Traditional Linear Control Research Articles

Path Tracking Control of Intelligent Vehicles Considering Multi-Nonlinear Characteristics for Dual-Motor Autonomous Steering System

In the path tracking control of intelligent vehicles, the traditional linear control method is prone to high tracking errors for uncertain parameters of the steering transmission system and road conditions. Therefore, considering the mechanical friction in the dual-motor autonomous steering system and the nonlinearity of tires, this paper proposes a path tracking control strategy of intelligent vehicles for the dual-motor autonomous steering system that considers nonlinear characteristics. First, a dual-motor autonomous steering system considering mechanical friction and the variation of tire cornering stiffness under different tire–road friction coefficients was established based on the structure of an autonomous steering system. Second, a tire–road friction coefficient estimator was designed based on a PSO-LSTM neural network. The tire cornering stiffness under different tire–road friction coefficients was estimated through the recursive least-square algorithm. Then, the control strategy of the dual-motor autonomous steering system was designed by combining the LQR path tracking controller with the adaptive sliding mode control strategy based on field-oriented control. Here, mechanical friction and the variation of tire cornering stiffness were considered. Finally, simulation and HiL tests validated the method proposed in this paper. The results show that the proposed control strategy significantly improves the tracking accuracy and performance of the dual-motor autonomous steering system for intelligent vehicles.

Nonlinearities compensation of a parallel piezostage using discrete-time sliding mode predictive control with decoupling and damping properties

Parallel piezostages have been prevalently applied in the micro-/nano-positioning field, but, unluckily, suffer from the undesired effects, like creep, hysteresis, vibration, cross coupling, and input saturation. To eliminate these nonlinearities, a new discrete-time sliding mode predictive control (DSMPC) with decoupling and damping properties (DSMPC-DDP) is proposed for a parallel six-axis piezostage devoted to cell micromanipulation. Firstly, a state-space-based decoupler is applied to make the MIMO system decoupled into six SISO subsystems, and then a damping compensator via the notch filter principle is selected to suppress the high-frequency vibration dynamics of six subsystems. After that, a feedback DSMPC law is designed for each decoupled and damped subsystem. Unlike the traditional linear model predictive control (MPC), the feedback DSMPC is developed based on a nonlinear discrete-time terminal sliding mode prediction model, leading to stronger robustness and faster convergence speed. Through the utilization of the receding horizon optimization, the sliding mode states are optimized to explicitly deal with the input constraints. The stability of the closed-loop subsystems is analyzed based on the terminal constraint theory. Finally, the effectiveness and superiority of the proposed controller is validated by a series of experimental tests. Results show that the proposed DSMPC-DDP scheme can achieve higher tracking accuracy and control bandwidth than the existing proportional–integral (PI) and MPC methods in the context of abovementioned nonlinearities.

Observability Decomposition of Boolean Control Networks

This article investigates the observability decomposition of Boolean control networks (BCNs) under the algebraic frame based on the semitensor product of matrices. First, the definition for the observability decomposition of BCNs is proposed, which is consistent with the observability decomposition of traditional linear control systems. Then, by using the vertex partition method, a necessary and sufficient vertex partition condition for the observability decomposition of BCNs is proposed. Next, an algorithm is designed for the analysis and realization of observability decomposition. Finally, a biological example and a counterexample are analyzed to illustrate the obtained results.

Smartenance DC-DC On-Board Power Converters

Traditional marine power grids, using diesel motors/generators, pollute the atmosphere with their CO2 emissions and adverse particles. Nowadays, green energy supplies like solar systems and fuel cells play significant roles in the electrification of marine propulsion plants. In general, marine power systems with green energy units are often considered as a kind of mobile DC microgrids under constant power loads (CPLs). However, since the CPLs impose nonlinearity and negative impedance instability on marine power systems, traditional linear controllers no longer stabilize the system under various disturbances. This motivated this brief to develop a non-integer model predictive control for the accurate control of DC/DC converters with CPLs to stabilize the main bus voltage and current in the full-electric ferry ship. In practice, the stability of ship systems is highly threatened due to the negative-independence effect of CPLs. To address the adverse challenges of CPLs, the deep deterministic policy gradient (DDPG) with actor and critic neural networks (NNs) is applied to effectively design the coefficients embedded in the non-integer MPC. In this approach, the actor NN provides the regulatory signals to adjust the non-integer MPC controller while the critic NN is trained to evaluate the quality of actions of actor NN. The experimental results with comparative analysis are carried out to validate the accuracy of the non-integer MPC under the change in the CPL’s power. The results demonstrated that the proposed controller offers a higher level of robustness than the state-of-art-schemes.

