Control Coefficients Research Articles

PV Distribution Network Planning Method Considering the Cost and Effectiveness of Voltage Control

The large-scale grid connection of distributed power sources can save energy, reduce emissions, and improve the flexibility of the power supply, but the volatility and uncertainty of its power output lead to voltage fluctuations and power quality problems, among which voltage problems have become an important factor inhibiting the large-scale access of distributed power sources, posing new challenges to the grid planning and operation. To address the grid voltage crossing problem after PV grid connection, firstly, a multi-time scale voltage control model based on voltage control coefficients is established to achieve maximum network safety and economic satisfaction; the problem is then solved using a variety of group traction differential evolutionary algorithms with the objective function being the minimum annual integrated cost and the maximum percentage of clean energy generation. This method offers a strong theoretical and technical guarantee for the planning of distributed power access to the distribution system.

Adaptive PI control of ultrasonic motor using iterative learning methods

PI (proportional integral) controller is widely utilized in ultrasonic motor control systems. However, PI controllers which use fixed parameters is often difficult to ensure that the control performance meets the expectations. Based on the idea of iterative learning control (ILC), a new online adaptive adjustment method for PI control parameters is proposed. Simple P (proportional) type and PD (proportional differential) type iterative learning controllers are designed to adjust the proportional and integral coefficients of PI controller online. Using this method, the coefficients of PI controller are adaptively adjusted with the change of motor characteristics to ensure the control performance meets expectations. This method can improve the nonlinear expression ability of the controller, so that its input–output relationship can better match the characteristics of the controlled object, thereby improving the control performance. Results of comparative experiments indicate the validity of the proposed control strategy.

The path following of intelligent unmanned vehicle scheme based on adaptive sliding mode-model predictive control

An adaptive sliding mode-model predictive control for the path following of intelligent unmanned vehicle is given in this paper. On account of excellent performances of the sliding mode structure, this algorithm can not only effectively estimate the uncertainty of the vehicle system to further improve the following accuracy, but minish the amount of calculation in comparision with model predictive control. Then, the following accuracy between the real system and the theoretical model can be compensated by the fractional order coefficient of controller. Therefore, an adaptive fractional order sliding mode-fractional order model predictive control is designed to follow the path of the intelligent unmanned vehicle. Meanwhile, the robust stability and control accuracy of the associated control algorithm are proved. Finally, different paths are designed to verify the theoretical analysis of the control performance in the controllers.

Optimized Cooperative Control of Error Port-Controlled Hamiltonian and Adaptive Backstepping Sliding Mode for a Multi-Joint Industrial Robot

Robot joints driven by permanent magnet synchronous motors (PMSM) often cannot have both superior accuracy and rapidity when they track target signals. The robot joints have fine dynamic characteristics and poor steady-state characteristics when the signal controller is used, or they have fine steady-state characteristics and poor dynamic characteristics when the energy controller is used. It is hard to make robot joints that have both superior dynamic and steady-state characteristics at once using a single control method. In order to solve this problem, the strategy of optimized cooperative control is proposed. First, an error port-controlled Hamiltonian (EPCH) energy controller and an adaptive backstepping sliding mode (ABSM) signal controller are designed. Second, an optimized cooperative control coefficient based on the position error of a robot joint is designed; this enables the system to switch smoothly between the EPCH energy controller and ABSM signal controller. Next, the strategy of optimized cooperative control is designed. In this way, robot systems can combine the advantages of the EPCH energy controller and the ABSM signal controller. Finally, simulation results demonstrate that using the strategy of optimized cooperative control gives robot joints outstanding control performance in terms of tracking accuracy and response rapidity.

