Nonlinear Control Research Articles

Advanced golden jackal optimization-based fractional-order integral terminal sliding-mode (FO-ITSM) controller for level control of the conical tank system

This article examines a conical tank nonlinear system's level control. A crucial component of facilities like milk processing plants and sugarcane mills is the conical tank. The fluid drains entirely due to the conical shape of the tank. The conical form of the system causes nonlinear dynamics. The conical tank nonlinear system transfer mechanism is not easily accessible, and different operating heights require different transfer mechanisms, making controller design difficult. Developing the Fractional-Order Integral Terminal Sliding-Mode (FO-ITSM) Controller system for Advanced Golden Jackal Optimisation (GJO) based conical tank systems is the main objective of this paper. This is utilised to deliver real-time liquid level management in non-linear conical systems. The dynamics of these models make it possible to identify the conical tank system that produces control signals from liquid samples taken at reference levels with greater accuracy. This system, which is employed on multiple platforms, maintains and controls the liquid level while meeting all design specifications, including time constant, no overshoot, less settling, and rise time. For research simulation work, MATLAB software's FOMCON toolbox is utilised.

Nonlinear Robust Stabilization of Ship Roll by Convex Optimization

For ship roll stabilization, this paper presents a nonlinear robust controller design method using the sum of squares (SOS) technique combined with the dual of Lyapunov's stability theorem. Varying ship speed and initial metacentric height are seen as uncertainties, sea waves are regarded as external disturbance, and then the robust nonlinear controller is also designed based on the suggested method. Simulations are performed by taking container ship model as an example. It is shown that the designed system guarantees robust performance with respect to the uncertainties and disturbance.

Probabilistic Forecasting-Based Stochastic Nonlinear Model Predictive Control for Power Systems With Intermittent Renewables and Energy Storage

Probabilistic Forecasting-Based Stochastic Nonlinear Model Predictive Control for Power Systems With Intermittent Renewables and Energy Storage

Large-Signal Stabilization of Interleaved Multilevel Boost Converter Interfaced High-Power DC Microgrid Using an Adaptive Observer-Based Nonlinear Control Scheme

Large-Signal Stabilization of Interleaved Multilevel Boost Converter Interfaced High-Power DC Microgrid Using an Adaptive Observer-Based Nonlinear Control Scheme

Nonlinear robust motion trajectory tracking control based on backstepping method for a novel pneumatic actuator

Nonlinear robust motion trajectory tracking control based on backstepping method for a novel pneumatic actuator

Adaptive Fuzzy Control for Fractional-Order Networked Control Systems with Input Time Delay and Data Loss

This paper considers the adaptive fuzzy control of fractional-order nonlinear networked control systems subjected to network-induced input delay and data loss. To approximate unknown functions, fuzzy logic systems are employed. Furthermore, the Pade approximation method and an intermediate variable are introduced to eliminate the impact of input delay, and an adaptive fuzzy controller is designed using backstepping technology. Based on fractional-order Lyapunov stability theory, the proposed method can ensure that all signals are uniformly ultimately bounded, and the tracking error can converge to a small region of the origin. Two simulation examples are provided to verify the viability of the control method.

Effective Nonlinear Predictive and CTC-PID Control of Rigid Manipulators

Effective nonlinear control of manipulators with dynamically coupled arms, like those with direct drives, is the subject of the paper. The main proposal of the paper are model-based predictive control (MPC) algorithms, with nonlinear state-space models and most recent disturbance attenuation technique. This technique makes controller design and calculations simpler, avoiding necessity of dynamic modeling of disturbances or resorting to additional techniques like SMC. The core of the paper are computationally effective MPC-NPL (Nonlinear Prediction and Linearization) algorithms, where computations at every sample are divided into two parts: prediction of initial trajectories using nonlinear model, then optimization using simplified linearized model. For a comparison a known CTC-PID algorithm, which is also model-based, is considered. It is applied in standard form and also proposed in more advanced CTC-PID2dof version. For all algorithms a comprehensive comparative simulation study is performed, for a direct drive manipulator under disturbances. Additional contribution of the paper is an investigation of influence of sampling period length and computational delay time on performance of the algorithms, which is practically important when using model-based algorithms and fast sampling.

