Advanced Nonlinear Control Research Articles

Control of a Directional Downhole Drilling System Using a State Barrier Avoidance Based Method

Abstract Vibrations such as bit-bouncing, stick–slip, and whirl motion can significantly reduce downhole drilling's performance and efficiency. These phenomena are likely to arise once the dynamical states of drilling fall into undesired operating regimes, as verified by both theoretical analyses and experimental studies. To effectively avoid such undesired operating conditions of a downhole drilling process, this paper introduces an advanced nonlinear state-constrained control method to formulate a setpoint tracking problem of high-order directional drilling dynamics under state constraints. These constraints are carefully defined using the field test results, and their shapes are in complex state-space regions, causing additional complexity to the control design. Moreover, unlike vertical drilling, the directional drilling system requires modeling of coupled axial and torsional dynamics with a higher degree-of-freedom (DOF). In this study, the proposed method will tackle these challenges by converting the original high-order constrained control problem into a standard nonlinear control problem. Leveraging on the linear parameter varying (LPV) method that is applied to the converted system, we can actively prevent drilling from falling into the undesired regimes, to achieve the constrained control objective.

Advanced nonlinear control of WEC system in AC microgrid connected to the main grid with electric vehicle integration

This research investigates an AC microgrid interconnected with the utility grid, comprising a 5.2 kW wind power system using a Permanent Magnet Synchronous Generator (PMSG), a 6.1 kW photovoltaic system (PVS), and integrated electric vehicles (EVs) with 600 V, 10Ah batteries operating in V2G and G2V modes. Bidirectional back-to-back converters, controlled via a voltage-oriented control (VOC) strategy, facilitate the interconnections. The PMSG system is controlled using vector control methodology, while the PVS employs a double-stage configuration with a perturb and observe algorithm for maximum power point tracking (MPPT). Advanced controllers based on Active Disturbance Rejection Control (ADRC) regulate generator speed and the DC link voltage of the grid-connected converters. The backstepping technique is utilized for internal loop control of PMSG currents and coupling filter currents for the grid-connected converters. A comparative study between the ADRC-Backstepping controller and the PI controller was conducted, demonstrating the superior efficacy of the ADRC-Backstepping approach. Simulation results show accurate regulation of DC link voltage at reference levels, operation with unit power factor, with a current THD below 4 %, demonstrating the effectiveness and robustness of the proposed control strategies under varying conditions.

Particularities of Rotorcraft in Dealing with Advanced Controllers

Advanced nonlinear controllers are a desirable solution to rotorcraft flight control as they can solve the system high nonlinear dynamic behavior. However, conventional nonlinear controllers such as Nonlinear Dynamic Inversion (NDI) controller heavily rely on the availability of accurate model knowledge and this can be problematic for rotorcraft. Therefore, incremental control theory can solve the modelling errors sensitivity by relying on the information obtained from the sensors instead. The paper applied the Incremental Nonlinear Dynamic Inversion (INDI) controller to rotorcraft case. It will be demonstrated that, for rotorcraft, the incremental nonlinear controllers depend on the delays introduced in the controller by the rotor dynamics. To correct this behaviour, residualization and synchronization methods need to be applied accordingly in order to remove the effects of rotor flapping (disctilt) dynamics from the controller. These particularities of rotorcraft in dealing with advanced controllers shows that incremental nonlinear controllers can have relatively small stability robustness margin and careful controller design is needed in order to account properly for rotorcraft time delays and unmodelled dynamics.

Survey of Advanced Nonlinear Control Strategies for UAVs: Integration of Sensors and Hybrid Techniques.