Continuous Switching Control of an Input-Delayed Antagonistic Muscle Pair During Functional Electrical Stimulation

Existing controllers for functional electrical stimulation (FES) of upper limb muscles were initially designed to assist unilateral movements and may not be readily applicable to assist antagonistic muscle movements. Furthermore, it is yet unclear if electromechanical delays (EMDs) are present during the coactivation of muscles. In this article, a robust controller is designed to facilitate the FES of an antagonistic muscle pair during elbow flexion and extension. The controller uses a continuous switching law that maps a joint angle error to control the antagonistic muscle pair. Furthermore, the controller compensates for EMDs in the antagonistic muscle pair. A Lyapunov stability analysis yields uniformly ultimately bounded (UUB) tracking for the human limb joint. The experimental results on four participants without disabilities indicate that the controller is robust and effective in switching between antagonistic muscles. A separate set of experiments also showed that EMDs are indeed present in the coactivated muscle pair. The designed controller compensates for the EMDs and statistically improves root mean square error (RMSE) compared to a traditional linear controller with no EMD compensation. The proposed controller can be generalized to assist FES-elicited tasks that involve a weak antagonistic muscle pair.

Q-learning–based practical disturbance compensation control for hypersonic flight vehicle

Aiming at the attitude control problem of hypersonic flight vehicle, a compound control strategy based on the disturbance compensation technique and Q-learning optimization is proposed. Firstly, a three-channel independent control scheme based on the super-twisting extended state observer (STESO) is proposed such that coupling among channels, uncertainties, and external disturbances can be estimated and compensated in real time. Secondly, the Q-learning mechanism is introduced to optimize the control parameters while keeping the control structure unchanged. Lastly, the convergence of the STESO is analyzed by the Lyapunov theory, and some numerical simulation results are carried out to verify the effectiveness of the proposed strategy. Compared with the traditional linear active disturbance rejection control, the proposed method can avoid a lot of manual parameter tuning process and also has better performance.

Model predictive and compensated ADRC for permanent magnet synchronous linear motors

Traditional linear active disturbance rejection control (LADRC) may have difficulty to achieve a rapid precise disturbance rejection for a permanent magnet synchronous linear motor (PMSLM). By making use of model information, a model predictive and compensated LADRC (MPLADRC) method is proposed in this paper. In this method, a model compensated extended state observer (MESO) is designed to transform the controlled object into an established mathematical model through total disturbance compensation. Meanwhile, considering the delay problem of MESO, a phase advance module is designed to improve the estimation speed of MESO for system disturbance and state, thus the MESO can rapidly compensate various uncertainty disturbances to the controlled object in real time. The model predictive controller (MPC) is then designed based on the mathematical model, and its optimal control law is then obtained through a quadratic objective function to further suppress the disturbance unobserved by the designed MESO. The proposed method can thus realize a dual-degree-of-freedom disturbance rejection through the MESO and MPC. The simulation and experimental results validate the effectiveness of the proposed MPLADRC in rapid anti-disturbance and fast positioning for the motion control of the PMSLM.

Sliding-Mode Control of Full-State Constraint Nonlinear Systems: A Barrier Lyapunov Function Approach

This study presents the design of a robust control based on the sliding-mode theory to solve both; the stabilization and the trajectory tracking problems of nonlinear systems subjected to a class of full-state restrictions. The selected nonlinear system satisfies a standard Lagrangian structure affected by nonparametric uncertainties. A barrier Lyapunov function is used to ensure the state constraints by designing a time-varying gain, which guarantees the fulfillment of the predefined state constraints even under external perturbations. The proposed design methodology for the barrier sliding-mode control (BSMC) ensures the convergence of the sliding surface in finite time to the origin. Consequently, the asymptotic convergence of the states to the corresponding equilibrium point is achieved. The finite-time stability of the origin in the closed-loop system with the proposed controller has been demonstrated using the second Lyapunov stability method. The suggested controller was evaluated on a two-link robotic manipulator. Then, the obtained results showed better stabilization and tracking performances (while the restrictions are satisfied) than the traditional first-order sliding-mode or linear state feedback controllers.