Adaptive PI event-triggered control for MIMO nonlinear systems with input delay

An adaptive PI event-triggered control method for a class of multi-input and multi-output (MIMO) nonlinear systems with uncertain input delay is proposed. First, a new variable is designed to eliminate the impact of uncertain input delay based on the Pade approximation and Laplace transform. Then, a nonlinear controller with PI structure is developed, it has a linear structure and the ability to deal with uncertainties. Meanwhile, only one parameter is required to be updated online through RBFNN in the system. Moreover, an event-triggered control strategy is established to dynamically allocate the communication resources based on the PI nonlinear controller. With the proposed method, MIMO nonlinear systems are able to handle input delay of different duration dynamically and still maintain superior tracking performance. Finally, the effectiveness of the proposed method is demonstrated through two sets of simulation experiments.

Control strategy for trading off solar power and control input while rendezvous and docking

This paper presents a novel strategy for decoupling the attitude and orbit equations of a CubeSat for rendezvous and docking with an uncontrollable cooperative target. For computing a safe trajectory, the proposed control algorithm takes into account the solar energy received by the CubeSat. The CubeSat is equipped with a thruster for orbit maneuvers, magnetic coils for attitude control, and four fixed single-sided rectangular solar panels. The coupled dynamics arising from the dynamical model of the spacecraft is solved using a novel weighted vector-based approach. This paper presents a linearized nonlinear optimal control problem arising in small spacecrafts while trying to maximize solar energy input in the rendezvous and docking process. An intermediate orbit is defined and used to divide the problem into two different optimal control problems: rendezvous optimization and docking optimization problems. A geometrical approach based on the shape of the chaser and the target is contemplated for collision avoidance while docking. The proposed controller design is modified whenever the sunlight is obstructed by the Earth, and the maneuvering controls are redesigned accordingly for optimal rendezvous and docking. Numerical simulations have been carried out to show the efficacy of the proposed concept for steering the trade-off between solar energy and control input during the rendezvous and docking of CubeSat with a tumbling target.

On the simplification of the internal nonlinear robot models for the MPC-based visual servoing

AbstractVisual servoing enables tracking and grasping of static and moving objects and locating regions of interest. Its implementation requires the coordination of low-level control tasks with high-level supervision and planning. The use of the PID-based manipulation control addresses only the dynamical response and requires the use of optimization-based techniques at the supervisory level. On the contrary, model predictive control (MPC) simplifies the design as it combines both tasks within a single algorithm. However, successful MPC implementation depends on the quality of the internal model utilized by the algorithm. Robots are intrinsically and significantly nonlinear control objects, so their models are inherently complex. This leads to computational problems. Standard solutions are to shorten the MPC horizons or to parallelize calculations. The approach suggested here returns to the problem roots, i.e. to the model. This study proposes simplified models that allow the use of the MPC. General Hammerstein–Wiener-like model is applied to an arm, which is further simplified into the linear dynamics used as the predictive supervisory control over the torque control. The head uses linear dynamics used by the supervisory MPC working with the base servomotors. It is shown that they are computationally efficient and sufficiently accurate. It is shown that the linear MPC controllers, which use the suggested simplified dynamic robot models, can successfully support visual servoing with accurate control. They are compared with standard PID-based structures and show their superiority. The proposed approach is successfully validated using Matlab and Gazebo robot simulator and is ultimately confirmed by experiments on a real robot.