This survey paper explores advanced nonlinear control strategies for Unmanned Aerial Vehicles (UAVs), including systems such as the Twin Rotor MIMO system (TRMS) and quadrotors. UAVs, with their high nonlinearity and significant coupling effects, serve as crucial benchmarks for testing control algorithms. Integration of sophisticated sensors enhances UAV versatility, making traditional linear control techniques less effective. Advanced nonlinear strategies, including sensor-based adaptive controls and AI, are increasingly essential. Recent years have seen the development of diverse sliding surface-based, sensor-driven, and hybrid control strategies for UAVs, offering superior performance over linear methods. This paper reviews the significance of these strategies, emphasizing their role in addressing UAV complexities and outlining future research directions.

Trajectory Tracking Nonlinear Hybrid Control of Automated Guided Vehicles

Automated guided vehicles (AGVs), so necessary in industrial environments, require precise control of trajectory tracking to make accurate stops at logistics stations, such as loading stations, or to pick up or drop off trolleys, pallets, or racks. This paper proposes a hybrid control architecture for trajectory tracking of a hybrid tricycle-differential AGV. The control strategy combines conventional proportional integral derivative (PID) control with advanced nonlinear Lyapunov control (LPC). The LPC is used for trajectory tracking while the PID is used for speed control of the robot. The stability of the controller is demonstrated for any differentiable trajectory. When a PID optimized with genetic algorithms is compared with the proposed controller for several trajectories, the LPC outperforms it in all cases.

Propelling precision of longitudinal vibration mitigation in ship propeller shafts through advanced nonlinear intelligent semi-active control leveraging adaptive neuro-fuzzy inference system with linear quadratic regulator

This study presents a pioneering method to address longitudinal vibrations in ship propeller shafts by integrating an intelligent semi-active control system. By leveraging Adaptive Neuro-Fuzzy Inference System (ANFIS) and Linear Quadratic Regulator (LQR) techniques, the proposed system offers a novel solution to a longstanding challenge in maritime engineering. The efficacy of the semi-active control system is assessed through two complementary analyses: frequency domain analyses and Percentage Reduction Index (PRI) values. The investigation reveals substantial PRI values, ranging from 24.68% to 46.87%, indicating notable enhancements in vibration control with the adoption of semi-active technology. Furthermore, frequency domain analyses demonstrate significant advantages, with a peak reduction of 68.21% observed at 750 r/min. To rigorously evaluate the statistical significance of these improvements, paired t-tests were conducted between the semi-active control system and passive strategies. The results confirm a statistically significant difference in vibration reduction ( p < .05) between the two systems. Specifically, for Percentage Reduction Index Values, the t test yielded a p-value of .023, while for Frequency Domain Analyses, the t-statistic significantly exceeded the critical value (t ≈ 8.09, tcritical ≈ 2.571). Both data sets provide compelling evidence to reject the null hypothesis, indicating a significant difference in vibration reduction between the semi-active control system and passive strategies. The PRI values consistently demonstrate superior performance for the semi-active control system, highlighting its effectiveness in mitigating vibrations. These findings validate the effective ness of the system. These results show the practical implications of implementing semi-active control systems in maritime engineering, offering enhanced safety, fuel efficiency, and operational reliability in ship propeller shaft applications.

Integral Backstepping Sliding Mode Control for Maximizing the Power Production of Wind Turbines

Wind turbine control has attracted increasing attention, driven in part by evolving challenges due to the growing size and complexity of wind turbines. Addressing these challenges and maximizing wind turbine power production requires the application of advanced nonlinear control methods. Sliding Mode Control (SMC) has emerged as a promising approach in this context. Recent studies have explored the integration of an integral term with SMC, called I-SMC. This technique has been shown to result in system responses that exhibit chattering phenomena with noticeable state errors. This study aimed to address these issues through the introduction of a novel controller known as Integral Backstepping SMC (IB-SMC). This study demonstrated that IBSMC not only ensured the stability of wind turbines but also outperformed other control strategies, even in the presence of disturbances of approximately 30% of the rated electromagnetic torque. To validate the effectiveness of the proposed controller, extensive simulation tests were carried out using MATLAB /Simulink software to evaluate the controller's responsiveness to rapid changes in conditions, as well as its robustness and overall performance. A comparison was carried out between the IBSMC and previous SMCs to evaluate their ability to reduce steady-state error and chattering.