Research on Adaptive Exponential Droop Control Strategy for VSC-MTDC System

To solve the problem of large DC voltage deviation caused by the power fluctuations and poor power distribution characteristics of converters in a voltage source converter multi-terminal DC (VSC-MTDC) system based on traditional droop control, this paper proposes an adaptive exponential droop control strategy. This strategy introduces the relative power deviation factor of the converter, and replaces the traditional linear droop control curve with a nonlinear exponential curve. Under different working conditions, the converter adaptively adjusts the droop control coefficient of the converter according to the relative power deviation factor to realize stability for the DC voltage and a reasonable power distribution for the MTDC system. A simulation model of a three-terminal VSC-MTDC was established in MATLAB/Simulink, and the feasibility and effectiveness of the proposed strategy were verified.

An Adaptive Dynamic Reference Control for Power Converters in a Microgrid

This article proposes an adaptive dynamic reference (ADR) control for power converters in a microgrid. Conventionally, two separate controllers are required to control the input current and the dc bus voltage. The proposed ADR control eliminates the need for an additional controller by utilizing a simple discrete-time model to generate appropriate input current reference for the dc bus voltage regulation. The proposed technique is easy to design and guarantees a dynamic convergence of the dc bus voltage to its nominal reference even during nonidealities in the system, such as model parameter uncertainties, sensor imperfections, and unmodeled dynamics. A simple design procedure of the control parameters for the desired converter response is also provided based on a theoretical analysis. Unlike traditional linear controllers, the control parameter design of the proposed model is independent of the converter’s operating point. Finally, the performance of the proposed technique is validated experimentally by using a finite control set model predictive control of a typical grid-connected application and is compared with both conventional dynamic reference control and traditional linear controller.

Bus Voltage Control of Photovoltaic Grid Connected Inverter Based on Adaptive Linear Active Disturbance Rejection

According to the traditional voltage and current double closed-loop control mode, the inverter management strategy for photovoltaic grid connection has insufficient anti-interference ability and slow response. This paper proposes a control strategy that applies adaptive-linear active disturbance rejection control (A–LADRC) to the outer loop control to achieve the purpose of anti-interference. The control strategy uses the linear extended state observer (LESO) to evaluate external interference caused by the change of external conditions and the internal disturbance caused by parameter uncertainty. PD controller compensates the disturbances and adds adaptive control to simplify parameter adjustment. Finally, this paper takes advantage of Lyapunov theory to conduct stability analysis. Compared with the traditional linear active disturbance rejection control (LADRC), the superiority of this control strategy is verified. The experimental results show that the system has better control performance and anti-interference ability in the face of various disturbances.

Performance evaluation of model predictive control method for neutral point clamped inverter

The neutral-point clamped (NPC) inverter is a popular three-level converter topology used in motor drive applications and other dc/ac converter systems. In this paper, the performance evaluation of the model predictive control is performed to investigate its applicability in controlling NPC inverters. The model predictive control (MPC) is a promising closed-loop control strategy in applications where multiple control goals are considered. The ease of adding the objectives to the control law improves the reputation of the MPC. The numerous control goals can be regulated in single feedback. Thus, the adequate bandwidth is noticeably higher compared to the traditional linear controllers. However, controlling the multiple objectives require the use of weighting factors to tune the system performance. Regarding the system performance, multiple reference tracking performance is investigated in this study. Our case study considers three control goals: output load current, switching frequency control, and capacitor voltage balancing. The predictive control is designed to regulate these dynamics, and comprehensive performance analyses are performed. The designed controller is tested using a simulation tool. The simulation results prove that predictive control offers an excellent multi-objective control performance provided that the weighting factors and other design parameters are finely adjusted. The poor selection of the design parameters affects the closed-loop performance, and the conducted analyses show the effects of the controller parameters.

