. Reducing the losses in the adaptation stage allows to improve this efficiency. For that, in this paper we will work on the adaptation stage between the solar panels and the loads to reduce the losses. As a first step, we will present a comparison between a conventional DC-DC boost converter usually used in solar system and a quadratic boost with a single switch. This comparison aims to show the advantage of using quadratic converters with high gain in solar systems. The results show that the quadratic converter has a higher efficiency compared to the conventional one, it allows also to reach higher values of gain for a practically acceptable values of duty cycle. This comparison justifies the choice of the quadratic converter. Then, a new dual-mode variable step-size maximum power point tracking (MPPT) algorithm is proposed. The proposed algorithm is used to control the quadratic converter. The proposed algorithm is based on the perturb and observe (P&O) algorithm. The P&O is one of the most known and used MPPT methods, its drawbacks appear during fast solar irradiance variation. The proposed control method is a double mode algorithm. The first mode is active when the operating point is near of the MPP. This mode allows to stabilize the operating point to reduce the steady-state oscillations that occur using the P&O. The second mode is activated when the operating point moves away from the MPP. This mode is a P&O with a large step size that allows tracking the MPP quickly which also reduces the losses. The proposed algorithm is tested through simulations using Simulink/MATLAB. Simulations results prove the efficiency of the proposed algorithm and its advantage compared to the P&O.

Linear quadratic optimal control(LQOC) is a term to combine the control of optimal problems that including a linear of system with cost functional and input-state-output that is a form of quadratic of the input and state. The goal for this work is to given class of input functions by minimize this cost functional and to improve the stability for puma robotics. Puma Robotics has a complex movement with 6-degree of freedom and the challenging to assurance the stability with each of link of robotics to move without any mistake or issue. This work shows the stability of movement for Puma robotics with LQOC with MATLAB codes.

We present a novel method of optimal robust control through quadratic programs that offers tracking stability while subject to input and state-based constraints as well as safety-critical constraints for nonlinear dynamical robotic systems in the presence of model uncertainty. The proposed method formulates robust control Lyapunov and barrier functions to provide guarantees of stability and safety in the presence of model uncertainty. We evaluate our proposed control design on dynamic walking of a five-link planar bipedal robot subject to contact force constraints as well as safety-critical precise foot placements on stepping stones, all while subject to model uncertainty. We conduct preliminary experimental validation of the proposed controller on a rectilinear spring-cart system under different types of model uncertainty and perturbations.

There has been an increasing interest in on-orbit autonomous servicing and repair of satellites as well as controlled active debris removal (ADR) in the space industry recently. One of the most challenging tasks for servicing/repair as well as for ADR is the rendezvous and docking with a non-cooperative tumbling resident space object (RSO). This paper presents a propellant optimal maneuver profile for a servicing spacecraft to perform proximity operations and eventually dock with a non-cooperative target. The strategy is to find an optimal trajectory which will guide the servicing spacecraft to approach the tumbling satellite such that the two vehicles will eventually have no relative motion. Therefore, a subsequent docking or capture operation can be safely performed. The research described here elaborates on the previous work that studied the minimum-control-effort for a 3-D rendezvous to a tumbling object considering a full six-degree-of-freedom model of both chaser and target. The current work expands the scope by adding a new set of linearized equations of motion that capture not only the effect of the J 2 geopotential disturbance force but also quadratic drag force. Typically, Hill's linearized equations of relative motion have been used for this analysis, but they fail to capture the effect of J 2 disturbance force and drag on the chaser satellite. It appears that there is a need for a set of linearized equations that are as easy and useful as Hills equations, but at the same time capture the effect of the J 2 disturbance and drag force acting on the spacecrafts. The current paper presents a set of linearized, constant coefficient differential equations that capture the J 2 and quadratic drag perturbation. The importance of these perturbations is studied by doing trajectory propagation. Finally, it is shown for a control problem, the effect on the control-solution due to the formulated high fidelity dynamical constraints with respect to the simplified Hill's equations.

In this paper, a new active power loss allocation (LA) scheme is developed by eliminating the influence of cross-term mathematically from loss equation for allocating losses to the network participants with/without distributed generators (DGs). The unique bus identification scheme introduced performs load flow easily and efficiently by considering DGs as negative constant power loads. This method assigns losses to the consumers/DG units with due consideration to their power consumptions/injections and physical locations in the radial distribution network (RDN). The effectiveness of the proposed LA scheme has been investigated against the quadratic scheme of LA using a 33-bus RDN with/without DGs.

