Traditional Linear Control Research Articles

Extended state observer based fractional order controller design for integer high order systems

In this paper, based on the extended state observer (ESO) and on a fractional order controller (FOC), composed of an integer order PID cascaded with a fractional order filter (FOF), a new control scheme for an n th order integer plant is proposed. The ESO is used to estimate and cancel the unknown internal dynamics and the external disturbance. Afterwards, an FOC is designed to resolve the set-point tracking problem. An analytical and systematic method is proposed to design the FOC. This method is based on the Internal Model Control (IMC) and the Bode’s Ideal Transfer Function (BITF). Therefore, the proposed control structure improves the robustness and performance of the traditional linear active disturbance rejection control (LADRC), especially for the open-loop gain variation. In addition, since the system be controlled is an n th order, a general form of the BITF is also proposed. Numerical simulations on a nonlinear model and experimental results on a cart-pendulum system design illustrate the effectiveness of the suggested ESO-PID-FOF scheme for the disturbance rejection, the set-point tracking and robustness. A comparison with the results obtained using the standard LADRC is also presented.

Research on the Nonlinear Control Strategy of Three-Phase Bridgeless Rectifier under Unbalanced Grids

The three-phase Y-connected bridgeless rectifier is essentially a nonlinear system, and it is difficult to obtain superior dynamic performance under the action of traditional linear controller. Under the condition of unbalanced power grids, this paper has established a mathematical model based on Euler–Lagrange (EL) equations with line voltage and line current as state variables. Furthermore, it then designed a passivity-based controller in inner current loop based on the mathematical model. The hybrid nonlinear control strategy consisting of active disturbance rejection controller (ADRC) in the outer voltage loop and passivity-based controller (PBC) in the inner current loop is adopted to control the system, which does not need to consider the positive and negative sequence components. The control structure is simple and can improve the steady-state accuracy, dynamic performance and anti-interference ability. The feasibility of the proposed control strategy is verified by computer simulation, which has a guiding significance for the application of three-phase bridgeless rectifier in practical engineering.

Multi-segment fuzzy control for start-up optimizing of LCC-based high-voltage power supply

Widely applied in medical, industrial, and military fields, the high-voltage power supply based on LCC resonant converter (LCC) is ought to establish tens kilovolts of output voltage within milliseconds according to many standards. And any overshoot during this start-up process is not allowed because of the issue or even deadly consequence it may lead. Traditional linear control schemes are powerless to fulfill these two conflicting requirements, especially for the LCC with nonlinear gain characteristics. In this paper, a multi-segment fuzzy control is proposed to tackle this problem. Firstly, the nonlinear gain curve of LCC is piecewise linearized based on its large-signal model, and the control requirements for different segments are determined. Then the control parameters for each part are selected by an off-line fuzzy algorithm. The allowed minimum frequency during the start-up is also piecewise linearized so that the gain ability of LCC is utilized. Finally, the proposed control scheme is verified with an 80kW/150kV X-ray power supply. A low-cost controller is used to implement the start-up control scheme since the control parameters can be pre-calculated and stored in the controller. Compared with the commonly used linear PI control, the non-overshoot start-up time with the proposed control scheme is reduced by more than 60%.

A theoretical vibration‐reduction effect analysis of the floating slab track supported by nonlinear variable stiffness isolators and semi‐active magneto‐rheological dampers

Nonlinear vibration control theory has resolved some bottleneck problems that cannot be solved by traditional linear vibration control theories. In this study, intelligent controllable magneto-rheological dampers (MR-Ds) and high-static-low-dynamic variable stiffness vibration isolators (VS-VIs) are innovatively applied to a traditional steel-spring floating slab track (FST) to improve its low-frequency vibration-reduction effect. To determine the reasonable parameter group of the VS-VI and the parameter matching when used with MR-D, parameter groups were first preliminarily proposed through static and dynamic analyses of the equivalent single-degree-of-freedom (SDOF) model of the FST. A vertical vehicle–variable stiffness-magneto-rheological FST coupled dynamic model was established, and the parameter group was optimized based on safety and vibration-reduction analyses. The simulated results showed that the positive and negative stiffnesses were the key parameters that determined the stiffness nonlinearity level and dynamic support capacity of the VS-VI. Improving the positive stiffness is necessary to achieve a low-dynamic stiffness under no vehicle load and to ensure the millimeter-level dynamic displacement of the FST under vehicle load. An appropriate stiffness nonlinearity level can achieve a better vibration-reduction effect, and excessive nonlinearity level will not bring more significant improvements. The damping ratio in the dynamic analysis is a key parameter to prevent the jumping phenomenon, and the recommended value should not be less than 5%. Moreover, when the VS-VI and the MR-D were efficiently incorporated, a smaller MR damping force and larger displacement threshold could not only achieve a better vibration-reduction effect but also reduce the energy consumption.