Adaptive Sliding-Mode Control for Electric Spring in Microgrids with Distributed Renewable Energy

Electric springs (ESs) are novel electric-power devices that alleviate power-quality problems such as voltage fluctuations induced by grid access to renewable energy resources. However, with the continuous increase of uncertain factors such as parameter perturbation and external disturbance, the environment becomes more complicated, so traditional linear controllers for ESs are finding it increasingly difficult to meet the control requirements due to narrow stability regions, low precision, and poor robustness. To overcome this problem, we propose herein a control method that combines adaptive control and sliding-mode control and apply it to ESs. First, an inexact model of the ES system was established and analyzed. Next, an ES control system was designed based on adaptive sliding-mode control, and then the asymptotic stability of the closed-loop system is proven. Finally, the proposed control system was verified through a MATLAB simulation. The results show that adaptive sliding-mode control not only ensures the voltage stability of critical loads in the microgrid but also resists the influence of parameter perturbation and external disturbances, leading to better steady-state and dynamic performance than a linear controller.

A coupled numerical framework for hybrid floating offshore wind turbine and oscillating water column wave energy converters

Integrating floating offshore wind turbines with oscillating-water-column wave energy converters has been seen as a promising solution for hybrid offshore renewable energy production, as the cost-effective wave energy devices could possibly help increase the overall power absorption, reduce platform dynamic responses, and mitigate loads for critical wind turbine structures etc. As most existing research works on dynamic analysis of these hybrid concepts are based on frequency-domain simulations or scale model experiments, this work focuses on establishing an aero-hydro-elastic-servo-mooring coupled numerical framework for integrated time-domain dynamic analysis. In particular, the water column dynamics are characterised based on an equivalent virtual oscillating body approach so that the time-domain analysis capability for oscillating-water-columns with power take-off control is enabled. For validation, a novel combined concept is designed, and its time-domain numerical results under various environmental conditions have been compared against the 1:50 scale model wave basin test data. Good agreement has been observed between the numerical and experimental results, demonstrating the feasibility of the proposed numerical framework. Furthermore, different power take-off control strategies for the oscillating-water-column wave energy converters have been proposed, and it is found that the designed gain-scheduling control schemes are more beneficial for mitigating the platform motion responses and wind turbine structural loads compared with traditional linear damping control, resulting in 15% platform pitch motion mitigation and 6% tower base fatigue load reduction. Further studies on multi-objective optimal power take-off control design regarding both load reduction and power maximisation could be conducted for hybrid energy platforms based on the established numerical framework.

Trajectory planning and vibration control of translation flexible hinged plate based on optimization and reinforcement learning algorithm

Aiming at the flexible hinged plate with translational motion,a hybrid control strategy combining motion trajectory optimization and piezoelectric active control is developed. Combining finite element modeling (FEM) and experimental system identification, the piezoelectric driving model and motor acceleration driving model of the flexible hinged plate are obtained. On this basis, the immune optimization algorithm is adopted to obtain the optimal trajectory of vibration suppression. An Auto-Soft-Actor-Critic (Auto-SAC) reinforcement learning (RL) algorithm is designed to train modal active controllers. The experimental setup is constructed. Compared with the classical trajectory, the optimized trajectory shows a good effect in avoiding excitation vibration. For piezoelectric active control, the Auto-SAC RL modal controller is superior to the large-gain PD controller in suppressing vibration, and its nonlinearity effectively overcomes the problem that the traditional linear controller has limited ability to suppress small amplitude vibration. In addition, the hybrid control strategy of trajectory optimization and Auto-SAC RL modal controllers can effectively reduce and suppress the vibration of the flexible hinged plate in translational motion.

Dynamic trajectory tracking method of UAV using L1 Adaptive Control

Abstract An attitude control method based on the L1 adaptive structure was proposed for the quadrotor aircraft with nonlinear, strong coupling, and underactuated characteristics during flight. Then introducing a dynamic point trajectory algorithm to solve the problem that traditional linear controller lacks robustness in the quadrotor aircraft. The adaptive controller can quickly estimate the state of the system then track and compensate for interference items. The dynamic point trajectory algorithm was able to calculate the expected trajectory and trajectory deviation of the disturbed aircraft, which improved the anti-interference ability and fault tolerance ability of the system. The simulation results indicate that the flight control system designed based on the L1 adaptive dynamic point method is qualified for achieving high-precision trajectory tracking control and the system has strong anti-interference capability and robustness in the nonlinear state.