This paper proposes a new two-stage DC-DC-boosted inverter circuit. The new class of inverter topology is formed by cascading an existing fifth-order quadratic boost converter with a single-phase H-bridge inverter. This quadratic boost inverter is capable of producing a high peak AC voltage, with a high conversion ratio at high efficiency. In this research work, a single switch with less duty ratio is used in the DC-DC converter stage to obtain high boost factor, and maximum modulation index is used to get high AC voltage gain. The two-stage DC-AC power conversion system is simulated in MATLAB/Simulink environment, and it is validated with the experimental results.

In this paper, the problem of sub-optimal output tracking control of singularly perturbed systems is considered. The necessary and sufficient condition for the existence of sub-optimal output tracking controller is given in terms of matrix inequalities, such that the tracking system achieves the asymptotic tracking and the minimum value of quadratic performance index can be obtained. Furthermore, the controller design method is presented in the form of LMI for the case that the controller has an approximate solution of the generalized Riccati equation. Finally, one numerical example is given to illustrate the correctness and feasibility of the proposed results.

This paper focuses on the robust stability analysis of linear systems with time varying delays. The second derivative of the state is used to construct a novel Lyapunov-Krasovskii functional (LKF). Then, both the integrals and derivatives of the states along with the delayed states could be taken into consideration in handling of the LKF. We introduce these augmented variables and establish correlations between them. Moreover, our approach makes it available that quadratic convex combination can be applied not only to the time delay, but also to the derivative of it. Robust stability criteria are presented, and numerical examples illustrate that the methods are less conservative.

In this paper, the three-axis attitude stabilization of the axisymmetric spacecraft with bounded inputs is studied. By constructing some novel state transformations, saturated linear state feedback controllers are constructed for the considered attitude control system. By constructing suitable quadratic plus integral Lyapunov functions, globally asymptotic stability of the closed-loop systems is proved if the feedback gain parameters satisfy some explicit conditions. By solving some min–max optimization problems, a global optimal feedback gain for the underactuated attitude stabilization system is proposed such that the convergence rate of the linearized closed-loop system is maximized. Numerical simulations show the effectiveness of the proposed approaches.

The centralized longitudinal control problem for Cooperative Adaptive Cruise Control (CACC) systems is discussed in this paper, in which the imperfect wireless communication surroundings and actuator dynamics are taken into consideration. From the large-scale system standpoint, the centralized longitudinal control problem for platoon vehicles equipped with CACC functionality is formulated as minimizing a quadratic performance index under the constrains of a large-scale discrete-time system with actuator delay and system output delay, in which the leader vehicle is set as the control center. After that, benefiting from a designed delay-free transformed vector, a delay-free two-point-boundary-value problem is derived from the equivalent reconstruction forms for original system delay model and performance index. Thus the centralized longitudinal controller is obtained by solving a Riccati equation. Finally, simulation results demonstrate that the ego vehicle can reasonable response the accelerating or decelerating behaviors of the preceding vehicles under the proposed controller, thereby the desired CACC control performance is satisfied, and the wireless communication delay and actuator delay are compensated effectively.

This paper reports a comparative experimental evaluation of one model-free proportional derivative (PD) three degree-of-freedom (DOF) controller and two model-based three-DOF controllers designed to enable low-speed, neutrally buoyant, and fully actuated underwater vehicles to perform trajectory tracking in the X, Y, and heading DOFs. We show the model-free PD controller provides locally asymptotically stable set-point regulation. We show the model-based controllers provide locally asymptotically stable trajectory tracking. The reported controllers were experimentally evaluated on the Johns Hopkins University remotely operated vehicle. We report the model-based controller’s mean absolute position and velocity tracking error is significantly smaller than the model-free PD controller for coupled maneuvers. We report the fixed, model-based controllers performed best using a parameter model including fully parameterized quadratic drag model parameters, and that the choice of parameter model has a significant effect on the performance of the model-based controllers during disparate experimental trials.

In this contribution, an improved Extended Kalman Filter (EKF), named the Total Extended Kalman Filter (TEKF) is proposed for integrated navigation. It can consider the neglected random observed quantities which may appear in a dynamic model. In particular, this paper will consider the case of vision-based navigation. This algorithm is equipped with quadratic constraints and makes use of condition equations. The paper will show that the refined data from different sensors including a Global Positioning System (GPS) receiver, an Inertial Navigation System (INS) and remote sensors can be fused into a Constrained Total Extended Kalman Filter (CTEKF) algorithm. The CTEKF algorithm is applied to a case study in the Guilan province in the north of Iran. The results show the efficiency of the proposed algorithm.