An advanced observer-based nonlinear model predictive controller design for distribution networks in bushfire prone areas

An advanced observer-based nonlinear model predictive controller design for distribution networks in bushfire prone areas

DSP-based Control of Standalone PV/Battery Hybrid System

This paper investigates and presents the control and energy management of a typical standalone photovoltaic/battery hybrid system. The studied system in this paper includes two DC-DC converters. The first is a DC-DC boost converter works as a maximum power point tracker for the PV array, while the other is a bidirectional DC-DC converter used to regulate the DC bus voltage and to provide the necessary energy to the load by compensating the power deficit continuously. Generally, the developed control strategies in literature for such a system are based on proportional-integral (PI) controllers due to their simplicity of implementation, or by using some advanced nonlinear control concept, however this latter requires knowledge of system parameters and exact mathematical model for the design phase, which is sometimes difficult to obtain and makes the control technique sensitive to parameter variations. In order to overcome the previously mentioned drawbacks, this paper presents a model-free control technique based on a simplified version of fuzzy logic controller (FLC), this approach allows to keep the same implementation simplicity of PI controller and to considerably reduce the complexity of the conventional FLC. Experimental studies on a test bench were conducted to prove the effectiveness of the proposed approach.

Advanced Nonlinear Control of Shunt Active Power Filter Connected to PV System via DC/DC Converter

We address the challenge of controlling a single-phase shunt active power filter in the presence of nonlinear loads. The active power filter is linked to a photovoltaic (PV) system, which supplies active power to the load. The main control objectives are: (i) mitigation of harmonics current and reactive power induced by the non-linear load; (ii) regulation the PV generator output voltage to follow its reference provided by the Maximum Power Point Tracking (MPPT) block, (iii) regulation the DC bus voltage to a constant value that must be greater than the amplitude of the grid voltage in order to guarantee full power injection to the non-linear load. To solve this problem, we use a nonlinear multi-loop controller. The inner loop is designed using the Backstepping method to cope with compensation problems. The external loop is responsible for regulating the dc bus voltage. The DC-DC boost control loop is designed to achieve the MPPT objective. The efficiency of the proposed controller is validated by simulation in MATLAB/Simulink.

Event-triggered multi-time scale control and low carbon operation for electric-hydrogen DC microgrid

This paper addresses the escalating need for low-carbon solutions and discusses the integrated control system for the electric-hydrogen DC microgrids. A multi-time scale control mechanism for the DC microgrid is proposed, which aims to enhance its adaptability and scalability. The proposed multi-time control scheme comprises two control layers. The system-level control layer utilizes optimization to address economic issues and carbon trading, aiming to minimize operational costs and carbon emissions in the microgrid. The local-level control layer incorporates an advanced nonlinear control method based on the exponential reaching law-powered terminal sliding mode control (ERL-TSMC) for accurate tracking capabilities at the local-level and to handle dynamic control challenges in real-time. Furthermore, an event-triggered control mechanism is introduced to reduce considerable control efforts while guaranteeing system robustness under various disturbances. Both simulations and hardware experiments demonstrate that the proposed control method guarantees efficient low-carbon operation, achieving a significantly lower average carbon intensity of 0.19 kg CO2/kWh. Additionally, it exhibits robust performance at the local-level, with a steady-state error of only 0.03%.

Robust performance comparison of PMSM for flight control applications in more electric aircraft.