Study on Multi-Mode Switching Control Strategy of Active Suspension Based on Road Estimation

In this paper, the least squares method is used to determine the vertical height of the road space domain. Based on the road estimation method, the active suspension control mode switching model is constructed, and the dynamic characteristics of the vehicle in comfort, safety, and integrated modes are analyzed. The vibration signal is collected by the sensor, and the parameters such as vehicle driving conditions are solved for in reverse. A control strategy for multiple mode switching under different road surfaces and speeds is constructed. At the same time, the particle swarm optimization algorithm (PSO) is used to optimize the weight coefficients of LQR control under different modes, and the dynamic performance of vehicle driving is comprehensively analyzed. The test and simulation results show that the road estimation results under different speeds in the same road section are very close to the results obtained by the detection ruler method, and the overall error is less than 2%. Compared with the active suspension controlled by passive and traditional LQR, the multi-mode switching strategy can achieve a better balance between driving comfort and handling safety and stability, and also improve the driving experience more intelligently and comprehensively.

Design of a wireless charging system in DC microgrids with accurate output regulation and optimal efficiency

This paper presents a general circuit and control design method for wireless power transfer (WPT) systems in DC microgrids to achieve optimal power transfer efficiency, while maintain accurate output voltage regulation. An auxiliary inductor is added at the transmitter resonator to form a current sink to ensure zero voltage switching (ZVS) of the primary-side full-bridge inverter with even extreme-light load conditions. Besides, an adaptive proportional-integral (PI) controller is adopted to track the output voltage references by regulating the phase shift angle of the phase shift control for the full-bridge inverter. The coefficients of the adaptive proportional-integral controller are determined by the inductor of the auxiliary inductor. Both simulation and experimental results have validated the effectiveness of the proposed circuit and control design in achieving optimal efficiency and output voltage regulation for wireless power transfer systems in DC microgrids with source and load variations.

Jerk-Optimal Piecewise Planning of Tool Orientation for 5-Axis Ball-End Machining With Linearized Kinematic Constraints

Abstract When planning tool orientations for 5-axis machining from the perspective of kinematics, existing works often tend to optimize the angular velocity or acceleration of the rotary axes rather than the angular jerk due to its calculation sensitivity to discretization variations, though the jerk is fundamentally tied to the tracking errors and residual vibrations of the actuators. In this paper, a study is reported on how to optimize the angular jerk of the rotary axes while comprehensively considering the kinematic constraints, thus achieving a jerk-optimal tool orientation along the tool path, with driving capacity of the rotary axes respected. In this method, the displacements of the rotary axes are continuously represented by two quintic B-spline curves, and then the angular velocity, acceleration, and jerk of the rotary axes, which are the derivatives of the displacements, can be succinctly represented as a B-spline curve. Taking advantage of the convex hull property of B-spline curve, the linear analytical representations of the kinematic constraints of the rotary axes can be successfully derived in form of control coefficient combinations. To prevent the machining interference at the same time, a greedy strategy that incorporates a process of alternately smoothing tool orientation and checking machining interference is employed. Then, the smooth displacement splines of the rotary axes can be obtained by solving a constructed quadratic programming (QP) model that minimizes the angular jerk along the tool path, while satisfying kinematic constraints and without machining interference. Moreover, to generate efficiently tool orientations for long tool paths, a piecewise planning strategy that optimizes the tool orientation from coarse to fine is developed. Finally, the conducted experiments validate the proposed method.

Coordination of BESS and PV system with bidirectional power control strategy in AC microgrid

An AC microgrid in collaboration with Battery Energy Storage Systems (BESSs) and PV systems suffers uncertainties in power flow. The State of Charge (SoC) of an operating BESS reduces with time, degrading the DC link voltage and inverter’s output frequency. The conventional control on P-f droop characteristics has limitations in control flexibility and for that, the proposed droop control is designed on P-δ droop characteristics to maintain frequency and power factor even in discharging mode of the battery. The BESS follows a secondary distributed coordination scheme where SoC balancing using an average consensus technique is used to equalise the battery unit power contribution. The control scheme of BESS introduces an advanced polynomial regression-based changeover technique that adaptively changes battery mode ( discharging to charging or vice versa) to maintain DC link voltage, avoiding voltage cutoff of the battery. For PV systems, the decentralised control scheme is designed to contribute power using its local controller and is effective, especially in communication failure conditions. The control architecture of BESS and PV units has been configured differently and tested on a CIGRE LV feeder using MATLAB simulation. To validate the control strategy in real-time, hardware is implemented on the single-bus distribution network. The selection of control coefficients is made by small-signal analysis.