In this paper, we treat the problem of both state estimation and fault detection of nonlinear systems. Those systems could be described by a poly-quadratic Linear Parameter-Varying (LPV) form. A new adaptive observer is developed in terms of Linear Matrix Inequalities (LMI) technique. A feasible algorithm is developed to compute the designed parameters. This algorithm provides more flexibility and mitigates the conservatism of quadratic approach. Finally, simulation results are presented to illustrate the efficiency of the proposed method..

The challenge for control of permanent magnet synchronous motor (PMSM) drives is to achieve high dynamics, accurate steady-state performance, and to respect all constraints on input voltage and stator currents. Many partial results on each of these aspects are available. Recently, it has been shown that the existing techniques can be combined with ideas from predictive control to achieve satisfaction of state constraints such as maximum current amplitude. In this article, we propose to complement the direct-speed-control-based state-dependent Riccati equation (SDRE) approach by explicit constraints on the current amplitude and the field-weakening curve. Since the cost-to-go function for the SDRE is available, the problem is formulated as quadratic programming with a quadratic constraint. The resulting controller achieves an excellent steady-state solution due to SDRE and satisfies constraints on the maximum current amplitude and field-weakening operation. Experimental tests of the proposed cascade-free speed control are performed on a laboratory prototype of a 10.7-kW PMSM drive. The proposed optimization routine can be used to enforce state constraints in other unconstrained control methods.

In this paper, the relationship between the degree of controllability (DOC) of controlled plants and the corresponding quadratic optimal performance index in LQR control is investigated for the ele...

Proper orthogonal decomposition (POD) has been extensively used for developing reduced-order models (ROM) in fluid mechanics. In most of the research, velocity POD modes are employed for analysis while there is less focus on using pressure POD modes. In fact, pressure POD modes can be beneficial to gain physical insight to the aerodynamic forces acting on a structure. In this study, we simulate the flow past a circular cylinder and compute the velocity and pressure POD modes from the data ensemble. We then perform the novel process of pressure mode decomposition (PMD). We consider the localized pressure POD modes on the cylinder surface, integrate each mode on the cylinder surface, and decompose them into normal and streamwise components, namely lift (LDC) and drag (DDC) decomposition coefficients, respectively. The LDC and DDC are scalar quantities and are independent of spatial coordinates. These coefficients provide insight to the contribution of each mode in the development of aerodynamic forces. The lift and drag coefficients are expanded in a Galerkin fashion using the decomposition coefficients. The temporal coefficients are computed through a mapping function based on a quadratic stochastic estimator. The main contribution and strength of our research is the PMD analysis and to develop an efficient ROM for aerodynamic forces which shows promising results for circular and elliptic cylinders.

When a high-power wind turbine runs in normal status, and if bearing current exists for a long time, multiple point corrosion would occur and gradually increase, eventually forming a ripple groove on the inner ring race, outer ring race and rolling body. It would lead to more vibration and shock, thereby causing fault in the equipment; the best way to prevent the this kind of fault is to find the effective fault characteristics and predict the damage’s degree on the bearing. In this paper, an adaptive neural network prediction method based on the quadratic root mean square of sub-band manifold is proposed. The damage characteristics can be analyzed by following steps: firstly, the vibration signal is decomposed into multidimensional time frequency space by wavelet packet method. Secondly, the sub-band of the manifold is constructed. The third step is to extract the root mean square value. Finally, the damage characteristics of the bearing current of the two square root sub-band manifold are obtained. Based on the back propagation network, the adaptive prediction model is built, and the training speed could be adjusted automatically according to the prediction error and precision. According to the bearing’s fault mechanism with current damage on the high-power wind turbine, one fault experiment platform has been built to simulate the current damage process of the bearing and verify the prediction method based on the quadratic root mean square of sub-band manifold. The experimental results show that the method can effectively predict the degree of bearing current damage, and the relative error of prediction is less than 5%.