This paper describes a robust performance comparison of flight control actuation controllers based on a permanent magnet synchronous motor (PMSM) in more electric aircraft (MEA). Recently, the PMSM has become a favorite for the flight control applications of more electric aircraft (MEA) due to their improved efficiency, higher torque, less noise, and higher reliability as compared to their counterparts. Thus, advanced nonlinear control techniques offer even better performance for the control of PMSM as noticed in this research. In this paper, three nonlinear approaches i.e. Feedback Linearization Control (FBL) through the cancellation of the non-linearity of the system, the stabilization of the system via Backstepping Control (BSC) using the Lyapunov candidate function as well as the robust performance with chattering minimization by applying the continuous approximation based Sliding Mode Control (SMC) are compared with generalized Field-Oriented Controller (FOC). The comparison of FOC, FBL, BSC and SMC shows that the nonlinear controllers perform well under varying aerodynamic loads during flight. However, the performance of the sliding mode control is found superior as compared to the other three controllers in terms of better performance characteristics e.g. response time, steady-state error etc. as well as the control robustness in the presence of the uncertain parameters of the PMSM model and variable load torque acting as a disturbance. In essence, the peak of the tolerance band is less than 20% for all nonlinear and FOC controller, while it is less than 5% for SMC. Steady state error for the SMC is least (0.01%) as compared to other three controllers. Moreover, the SMC controller is able to withstand 50% parameter variation and loading torque of 10 N.m without significant changes in performance. Six simulation scenarios are used to analyze the performance and robustness which depict that the sliding mode controller performs well in terms of the desired performance for MEA application.

Rule-Based Safe Probabilistic Movement Primitive Control via Control Barrier Functions

In this paper, we develop a novel and safe control design approach that takes demonstrations provided by a human teacher to enable a robot to accomplish complex manipulation scenarios in dynamic environments. First, an overall task is divided into multiple simpler subtasks that are more appropriate for learning and control objectives. Then, by collecting human demonstrations, the subtasks that require robot movement are modeled by probabilistic movement primitives (ProMPs). We also study two strategies for modifying the ProMPs to avoid collisions with environmental obstacles. Finally, we introduce a rule-base control technique by utilizing a finite-state machine along with a unique means of control design for ProMPs. For the ProMP controller, we propose control barrier and Lyapunov functions to guide the system along a trajectory within the distribution defined by a ProMP while guaranteeing that the system state never leaves more than a desired distance from the distribution mean. This allows for better performance on nonlinear systems and offers solid stability and known bounds on the system state. A series of simulations and experimental studies demonstrate the efficacy of our approach and show that it can run in real time. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This paper is motivated by the need to create a teach-by-demonstration framework that captures the strengths of movement primitives and verifiable, safe control. We provide a framework that learns safe control laws from a probability distribution of robot trajectories through the use of advanced nonlinear control that incorporates safety constraints. Typically, such distributions are stochastic, making it difficult to offer any guarantees on safe operation. Our approach ensures that the distribution of allowed robot trajectories is within an envelope of safety and allows for robust operation of a robot. Furthermore, using our framework various probability distributions can be combined to represent complex scenarios in the environment. It will benefit practitioners by making it substantially easier to test and deploy accurate, efficient, and safe robots in complex real-world scenarios. The approach is currently limited to scenarios involving static obstacles, with dynamic obstacle avoidance an avenue of future effort.

A Backstepping Control Strategy for Power System Stability Enhancement

Secure power system operation relies extensively on the analysis of transient stability and control. The dynamics involved in power system control are often complex and nonlinear. Most of the currently existing works approach these frequent problems with nonlinear control techniques, leading to a requirement for specific controller parameter adjustments. In these veins, this paper proposes a new method for stabilizing electric power systems, using nonlinear backstepping control by optimizing the controller’s parameters. The Jaya algorithm and Genetic algorithm are utilized as a powerful meta-heuristic optimization technique to search parameters of an optimal controller. Improvement in system damping, transient stability, and voltage regulation has been achieved by minimizing the integral time absolute error (ITAE) as the objective function. Numerical simulations on an SMIB power system under different fault conditions showed that the proposed method outperforms classical power system stabilizer (PSS) methods, reducing overshoots and settling times and eliminating steady-state errors. These findings highlight the effectiveness of the proposed approach and its potential contribution to the development of advanced nonlinear control techniques for electric power systems. The suggested optimization methods demonstrate superior performance, compared to classical methods, and achieve a reduction of 27.5% in overshoot and 87% in transient time in addition to complete elimination of static error.