Research on low-speed characteristics of differential double-drive feed system

Abstract. It is difficult to achieve high-precision control due to frictional nonlinearity by traditional linear control methodology for the classical drive feed system at low speed. Here, the double-drive differential feed system is proposed to reduce the influence of the nonlinear friction at the ball screw pair of a linear feed system operating at low speed. The dynamic models and the LuGre friction models of the classical drive feed system and the double-drive differential feed system are established, respectively. Based on these, the simulation models of the classical drive feed system and the double-drive differential feed system are established in MATLAB to study the critical creeping velocity of the table. Compared with the classical drive feed system, a lower stable velocity can be obtained for the table with the double-drive differential feed system, because the speed of both motors in the double-drive differential feed system is higher than the critical creeping speed of the classical drive feed system screw motor, thereby overcoming the influence of the Stribeck effect and avoiding the frictional nonlinearity at low speed.

Leader-Following Consensus of Multiagent Systems via Asynchronous Sampled-Data Control: A Hybrid System Approach

In this article, we develop a hybrid system approach for sampled-data-based leader-following consensus of multiagent systems over a static/switching directed network. First, an asynchronous sampled-data hybrid consensus control protocol is proposed, which has an extra reset control part in contrast with the traditional linear control protocol and is beneficial for achieving better transient consensus performance. Then, with several properly defined internal variables, a hybrid model consisting of both flow and jump dynamics is constructed to describe the closed-loop dynamics. Based on this model and internal decreasing functions, Lyapunov-based stability conditions are derived, and larger upper bound of sampling period can be obtained compared to the existing related work. Finally, an example is given to show the superiority of the proposed approach.

Composite adaptive backstepping controller design and the energy calculation for active suspension system.

The half-car suspension has the coupling of pitch angle and front and rear suspension. Especially when the suspension model has a series of uncertainties, the traditional linear control method is difficult to be applied to the half-car suspension model. At present, there is no systematic method to solve the suspension power. According to the energy storage characteristics of the elastic components of the suspension, the power calculation formula is proposed in this paper. This paper proposes a composite adaptive backstepping control scheme for the half-car active suspension systems. In this method, the correlation information between the output error and the parameter estimation error is used to construct the adaptive law. According to the energy storage characteristics of the elastic components of the suspension, the power calculation formula is introduced. The compound adaptive law and the ordinary adaptive law have good disturbance suppression, both of which can solve the pitching angle problem of the semi-car suspension, but the algorithm of the compound adaptive law is superior in effect. In terms of vehicle comfort, the algorithm of the general adaptive law can achieve stability quickly, but compared with the composite adaptive law, its peak value and jitter are higher, while the algorithm of the composite adaptive law is relatively gentle and has better adaptability to human body. In terms of vehicle handling, both control algorithms can maintain driving safety under road excitation, and the compound adaptive algorithm appears to have more advantages. Compared with the traditional adaptive algorithm, the power consumption of the composite adaptive algorithm is relatively lower than that of the former in the whole process. The simulation results show that the ride comfort, operating stability and safety of the vehicle can be effectively improved by the composite adaptive backstepping controller, and the composite adaptive algorithm is more energy-saving than the conventional adaptive algorithm based on projection operator.