In this paper, we investigate the application of model predictive control (MPC) strategy for controlling a chaser spacecraft to dock with a tumbling target in space. The CW-based model is used to predict the proximity trajectory, while a receding prediction horizon is employed to complete the specific docking manipulation in finite time, and a 2-norm cost index about control input is used to minimize the fuel consumption of the chaser during docking. To ensure the implementation of the docking procedure, several engineering constraints such as control input saturation, collision avoidance, velocity constraint, and dock-enabling condition are considered simultaneously. A time-varying linear inequality constraint is used rather than the original quadratic constraint, to achieve the collision-avoidance requirement. Therefore, the constrained optimal rendezvous trajectory in the case of such a MPC framework can be obtained by solving a quadratic program subject to linear constraints. To verify the feasibility of the proposed MPC strategy, four AR&D scenarios are simulated. The simulation results show that MPC strategy can be an effective approach to achieve rendezvous and proximity maneuvering between a chaser spacecraft and a tumbling target while satisfying various constraints, and provide robustness to disturbances.

In this paper, a novel approach is proposed to formulate and solve the problem of active fault detection and control for multi-model discrete-time systems. The main objectives are to design an optimal fixed-order controller for stabilizing the healthy and faulty models by minimizing a well-defined quadratic performance index, meanwhile synthesizing a test signal for active fault detection in the presence of bounded energy uncertainty. To do this, an optimal solution is proposed to obtain the trade-off between the optimal fixed-order stabilizing controller signal and optimal test signal for active fault detection. These objectives could be achieved by solving a finite-dimensional constrained optimization problem. The dynamic nonlinear optimization problem is solved by two constructive recursive solution algorithms which are finally applied to a numerical example. Simulation results show the effectiveness of the proposed algorithms.

This paper further demonstrates and confirms the tapped inductor switcher analysis method proposed earlier. The objective of this paper is to extend the application of this modeling technique to integrated converters containing multiple switched inductive elements. Following the proposed methodology makes the dynamic modeling of a switching power converter a fast and easy task. Three typical high-order dc–dc topologies, the Quadratic Buck Converter, the Cuk, and the SEPIC converters, are considered as study cases. The obtained small-signal dynamic models were verified by simulation and by comparison to previous theoretical and experimental results.

International audience; In the paper, a novel methodology of actuator fault estimation for linear discrete-time systems is proposed. To solve such a challenging problem, a quadratic boundedness approach is used to guarantee the convergence of the proposed state and actuator fault estimation method. In the proposed methodology, the robustness is achieved through the unknown input decoupling while an unappealing effect of the undecoupled disturbances is minimized. Moreover, the developed approach enables to obtain a feasible set of joint system state and fault estimation error. Based on this knowledge, a novel methodology of calculating the uncertainty intervals of the system state and actuator fault is proposed. The illustrative part of the paper presents results obtained for the laboratory DC servo-motor and compares the proposed approach with two alternative methods. Based on this real-data example, the efficiency of the developed methodology is clearly exposed.

This paper addresses the consensus control problem for linear multi-agent systems subject to uniform time-varying input delays and external disturbance. A novel state-feedback consensus protocol is proposed under the integral quadratic constraint (IQC) framework, which utilises not only the relative state information from neighbouring agents but also the real-time information of delays by means of the dynamic IQC system states for feedback control. Based on this new consensus protocol, the associated IQC-based control synthesis conditions are established and fully characterised as linear matrix inequalities (LMIs), such that the consensus control solution with optimal H∞ disturbance attenuation performance can be synthesised efficiently via convex optimisation. A numerical example is used to demonstrate the proposed approach.

In this paper, the fault diagnosis (FD) and fault-tolerant tracking control (FTTC) problem for a class of discrete-time systems with faults and delays in actuator and measurement is investigated. In the first step, a discrete delay-free transformation approach is introduced for an constructed augmented system such that the two-point-boundary-value (TPBV) problem with advanced and delayed items can be avoided. Then, the optimal fault-tolerant tracking controller (OFTTC) is proposed with respect to an equivalent reformed quadratic performance index. Moreover, by using the real-time system output rather than the residual errors, a reduced-order-observer-based fault diagnoser for the augmented system is designed to diagnose faults in actuator and measurement, and solve the physically unrealizable problem of proposed OFTTC. Finally, the effectiveness of the proposed fault diagnoser and OFTTC is illustrated by a realistic design example for industrial electric heater.

This paper mainly studies the wave-free characteristic of heave motion of a sandglass-type floating body to overcome the limitations of traditional ship-type and cylindrical floating, production, storage and offloading units. In the first instance, the theoretical and numerical methods are used to determine the added mass and damping wherein the heave motion can be decoupled. The existence of wave-free characteristic of heave motion is theoretically and numerically verified. Moreover, the heaving quadratic transfer function of second-order wave drift force is calculated and its performance has been discussed. Finally, the relationship between wave-free frequency and wave spectrum is selected as the design principle of the shape parameters. On this basis, some experiments of new sandglass-type and cylindrical models are carried out, which coincide with the numerical predictions very well and show that the new design based on wave-free frequency can significantly improve the heave motion performance.