Global regulation of feedforward nonlinear systems with unknown time-varying control coefficients and growth rate

In this paper, we consider a regulation problem for a class of feedforward nonlinear systems with unknown control coefficients and unknown growth rate. More specifically, the unknown control coefficients are assumed to be time-varying and belong to ranges with unknown upper and lower bounds. Due to the described control coefficients with uncertain feedfoward nonlinearities, our considered system is the natural extension of the related existing results. In solving our control problem, a new adaptive controller is derived by constructing a Lyapunov function in backstepping-like procedure and utilizing appropriate parameters based on the gain scaling technique and Nussbaum function. The uniquely designed exponents of a dynamic gain overcome the difficulties caused from the unknown sign and unknown ranges of the control coefficients and uncertain nonlinearities and thus play a key role in system regulation. We give the rigorous system analysis and simulation results of the numerical example to certify our control method.

Nussbaum‐based robust adaptive control for nonlinear switching systems with switching unknown control coefficients

AbstractThis paper develops a robust adaptive dynamic surface control (DSC) design approach for switching strict feedback nonlinear systems with multiple switching control gains and unknown control directions. To capture the multiple unknown control directions, we design multiple Nussbaum‐type gains for every subsystem. Based on this Nussbaum‐type gain design, we construct a common Lyapunov function for all subsystems via backstepping technique, which can ensure the system stability under the existence of random switches. Meanwhile, the problem of multiple switching control gains is resolved by this means. To obviate repeated differentiation and decrease computation complexity, the dynamic surface control technique is introduced. Finally, two simulation examples are provided to validate the effectiveness of the developed method.

New Results on Fixed-Time Stabilization of Switched Uncertain Nonlinear Systems in p-Normal Form

In this article, the global fixed-time stabilization problem is addressed for a class of switched uncertain nonlinear systems in <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> -normal form. Different from the existing results, the bounds of the unknown system parameters, including dead-zone parameters and control coefficients, are not required to be known. By combining the adding a power integrator technique with the common Lyapunov function method, a novel adaptive controller is proposed and a dynamic controller parameter is introduced to cope with the unknown system parameters. Besides, an effective common regulate rule is designed based on the improved fixed-time stability framework. Furthermore, it is shown that the controller parameter can be regulated online by the switching mechanism to compensate the unknown system parameters and the system states can converge to zero in fixed time under arbitrary switchings. The effectiveness of the proposed method is verified by a simulation example.

First Order Limiting Optimality Conditions in the Coefficient Control of an Obstacle Problem

AbstractWe consider an optimal control problem governed by an obstacle problem. The novelty and focus of this work will be the introduction of a control variable into the coefficients of this optimal control problem.In particular, we discuss the effects of the multiplicative coupling between control and, derivatives of, the state. This prevents the use of standard weak convergence arguments in limit arguments. As it is known, H‐convergence needs to be utilized, e.g., to prove existence of a solution to the problem. To obtain optimality conditions, we present a regularization approach to circumvent the non‐differentiability of the solution operator of the obstacle problems. We then discuss the limiting optimality conditions obtained by the proposed regularization.