A Dynamic Bayesian Network Control Strategy for Modeling Grid-Connected Inverter Stability

The dynamic performance of a grid-connected inverter in a distributed generation system brings new challenges by affecting the power quality and dynamic stability. The traditional linear control method for the inverter requires complex processes including decoupling and control parameter tuning and relies on a pulsewidth modulation module. The nonlinear control method, e.g., the model predictive control (MPC), can address some of the aforementioned challenges but is incapable of dealing with system parameter changes. A data-driven method using the dynamic Bayesian network-based model predictive control (DBN-MPC) is proposed, which takes advantage of the predictive ability of the DBNs to implement the MPC strategy. A predictive model is built, and the prediction signals are generated based on the DBNs. Then, by constructing the cost function and optimization criteria, the controller generates the optimal switching state combination. Using the proposed DBN-MPC method, the predictive model implements parameter learning online, providing more accurate prediction signals for further optimization and the feedback correction. In addition, the control law of the proposed controller can update over time, which enables the grid-connected inverter system to achieve the optimal control. The case studies using the grid-connected inverter system and IEEE 39-bus benchmark power system integrated with the battery energy storage system demonstrate and verify the superiority of the proposed DBN-MPC method.

Adaptive integral-sliding-mode control strategy for maneuvering control of F16 aircraft subject to aerodynamic uncertainty

Aircraft dynamics are highly nonlinear and the traditional linear flight controllers cannot fully utilize the real-time performance ability of aircraft. This paper investigates a nonlinear control approach for high maneuvering fighter aircraft that deployed baseline nonlinear-dynamics-inversion (NDI) control and high-level robust adaptive integral-sliding-mode (ISM) control. The design objective is to maintain the acceptable flight quality at a high angle-of-attack under the influence of unknown disturbance and aerodynamics uncertainties. The nonlinear dynamics of F16 aircraft are divided into two loops by utilizing time-scale separation principle and adaptive ISM/NDI controller is designed for each loop. The aerodynamics coefficients are estimated using the iterative reweighted least square (IRLS) algorithm based on the wind tunnel real-time flight data available at NASA Langlet and Ame Research center. The internal dynamics stability is proven to validate the complete system stability. Simulations are conducted on F16 aircraft and compared the results with the NDI controller and ISM/NDI controller (without adaptive strategy).

Stochastic Control With Affine Dynamics and Extended Quadratic Costs

An extended quadratic function is a quadratic function plus the indicator function of an affine set, i.e., a quadratic function with embedded linear equality constraints. In this article, we show that, under some technical conditions, random convex extended quadratic functions are closed under addition, composition with an affine function, expectation, and partial minimization, i.e., minimizing over some of its arguments. These properties imply that dynamic programming can be tractably carried out for stochastic control problems with random affine dynamics and extended quadratic cost functions. While the equations for the dynamic programming iterations are much more complicated than for traditional linear quadratic control, they are well suited to an object-oriented implementation, which we describe. We also describe a number of known and new applications.

SEPIC converter of Automatic Power Factor Correction by improved traditional linear control

With increasing using of nonlinear electronic equipment at a wide range, total harmonics distortion (THD) in the line current has grown to be a considerable problem. The methods that include reducing harmonics in line current and power factor correction are required. Therefore, the essential target of this proposed work is to reduce the harmonics produced in the line current and improve the power quality of the load by the output voltage regulation and power factor correction (PFC). This paper presents a Single-Ended Primary Inductance Converter (SEPIC) at Discontinuous Conduction Mode (DCM) for automatic power factor Correction (APFC). The SEPIC converter is modeling by utilizing Current Injected Equivalent Circuit Approach (CIECA). The control system is applied by traditional linear control (TLC). The TLC has been modified by an optimization algorithm (OA) and adds an (inner or current). The proposed design of SEPIC-PFC is implemented by MATLAB and Simulink. According to the simulated dynamic behavior results, a unity power factor and 15% of total harmonics distortion (THD) reduction have been obtained comparing with previous work. Besides, a regulated DC output voltage has been achieved. The proposed control system has a low overshoot, small settling time, and steady-state error close to zero

Experimental Study on the Optimal Strategy for Power Regulation of Governing System of Hydropower Station

Active power instability during the power regulation process is a problem that affects the operation security of hydropower stations and the power grid. This paper focuses on the dynamic response to power regulation of a hydro-turbine governor in the power control mode. Firstly, the mathematical model for the hydro-turbine governing system connected to the power grid is established. Then, considering the effect of water hammer and the guide vane operating speed on power oscillation and reverse power regulation, a novel control strategy based on the S-curve acceleration and deceleration control algorithm (S-curve control algorithm) is proposed to improve power regulation. Furthermore, we carried out field tests in a real hydropower station in order to compare the regulation quality of the novel control strategy based on the S-curve control algorithm with the traditional linear control strategy. Finally, the obtained results show that the proposed optimal control strategy for the hydro-turbine governor improves the stability of power regulation by effectively suppressing reverse power regulation and overshoot. This study provides a good solution for the instability of power and reverse power regulation during the regulation process of the hydro-turbine governor in the power control mode.