Iterative Real-Time Optimization (RTO) has gained increasing attention in the context of model-based optimization of the operating points of chemical plants in the presence of plant-model mismatch. In all iterative RTO schemes, it is necessary to wait until the plant has reached a steady-state to obtain the required information on plant performance and constraint satisfaction which leads to slow convergence in the case of processes with slow dynamics. It has recently been proposed to use a linear black-box model that is identified online to predict the steady-state values of the plant during the transient between different stationary operating points; these values are then employed in the modifier adaptation with quadratic approximation to drive the process to its optimum (Gao et al., 2016). In this contribution, this idea is extended by integrating nonlinear system identification into iterative RTO. Specifically, a Nonlinear Output Error (NOE) model is proposed to describe the dynamics of the process, thus providing a faster prediction of the steady-state of the plant. A robust scheme for the estimation of the model parameters is proposed. The performance of the strategy is illustrated by simulation studies of a continuous stirred-tank reactor. By means of the proposed methodology a fast convergence to the plant optimum can be achieved despite plant-model mismatches.

A power balancing method of distributed generation (DG) and electric vehicle charging is presented for minimizing operation costs of distribution systems with uncertainties, which includes the uncertainties in output power of DG and the randomness of charging power of electric vehicles (EV). A probability model is established for the uncertain characteristics of DG and electric vehicle charging. A multi-state optimization coordination method for DG of renewable energy systems and electric vehicle charging in distribution systems based on quadratic rotation cone programming is presented to minimize the expected generation cost of generators in the main power grid, the expected operation cost of DG systems in distribution systems, and the expected social outage loss. An objective function maximizing the operation efficiency of distribution systems with DGs and EVs is proposed. Using quadratic rotated conic programming, the nonlinear objective function and constraint functions are transformed into a linear form. An IEEE 14-node distribution system is used as a study example to illustrate adaptability of the proposed model and the feasibility of the proposed method. The simulation results show that the proposed method simplifies the original problem of the optimization problem and makes its solution faster, more stable, and better.

PurposeThe presented paper aims to consider algorithm for optimal design of multilayer thermal insulation.Design/methodology/approachDeveloped algorithm is based on a sequential quadratic programming method.Findings2D mathematical model of heat transfer in thermal protection was considered in frame of thermal design of spacecraft. The sensitivity functions were used to estimate the Jacobean of the object functions.Research limitations/implicationsDesign of distributed parameter systems and shape optimization may be thought of as geometrical inverse problems, in which the positions of free boundaries are determined along with the spatial variables. In such problems, the missing data (i.e. the position of boundaries) are compensated for by the presence of the so-called inverse problem additional conditions. In the case under consideration, such conditions are constrains on the temperature values at the discrete points of the system.Practical implicationsResults are presented how to apply the algorithm suggested for solving a practical problem – thickness sampling for a thermal protection system of advanced solar probe.Originality/valueThe procedure proposed in the paper to solve a design problem is based on the method of quadratic approximation of the initial problem statement as a Lagrange formulation. This has allowed to construct a rather universal algorithm applicable without modification for solving a wide range of thermal design problems.

This paper is concerned with the problem of state feedback control for nonlinear quadratic systems with randomly occurring actuator saturation. The considered actuator saturation is assumed to occur in a random way, and the randomly occurring rates of the saturation are time-varying with known upper and lower bounds. By using the Lyapunov approach, a sufficient condition is given to guarantee that the closed-loop system is locally asymptotically stable in mean-square sense. The desired controller gain can be obtained in terms of the solutions to certain linear matrix inequalities. Finally, a simulation example is provided to show the effectiveness of the proposed control scheme.

This paper concerns the stabilization problem of Markovian jump LPV systems with incomplete transition descriptions and measurement noises. The attention of this paper is focused on two aspects: (1) proposing a quadratic combination method of totally incorporating the transition rate constraints into the H 2 stabilization condition, and (2) developing a nonlinear control strategy of clearly eliminating the influence of measurement disturbances in the control process. Continuing this, the derived stabilization condition is applied to the speed control of DC-motor devices with voltage fluctuations, where the features of fluctuating voltage are modeled in both stochastic and deterministic senses.