Design and Analysis of a Non-Contact Tension Testing Device Based on Magnetic Levitation

To facilitate mechanics testing in special environment, in this paper, a non-contact tension testing device was developed based on magnetic levitation technology. To find a floator that facilitates the alignment of tension force, electromagnetic analyses were performed using J-Mag software and a ring floator was found to be self-aligning. In addition, since the levitated objects need to bear a tension force, which will cause the nonlinearity of the magnetic levitation system to emerge, to address the nonlinear issue, a nonlinear mathematical model was established, and a centralized feedback linearization control algorithm was proposed. Furthermore, a tuning method for the control algorithm was proposed to deal with the mismatches between the controller and the plant. Moreover, a model for estimating specimen elongation was developed using support vector machine (SVM), the estimation results demonstrated that the range of the estimation error was between −0.1988mm and 0.2269mm, the root mean square error (RMSE) and coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) were 0.0843mm and 98.76% respectively. Ultimately, a levitation experiment and a tension experiment were successfully performed, the levitation experiment results demonstrated that the proposed tuning method is effective and the centralized feedback linearization controller has stronger robustness to step disturbance than the traditional linear controller. The tension experiment results indicated that the whole control system copes well with an increasing tension force.

Variable Pitch Control of Wind Turbine Based on Fuzzy Feedforward and Feedback Linearization Sliding Mode

When the wind turbine operates above the rated wind speed, the pitch angle is usually adjusted to make the output power stable at the rated value. As the traditional linear controller is difficult to achieve better control effect for the large inertia and strong nonlinear wind power generation system, a variable pitch control method combining fuzzy feedforward and feedback linearization sliding mode is proposed in this study. Firstly, the nonlinear wind power model is globally linearized by input/output feedback linearization, and then the feedback controller is designed by combining with sliding mode control. The fuzzy feedforward controller can give the appropriate pitch angle according to the change of wind speed and realize dynamic feedforward compensation. The simulation results show that the proposed control method can make the output power of the wind turbine stable, and has better control accuracy and robustness.

A review on model predictive control techniques applied to hierarchical control of AC microgrids

A microgrid (MG) is an advantageous option in the integration of distributed generation and in smart grid networking solutions. MG does not implement any fixed topologies. Therefore, a sophisticated control system with a good dynamic response, easy inclusion of constraints with nonlinearities, resiliency in case of system parameter changes, and stability are required. Traditional linear cascade control does not meet the above requirements. It also requires a complex tuning mechanism for changes in the system and also lags in fast dynamic response. The present trend is to focus on finite control set model predictive control (FCS-MPC) applied to power electronics, which acts as an alternative option compared to the century-old linear cascade control strategy. In this review article, FCS-MPC is reviewed for AC MG hierarchical control. Survey of MPC control strategy on converter level, grid-level their limitations and design constraints are reviewed as well as challenges of MPC applied to hierarchical control for AC MG environment are also discussed.

Large-Signal Stability Analysis and Design of Finite-Time Controller for the Electric Vehicle DC Power System

In the electric vehicle dc power system (EVDCPS), a motor or converter can be regarded as a constant power load (CPL) when tightly controlled. Different from the resistive load, CPL may make the EVDCPS unstable. Thus, in addition to less overshoot and shorter setting time, the controller of the converter applied in EVDCPS should maintain the system stable. In this article, the finite-time controller (FTC) is proposed based on exact feedback linearization (EFL) to stabilize the EVDCPS feeding CPL. First, to handle the nonlinearity caused by the CPL, the affine nonlinear model of EVDCPS is transformed into the Brunovsky canonical form by using EFL. Then, the finite-time control is adopted and a PI compensator is designed to eliminate the steady-state error. Under the proposed control algorithm, rigorous proofs indicate that not only the closed system can be large-signal stable, but also the EVDCPS bus voltage can converge the reference in finite time. Furthermore, to reduce the design difficulty of FTC, the backstepping method is adopted to design a backstepping finite-time controller(BFTC). Finally, simulations and experiments of FTC and BFTC are carried out. Results indicate that both FTC and BFTC have large-signal stability, and although the robustness of BFTC is inferior to FTC, the setting time and overshoot are greatly improved compared with the traditional linear feedback controller.