Attitude control of an unmanned patrol helicopter based on an optimised spiking neural membrane system for use in coal mines

For the attitude control of unmanned helicopters used in the intelligent patrolling of coal mines, a spiking neural membrane system is introduced for attitude optimisation control. First, a geometry-based attitude dynamics model suitable for coal mine scenarios is constructed. Second, in accordance with the attitude dynamics model, an extended spiking neural membrane system (ESNMS) is constructed, and an optimised spiking neural membrane system (OSNMS) and accompanying algorithm are designed to optimise the ESNMS. Then, the attitude control performance of the developed system is theoretically analysed. Finally, through the simulation of a semiphysical experimental platform, trajectory tracking is effectively realised. Under normal and wind disturbance conditions, comparisons with the traditional synovium controller (TSC) and linear feedback controller (LFC) show that the performance of the designed OSNMS is greatly improved, and the experimental results show that the OSNMS has good stability and effectiveness.

Graph-Theoretic Approach to Finite-Time Synchronization for Fuzzy Cohen–Grossberg Neural Networks with Mixed Delays and Discontinuous Activations

This paper investigates finite-time synchronization for fuzzy Cohen–Grossberg neural networks (FCGNNs) with mixed delays and discontinuous activations via state-feedback control. The features of FCGNNs, discrete time delays, distributed delays and discontinuous activations are taken into account which makes our networks more general and practical in comparison with the most existing Cohen–Grossberg neural networks. Two switching state-feedback controllers designed for the implement of finite-time synchronization can be used to effectively overcome the limitations of the traditional continuous linear feedback controllers. Different from previous work, graph theory and Lyapunov method are used to study finite-time synchronization of FCGNNs for the first time in this paper, then some sufficient criteria are obtained to guarantee the finite-time synchronization of FCGNNs. In particular, it is worth noting that the settling time for finite-time synchronization is closely related to the topological structure of FCNNs. Finally, two numerical examples are given to verify the feasibility and effectiveness of the theoretical results.

Neural PD Controller for an Unmanned Aerial Vehicle Trained with Extended Kalman Filter

Flying robots have gained great interest because of their numerous applications. For this reason, the control of Unmanned Aerial Vehicles (UAVs) is one of the most important challenges in mobile robotics. These kinds of robots are commonly controlled with Proportional-Integral-Derivative (PID) controllers; however, traditional linear controllers have limitations when controlling highly nonlinear and uncertain systems such as UAVs. In this paper, a control scheme for the pose of a quadrotor is presented. The scheme presented has the behavior of a PD controller and it is based on a Multilayer Perceptron trained with an Extended Kalman Filter. The Neural Network is trained online in order to ensure adaptation to changes in the presence of dynamics and uncertainties. The control scheme is tested in real time experiments in order to show its effectiveness.

New Double Closed Loop Linear Active Disturbance Rejection Control of Energy Storage Grid-Connected Inverter Based on Lead-Lag Correction Link

The energy storage grid-connected inverter system is a complex system with strong nonlinearity and strong coupling, which quality and efficiency of grid-connection are affected by factors such as grid voltage fluctuations and model uncertainty. Based on the analysis of the working principle of the grid-connected energy storage system, this paper aims to improve the performance of the traditional linear active disturbance rejection control (LADRC) technology, in order to overcome the problems of serious phase lag of linear extended state observer (LESO) and poor ability to suppress high-frequency noise on the basis of introducing the proportional differential link in the traditional LESO, the differential term of output voltage error is introduced in LESO. In addition, the output of the channel for total disturbance is corrected to improve the disturbance observation ability of LESO against high-frequency noise. The theoretical proof of LADRC and the comparative analysis of Bode plots show that the improved LADRC has better anti-interference performance. Finally, to verify the effectiveness of the control strategy designed in this paper, different types of low-voltage ride-through faults are designed on the grid-side. The simulation results show that the new controller can improve the control performance effectively of the energy storage grid-connected system.