This paper presents a trajectory planner for humanoid robots based on nonlinear model predictive control that can generate trajectories with variable center of mass (COM) heights and adaptive foot positions in real-time. This is done by analytically formulating the zero moment point constraint as a quadratic function that includes the vertical COM state. This constraint is then combined with linear foot position constraints, constraints on the vertical COM state and a quadratic goal function to form a quadratically constrained quadratic program. This program is then solved via sequential quadratic programing. It can be solved in less than 5 ms on average with a 3.4 GHz CPU by taking advantage of the fact that the analytic problem formulation allows the analytical gradients to be computed, thus time consuming numerical differentiation is avoided and the gradient computation time is reduced from 12.4 to 0.12 ms. An experiment on a 3.5 Kg robot is conducted to verify the effectiveness of this algorithm in walking on a terrain where the two feet are at different heights. Lastly it is shown via simulation that the proposed approach can improve disturbance recovery capacity by up to 7%.

In Global Navigation Satellite Systems (GNSS) positioning, one often tries to establish a mathematic model to capture the systematic behaviors of observations as much as possible. However, the observation residuals still exhibit, to a great extent, as (somewhat systematic) signals. Nevertheless, those systematic variations are referred to as the unmodelled errors, which are difficult to be further modelled by setting up additional parameters. Different from the random errors, the unmodelled errors are colored and time correlated. In general, the larger the time correlations are, the more significant the unmodelled errors. Hence, understanding the time correlations of unmodelled errors is important to develop the theory for processing the unmodelled errors. In this study, we compare and analyze the time correlations caused by unmodelled errors of Global Positioning System (GPS) and BeiDou signals. The time correlations are estimated based on the residuals of double differenced observations on 11 baselines with different lengths. The results show that the time correlation patterns are different significantly between GPS and BeiDou observations. Besides, the code and phase data from the same satellite system are also not the same. Furthermore, the unmodelled errors are affected by not only the baseline length, but also some other factors. In addition, to make use of the time correlations with more efficiency, we propose to fit the time correlations by using exponent and quadratic models and the fitting coefficients are given. Finally, the sequential adjustment method considering the time correlations is implemented to compute the baseline solutions. The results show that the solutions considering the time correlations can objectively reflect the actual precisions of parameter estimates.

This study presents a strategy for platoon formation and evolution of connected and autonomous vehicles (CAVs) in free-flow traffic. The proposed strategy is based on swarm intelligence, which describes bird flocking, fish schooling, and so on, in natural and artificial systems. In this concept, CAVs behave according to some rules to move together as a platoon without collisions. The rules are expressed by a spring–mass–damper system: CAV platoon formation and evolution are controlled by the spring constant and damping coefficient. Valid domains of these control parameters were derived on the basis of physical vehicle properties (e.g., bounded acceleration and deceleration) for realistic control. Furthermore, various relationships—maximum (in which the spring constant was set at its maximum for the given flow), quadratic, and cubic—between the control parameters and traffic flow were examined with simulations to obtain insight into desirable control parameter settings. The results suggest that the most efficient platooning can be achieved by the maximum relationship between the spring constant and flow with critical damping. However, the cubic relationship coupled with overdamping is more desirable in low-flow states to allow more freedom for vehicles to change lanes.

The problem of active fault-tolerant control (FTC) with multiple performance constraints including the closed-loop stability, the control input and system state constraints, and the optimized quadratic performance as well, is investigated for a class of discrete time-varying systems subject to sensor faults. A satisfactory FTC strategy is proposed by means of the numerical recursion to guarantee the acceptable performance constraints on the faulty closed-loop system. In the strategy, according to the constraints relaxation technique from MPC technology, a soft-constraint based online FTC design method is developed to explicitly handle the multi-objective requirements of the performance constraints. Its main contributions include that during the online FTC controller reconfiguration, the infeasibility due to the possibly inconsistent constraints is avoided by relaxing soft constraints on the one hand, such that the continuous operation of the closed-loop FTC system is ensured. On the other hand, the developed nonlinear and non-convex optimization calculation for the numerical FTC controller design is converted into a convex quadratically constrained quadratic programming (QCQP) under certain condition, to solve the NP-hard problem due to the intrinsic non-convex constraints. Moreover, the stability of the closed-loop FTC system is analyzed based on the stability theory of discrete-time nonlinear system. Finally, two simulative examples are presented to verify the effectiveness of the proposed satisfactory FTC method.