Incremental adaptive fixed‐wing unmanned aerial vehicle maneuver control with nonlinear disturbance observer: A finite‐time approach

AbstractThis article proposes a versatile controller based on the incremental strategy to explore a finite‐time (FT) solution of fixed‐wing unmanned aerial vehicle (UAV) maneuver control considering the lumped disturbances such as unmodeled dynamics or exogenous perturbations. First, a finite‐time nonlinear disturbance observer augmented with a neural network item is developed to attenuate the adverse effect of the disturbances mentioned above. Then considering the estimated lumped disturbance, a finite‐time incremental controller in the quaternion form is proposed. Rigorous proof shows that the nonlinear disturbance observer and controller achieve finite‐time convergence. In the numerical simulation, the superiority of the proposed method compared to several advanced nonlinear control approaches is illustrated, despite the mismatches in aerodynamic coefficients, wind gusts, and actuator failures. Furthermore, based on a novel model identification approach, a hardware‐in‐the‐loop (HITL) experiment is performed in a high‐fidelity environment, wherein our method's feasibility and practicability are verified.

Enhancement of LVRT Ability of DFIG Wind Turbine by an Improved Protection Scheme with a Modified Advanced Nonlinear Control Loop

One of the problems with the doubly-fed induction generator (DFIG) is its high vulnerability to network perturbations, notably voltage dips, because of its stator windings being coupled directly to the network. As the DFIG’s stator and rotor are electromagnetically mated, the stator current peak occurs during a voltage dip causing an inrush current to the critical converter back-to-back and an overload of the DC-link capacitor. For this purpose, a series of researchers have achieved a linear and non-linear controller with a crowbar-based protection scheme. With this type of protection, the Rotor Side Converter (RSC) is disconnected momentarily, and consequently, its control of both the active and reactive output power of the stator is totally lost, leading to incorrect power quality at the point of common coupling (PCC). In this document, a robust nonlinear controller by Advanced Backstepping with Integral Action Control (ABIAC) is initially employed to monitor the rotor and the network side converters under normal network operations. In the presence of a network fault, an improved protection scheme (IPS) is tacked on to the robust nonlinear control to help enforce the behavior of the DFIG system to be able to overcome the fault. The IPS, which is formed by a crowbar and an RL series circuit, is typically located in the space between the rotor coils and the RSC converter. Compared to a standard crowbar, the developed scheme is successful to limit the rotor transient current and DC-link voltage, also an RSC disengagement to rotor windings can be prevented during the fault. Furthermore, the controllers of both the RSC and the Network Side Converter (NSC) are modified to boost the supply voltage at the PCC. A comparative study is also performed between the IPS without and with modification of the reactive power control loops. The simulation results mean that with the modified controllers during the fault, the amount of reactive power sustainment with ABIAC at the PCC is optimized to 17.5 MVAr instead of 15 MVAr with proportional-integral control (PIC). Therefore, the voltage at the PCC is fort increased in order to comply with the voltage requirements of the farm and absolutely to maintain the connection to the network in case of voltage dip.

Advanced non-linear control based on artificial intelligence tuner for hand tremor suppression

One of the most common symptoms among diverse patients (e.g. Parkinson’s disease, essential tremor) is hand tremor. Deep brain stimulation (DBS) has been a popular method of controlling hand tremor in recent years. Although recent advancements in the DBS system have enabled DBS applications in the context of various neurodegenerative disorders, the deployment of such interface tactics in DBS applications is seriously threatened by un-adaptability concerns and a lack of an accurate model. In response to these issues, the current letter discusses the construction of an intelligent technique for minimizing hand tremor and self-adaptive DBS settings without requiring a proper hand tremor system modeling setup. For this purpose, the hand tremor is suppressed using an intelligent proportional–integral (iPI) as the main controller and the sliding mode (SM) as an observer. Furthermore, as an auxiliary controller, a deep reinforcement learning (DRL) method with the Actor-Critic structure is adaptively incorporated. The feedback parameters of the ultra-local model (ULM) controller will be adaptively modified utilizing the DRL method by online neural network (NN) updating in the proposed strategy. The advantages of the ULM based on DRL structure over state-of-the-art approaches are demonstrated by a complete assessment.