A Subsynchronous Oscillation Suppression Method Based on Self-Adaptive Auto Disturbance Rejection Proportional Integral Control of Voltage Source Converter Based Multi-Terminal Direct Current System with Doubly-Fed Induction Generator-Based Wind Farm Access

A subsynchronous oscillation suppression strategy based on self-adaptive auto disturbance rejection proportional integral controller is proposed for doublyfed induction generator-based wind farm integrated into grid through voltage source converter based multi-terminal direct current. In this strategy, the nonlinear PI controller is constructed by fal function to replace the traditional linear PI controller, and then the tracking differentiator is used to arrange the appropriate transition process in combination with the idea of active disturbance rejection control, and the self-adaptive auto disturbance rejection proportional integral controller is designed. By applying the controller to the inner loop of the converter on the rotor side of the doubly-fed induction generator, the adaptability of the control parameters of the inner loop to the change of operating conditions of the system can be improved, and the dynamic performance of the system can be improved. The simulation results on PSCAD/EMTDC show that, compared with SSDC, when the wind speed is 7.5 m/s, 8.5 m/s and 9.5 m/s, the convergence time can be shortened by 0.2 s, 0.1 s and 0.25 s, respectively. When the number of grid-connected doubly-fed induction generator wind turbines is 200 and 220, the convergence time is shortened by 0.1s. When the self-adaptive auto disturbance rejection proportional integral controller and the multi-channel variable-parameter additional subsynchronous damping controller work together, the convergence time under the above three wind speeds are 2.7 s, 2.7 s and 2.3 s, respectively. When the number of grid-connected doubly-fed induction generator wind turbines is 200 and 220, the convergence time is 2.65 s and 2.75 s, respectively. It can be concluded that the self-adaptive auto disturbance rejection proportional integral controller can realize the effective suppression of the subsynchronous oscillation under different operating conditions of the wind farm via the comparison with the additional subsynchronous damping control of doubly-fed induction generator. Besides, subsynchronous oscillation will converge faster and the stability of the system can be enhanced when the self-adaptive auto disturbance rejection proportional integral controller and the multi-channel variable-parameter additional subsynchronous damping controller work together.

Path Tracking Control Based on the Prediction of Tire State Stiffness Using the Optimized Steering Sequence

This study proposes a linear time-varying model predictive control method based on tire state stiffness prediction for the path tracking using a steering decision sequence in a prediction horizon. A nonlinear UniTire model is employed to represent the nonlinear features of vehicle dynamics in critical situations. And the changing trend of tire state stiffness over the prediction horizon is constructed based on the steering decision sequence, which is the optimized solution of the previous execution step by the controller. Moreover, a method of adjusting the tire state stiffness is proposed to address the jittering in the process of linearization. Meanwhile, a nonlinear model predictive controller and a traditional linear time-varying model predictive controller are designed to verify the effectiveness of the proposed linearization method. Experimental results clearly show that this linearization method can considerably improve vehicle stability under extreme conditions.

Novel Piecewise Linear Formation of Droop Strategy for DC Microgrid

This paper proposes a new load current sharing control strategy for dc microgrid (MG). It adopts the piecewise linear formation to adaptively assign droop gains to balance the load sharing accuracy with the deviation of bus voltage. Communication among the distributed units is unnecessary for the proposed operation. The approach is considered as a comprehensive solution to deal with the unbalanced performance in dc MG for different load levels. The balancing performance is shown by comparing with the traditional linear droop control and the latest nonlinear droop gain solution. The study is proceeded by the in-depth analysis of both steady state and system dynamics with simulation support. The claimed advantage is finally verified by both simulation and experimental evaluations.

Nonlinear Switching Control of the CO Oxidation Reaction Rate in Hydrogen Production

Catalytic CO oxidation on platinum group metals can exhibit nonlinear behaviors like catastrophe, bistability, and hysteresis, which are indicative of self-organizing processes occurring in the course of the oxidation reaction. As a result, the system demonstrates a multi-branch nonlinear input/output relationship for which the output value depends not only on the instantaneous input values, but also on the history of operations. Traditional linear control approaches may cause unstable operation in the CO oxidation reaction. In this paper, a nonlinear control strategy is proposed to solve the control problem. The control strategy incorporates a PI controller and a switching control strategy by which the control system can maintain a high regulating performance while preventing unstable operation. It may be applied to control operations in industrial processes of catalytic CO oxidation.