An Improved Inertia Disturbance Suppression Method for PMSM Based on Disturbance Observer and Two-Degree-of-Freedom PI Controller

In this article, an improved inertia disturbance suppression method is proposed for permanent magnet synchronous motor (PMSM) based on disturbance observer and two-degree-of-freedom PI controller (DOB-2DoFC). The DOB-2DoFC is proposed to enhance the speed control performance with variation of inertia caused by load changing. The traditional two-degree-of-freedom controller (2DoFC) is first introduced and the influence of inertia disturbance on 2DoFC is analyzed. Then, the disturbance observer (DOB) is designed to combine with 2DoFC for inertia disturbance suppression. Furthermore, the discretization of DOB-2DoFC is implemented and the impact of discretization is also analyzed, in the meanwhile, the parameters of DOB- 2DoFC is designed based on the premise of stability and good speed control performance. With the designed parameters, better speed control performance can be maintained without tuning any controller coefficients even when the actual inertia is 20 times the controller inertia. Finally, the effectiveness and the correctness of the proposed DOB-2DoFC are verified with experiments on a 400 W PMSM drive platform in two modes.

Formation Tracking of Multiagent Systems With Time-Varying Actuator Faults and Its Application to Task-Space Cooperative Tracking of Manipulators.

This article is concerned with a fault-tolerant formation tracking problem of nonlinear systems under unknown faults, where the leader's states are only accessible to a small set of followers via a directed graph. Under these faults, not only the amplitudes but also the signs of control coefficients become time-varying and unknown. The current setting will enhance the investigated problem's practical relevance and at the same time, it poses nontrivial design challenges of distributed control algorithms and convergence analysis. To solve this problem, a novel distributed control algorithm is developed by incorporating an estimation-based control framework together with a Nussbaum gain approach to guarantee an asymptotic cooperative formation tracking of nonlinear networked systems under unknown and dynamic actuator faults. Moreover, the proposed control framework is extended to ensure an asymptotic task-space coordination of multiple manipulators under unknown actuator faults, kinematics, and dynamics. Lastly, numerical simulation results are provided to validate the effectiveness of the proposed distributed designs.

Load-Frequency Control in a Two-Area Power System Using the Fuzzy-PID Method

Power systems are plant units connected to each other, with the electrical power flow constantly moving between them and the load. All systems must be implemented in such a way that they remain stable not only under normal conditions but also after the implementation of unwanted inputs or malfunctions and should be able to return to stable nominal conditions as soon as possible. The basic factors of stability control in a power system are the frequency of different areas and the amount of power flow between them. Now that the main goals of stability control in a power system have been stated, these indicators must be kept at their desired levels through the design and implementation of controllers. This paper first describes the mechanism of load frequency control (LFC). The parameters of several types of complementary controllers are then designed based on the proposed algorithm. The controllers designed on the simulated power system are finally applied, and the effect of each controller on faster damping of frequency fluctuations is discussed. In this study, a multi-area power system is simulated and the effect of disturbance in each area and the performance of the proposed controller in controlling the frequency of the whole network are investigated. The case studies show that the share of active and reactive power generation sources in each region have the greatest impact on frequency control, given that the proposed control coefficients are determined using fuzzy logic in the shortest possible time and the number of transient oscillations eliminated, resulting in a steady system.

Adaptive Backstepping Terminal Sliding Mode Control of Nonlinear System Using Fuzzy Neural Structure

An adaptive backstepping terminal sliding mode control (ABTSMC) method based on a multiple−layer fuzzy neural network is proposed for a class of nonlinear systems with parameter variations and external disturbances in this study. The proposed neural network is utilized to estimate the nonlinear function to handle the unknown uncertainties of the system and reduce the switching term gain. It has a strong learning ability and high approximation accuracy due to the combination of a fuzzy neural network and recurrent neural network. The neural network parameters can be adaptively adjusted to optimal values through the adaptive laws derived from the Lyapunov theorem. To stabilize the control signal, the additional parameter adaptive law derived by the adaptive projection algorithm is used to estimate the control coefficient. The terminal sliding mode control (TSMC) is introduced on the basis of backstepping control, which can ensure that the tracking error converges in finite time. The simulation example is carried out on the DC–DC buck converter model to verify the effectiveness and superiority of the proposed control method. The contrasting results show that the ABTSMC−DHLRNN possesses higher steady−state accuracy and faster transient response.