A novel approach to the FPIBSC strategy for an electric power steering system

The electric power steering system is used to improve comfort and steering feel for the user. In this article, we propose to use an integrated nonlinear control strategy for the electric steering system, which is described based on a complicated dynamic model. This work provides two new contributions. First, this control algorithm combines backstepping and proportional–integral (PI) techniques with parameters adjusted by a fuzzy algorithm, so it is called Fuzzy proportional–integral backstepping control (FPIBSC). The output of the PI algorithm is the input of the backstepping technique, while system stability is evaluated based on the Lyapunov function with virtual control variables. Second, road reaction torque is calculated based on a spatial dynamic model, which considers the influence of many other factors. Numerical simulation methods are used to evaluate the performance of the system. According to research findings, the value of the steering motor angle (controlled object) continuously tracks the desired value with negligible error. Under some specific conditions, the error between signals can be reduced to zero. In addition, other outputs that are obtained from the FPIBSC algorithm also tend to follow the reference signal with high accuracy. The phase difference phenomenon only occurs when using the conventional backstepping algorithm instead of FPIBSC. The assisted torque increases as speed decreases or the driver torque increases. In general, the system’s stability is always guaranteed under many different simulation conditions.

Nonlinear control of three level NPC inverter used in PV/grid system: comparison of topologies and control methods

With the passage of time, the importance of using renewable energy systems to overcome energy consumption and improve the quality of the grid has emerged through the use of nonlinear control techniques and reliance on advanced types of inverters such as multi-level inverters. This research is focused on comparing two grid-connected converter topologies in a photovoltaic (PV) generation system connected to a three-phase grid that serves a non-linear load. Additionally, the study explores two different control techniques applied to this converter, evaluating their effects on the total harmonic distortion coefficient. A comparison has been made between the traditional inverter and the three-level inverter type neutral point clamped (NPC) inverter, with the use of integral backstepping (IBS) technique which was also compared with the proportional integral (PI) controller. The simulation results in MATLAB/Simulink are presented illustrating the performances and the strong effectiveness of the three-level NPC inverter controlled by the proposed technique (IBS).

Robust Tracking Control Design for a Class of Nonlinear Networked Control Systems Considering Bounded Package Dropouts and External Disturbance

Robust Tracking Control Design for a Class of Nonlinear Networked Control Systems Considering Bounded Package Dropouts and External Disturbance

Robust adaptive deep brain stimulation control of in-silico non-stationary Parkinsonian neural oscillatory dynamics

Objective. Closed-loop deep brain stimulation (DBS) is a promising therapy for Parkinson’s disease (PD) that works by adjusting DBS patterns in real time from the guidance of feedback neural activity. Current closed-loop DBS mainly uses threshold-crossing on-off controllers or linear time-invariant (LTI) controllers to regulate the basal ganglia (BG) Parkinsonian beta band oscillation power. However, the critical cortex-BG-thalamus network dynamics underlying PD are nonlinear, non-stationary, and noisy, hindering accurate and robust control of Parkinsonian neural oscillatory dynamics. Approach. Here, we develop a new robust adaptive closed-loop DBS method for regulating the Parkinsonian beta oscillatory dynamics of the cortex-BG-thalamus network. We first build an adaptive state-space model to quantify the dynamic, nonlinear, and non-stationary neural activity. We then construct an adaptive estimator to track the nonlinearity and non-stationarity in real time. We next design a robust controller to automatically determine the DBS frequency based on the estimated Parkinsonian neural state while reducing the system’s sensitivity to high-frequency noise. We adopt and tune a biophysical cortex-BG-thalamus network model as an in-silico simulation testbed to generate nonlinear and non-stationary Parkinsonian neural dynamics for evaluating DBS methods. Main results. We find that under different nonlinear and non-stationary neural dynamics, our robust adaptive DBS method achieved accurate regulation of the BG Parkinsonian beta band oscillation power with small control error, bias, and deviation. Moreover, the accurate regulation generalizes across different therapeutic targets and consistently outperforms current on-off and LTI DBS methods. Significance. These results have implications for future designs of closed-loop DBS systems to treat PD.