High-Gain Observer-Based Advanced Nonlinear Control of a Grid-Connected Wind Energy Conversion System with Sensorless Maximum Power Point Tracking

This paper deals with the control development of a wind energy conversion system (WECS) interfaced to a utility grid by using a doubly fed induction generator (DFIG), a back-to-back (B2B) converter and an RL filter for optimal power extraction. The aim was to design a sensorless controller to improve the system reliability and to simultaneously achieve the regulation of the generator speed, reactive power and DC-link voltage. The proposed global control scheme combines: (i) a high-gain observer employed to estimate the generator speed and the mechanical torque, usually regarded as accessible, (ii) a sensorless MPPT block developed to provide optimal generator speed reference, which is designed on the basis of the mechanical observer and a polynomial wind-speed estimator and (iii) a finite-time controller (FTC) applied to the B2B converter to meet the output reference’s tracking objectives in a short predefined finite time by using the backstepping and Lyapunov approaches. The proposed controller performance is formally analysed, and its capabilities are verified by numerical simulations using a 2 MW DFIG wind turbine (WT) under different operating conditions.

DC Shipboard Microgrids With Constant Power Loads: A Review of Advanced Nonlinear Control Strategies and Stabilization Techniques

In modern dc shipboard microgrid (SMG) systems, the propulsion motors and hotel loads are always supplied through tightly regulated point of load converters, which behave as constant power loads (CPLs). The negative incremental impedance due to CPL’s characteristics destabilizes the dc bus voltage of dc SMGs. Due to uncertain operating conditions of maritime ships on the sea, the dc bus voltage robust control is a crucial matter. Therefore, this paper presents a cutting-edge systematic review on advanced nonlinear control strategies to stabilize and control the CPLs in dc SMGs, such as sliding mode control, synergetic control, backstepping control, model predictive control, and passivity-based control. The latest stabilization techniques and the future trends towards an adaptive nonlinear control have been presented throughout this review. Several feedforward control-based observation and estimation techniques have been highlighted. The stability analysis and stability challenges of dc SMGs are also discussed.

Fractional Order Modeling of PWR Pressurizer Dynamics and Fractional Order Nonlinear H∞ Controllers Design in LabVIEW

The novel fractional order intelligent transient dynamics and advanced fractional order nonlinear robust control synthesis scheme of the Pressurized Water Reactor (PWR) pressurizer are addressed in this research work. The Graphical User Interface (GUI) is designed for closed-loop model-based PWR pressurizer dynamical studies in LabVIEW. Based on the demand for power, the reactor power and turbine power are predicted using a fractional order backpropagation algorithm in an open loop configuration. Using turbine power and heater power as input variables, pressurizer level, pressurizer pressure and coolant average temperature as output variables, the open loop multi-input multi-output (MIMO) dynamic model of pressurizer is estimated using fractional order artificial intelligence in LabVIEW. Four fractional order robust nonlinear H∞ sub-controllers are designed for charging flow, spray flow, proportional heaters power and backup heaters power. All the dynamic controller models are in fractional order nonlinear H∞ framework and are designed in LabVIEW. The performance of the proposed design work is evaluated in closed loop configuration at 100 %, 75 % and 15 % in steady state conditions. Dynamic transient analysis is performed from 100% to 90% power reduction scenario and found satisfactory and within design limits and robust bounds.