Modelling criteria proposal for dynamic analysis of beam bridges under moving loads using fem models

Regulations establish requirements to be met in dynamic analyses of moving loads, but do not establish any criteria for how to model a bridge for dynamic analysis. In other words, what spatial and temporal discretisation must the model comply with in order for the results to be accurate? In this article, four control coefficients and their limiting values are established so that, depending on the characteristics of the bridge and the speed of the moving load, it can be guaranteed that the results of the FEM models are accurate. A total of 35 cases, focused on beam bridges, are analysed and the computational models are compared with a proposed formula for moving loads. It is shown that with common geometric discretisation techniques and, independently of the time discretisation, a bridge can undergo resonance when the moving load passes but the model cannot represent this phenomenon. Finally, it is demonstrated that by complying with the modelling criteria proposed in this article, any structural response could be correctly captured by the calculation models.

Innovative Inertial Response Imitation and Rotor Speed Recovery Control Scheme for a DFIG

This paper proposes an innovative inertial response imitation (IRI) and rotor speed recovery (RSR) control scheme of a doubly-fed induction generator (DFIG, Type 3 wind turbine generator) to provide better frequency support response and RSR services for a high wind power penetrated electric power grid. To achieve the first benefit, the coupling relationship between the control coefficient of DFIGs and the frequency deviation was established by using the exponential function so that the control coefficient becomes large with the increasing frequency deviations and sizes of disturbance. After supporting the system frequency, the exponential function was employed to schedule the dynamic control coefficient to alleviate the negative effects of RSR on the instantaneous system frequency. The benefits of the proposed IIR and RSR strategy were investigated in a test system under various scenarios of sizes of disturbance and wind speed conditions. Test results clearly demonstrate that the proposed IIR and RSR strategy is capable of boosting the maximum system frequency excursion and reducing the negative influences on the system frequency during the speed recovery period.

Neural-network-based output feedback adaptive resilient control for stochastic nonlinear systems subject to sensor and actuator attacks

This paper addresses the output feedback adaptive resilient control problem for a class of stochastic nonlinear systems under sensor and actuator attacks. By applying a linear state transformation, the original system is transformed into a new one for which output feedback control becomes possible. The Nussbaum function is exploited to overcome the unknown control direction problem generated by uncertain control coefficients and adversary attacks. Based on the available compromised output signal and the states reconstructed by a new extended state observer, an output feedback adaptive resilient control scheme is proposed for such systems by combining the neural-networks-based approximation approach, backstepping technique and some special methods. The proposed controller can guarantee that all signals in the closed-loop system are semiglobally bounded in probability and the stabilisation error variables remain in an adjustable vicinity of origin. Finally, the effectiveness and applicability of the proposed control methodology is verified by two illustrative examples.

Adaptive backstepping control for permanent magnet linear motors against uncertainties and disturbances

In practical operation, the system parameters of a permanent magnet linear motor are affected by unknown factors, including nonlinear friction, sudden load changes, thrust fluctuations, and so on. Therefore, a controller designed for fixed parameters often cannot produce satisfactory results. To solve this problem, a novel design method for permanent magnet linear motor systems based on backstepping is proposed in this article. The proposed control scheme does not require the values or range of changes of the system parameters in advance but constructs an update law to perform online parameter estimation. A difficulty encountered in controller design and stability analysis is the estimation of the unknown coefficient of a single-variable state, which can be considered as a virtual input. In this article, the estimated virtual control coefficient is introduced into the coordinate transformation to construct a novel coordinate transformation. However, introducing the estimated coefficient into the virtual control makes the derivative of the Lyapunov function used in the stability analysis more complex. This novel nonlinear dynamic term can be precisely cancelled by changing the update law of the coefficient. Finally, simulations are performed to assess the performance of the proposed control scheme against that of a traditional proportional–integral–derivative controller, and the simulation results show that the proposed control law can effectively improve the system performance.