Nonlinear Model Predictive Control With Obstacle Avoidance Constraints for Autonomous Navigation in a Canal Environment

Nonlinear Model Predictive Control With Obstacle Avoidance Constraints for Autonomous Navigation in a Canal Environment

Vector Control of Brushless Doubly-Fed Induction Machines Based on Highly Efficient Nonlinear Controllers

Vector Control of Brushless Doubly-Fed Induction Machines Based on Highly Efficient Nonlinear Controllers

Extended-State-Observer-Based Nonlinear Control for PMSM Servo Systems With Current Constraints and Voltage Saturations

Extended-State-Observer-Based Nonlinear Control for PMSM Servo Systems With Current Constraints and Voltage Saturations

Energy enhancement in grid-connected photovoltaic generation systems using adaptive control technique

This research paper presents an innovative adaptive control technique for enhancing energy efficiency in grid-connected photovoltaic (PV) generation systems. By integrating an advanced high-gain DC-DC converter with adaptive and non-linear control methods, the proposed system ensures optimal power absorption from various renewable resources and maintains robust power transfer efficiency and reliability. Key results from the implementation at the Union Territory of Puducherry substation demonstrate significant improvements: the adaptive control system dynamically adjusts to changes in source voltage and load conditions, maintaining a constant output voltage with minimized steady-state error and low overshoot. The feedback controller effectively responds to fluctuations in duty cycle and output load, ensuring stable operation under varying environmental conditions. These enhancements contribute to a reliable and efficient integration of renewable energy sources into the power grid, addressing critical challenges in renewable energy technology.

Improved control of permanent magnet synchronous motors using adaptive nonlinear control with Deadbeat Observer

Permanent Magnet Synchronous Motor (PMSM) control optimization is crucial for a variety of applications, including electric vehicles, industrial automation, and renewable energy systems (RESs). However, traditional control methods often struggle to adapt to the dynamic and non-linear nature of PMSMs, leading to sub-optimal performance. To address these challenges, this paper proposes a hybrid adaptive nonlinear control strategy with a deadbeat observer (DO) designed to improve the performance of PMSMs. This approach aims to improve the accuracy and robustness of the control while taking into account the system parameters variations and disturbances. Simulation tests comparing the proposed method with an adaptive nonlinear control (ANLC) are presented, highlighting its superior effectiveness in controlling PMSMs under varying load conditions and speed fluctuations. The proposed strategy achieves a maximum relative speed error around of 6% at 0.4 s under gradually varying load torque disturbances, which is better than the ANLC with 8%. Furthermore, under large speed variations, the suggested method maintains a maximum relative speed error of 0.82% at 0.85 s. Furthermore, the robustness assessment under system parameters variations, stator resistance, and inductance, shows extraordinary performances of the proposed scheme. These results highlight the effectiveness and robustness of the proposed strategy in achieving precise PMSM control while dynamically adapting to changing conditions. This research underscores the potential of our approach to advance key technologies, including electric vehicles, industrial automation, and renewable energy systems. By optimizing PMSM control, this strategy contributes to increased efficiency, reliability, and adaptability, facilitating broader adoption of electric propulsion systems and sustainable energy solutions.

Robust tracking of discrete-time linear switched systems with disturbance via second-order ILC with data loss

The purpose of this article is to investigate the second-order P-type iterative learning control (ILC) scheme in the presence of data loss for a class of linear discrete-time switched systems with disturbances. Employing the super-vector representation technique, the discrete-time linear switched system is reformulated as an input-output transmission equation. The robustness of the resulting switched system driven by a second-order P-type ILC scheme is guaranteed through the use of the super-vector representation technique. Importantly, the article also explores cases of data loss occurring during data transmission. The proposed methodology exhibits significantly improved convergence performance compared to the P-type ILC scheme (Yang et al., 2022, Robust finite-iteration tracking of discrete-time systems in repetitive process setting via ILC scheme. International Journal of Robust and Nonlinear Control, 32(5), 2585–2602. https://doi.org/10.1002/rnc.5782). Simulation examples are provided to demonstrate the effectiveness of the proposed scheme.