Disturbance Rejection Capability Research Articles

Blood-glucose regulator design for diabetics based on LQIR-driven Sliding-Mode-Controller with self-adaptive reaching law.

Type I Diabetes is an endocrine disorder that prevents the pancreas from regulating the blood glucose (BG) levels in a patient's body. The ubiquitous Linear-Quadratic-Integral-Regulator (LQIR) is an optimal glycemic regulation strategy; however, it is not resilient enough to withstand measurement noise and meal disruptions. The Sliding-Mode-Controller (SMC) yields robust BG regulation effort at the expense of a discontinuous insulin infusion rate that perturbs the BG concentrations. Hence, the novel contribution of this article is the formulation of a hybridized LQIR-driven SMC strategy that retrieves the benefits of the aforesaid control schemes while avoiding their inherent problems. The proposed control approach is realized by linearly combining a glycemic LQIR law with an innovative sign function sliding mode reaching law that is driven by a customized LQIR-driven sliding surface. The hybridized control scheme generates optimal control decisions yielded by the LQIR while mimicking the robustness characteristic of SMC against bounded exogenous disturbances. Additionally, the SMC reaching law in the proposed control scheme is augmented with a nonlinear adaptation mechanism that flexibly modulates the control activity to effectively compensate for the external perturbations while minimizing the chattering content. The controller parameters are numerically optimized offline. The efficacy of the prescribed hybrid control law is analyzed via customized MATLAB simulations that normalize the patient's BG level to 80 mg/dL, under measurement noise and meal disruptions, from an initial hyperglycemic state. The results justify the improved BG regulation accuracy and disturbance-rejection capability of the proposed control procedure.

Active disturbance rejection control with adaptive RBF neural network for a permanent magnet spherical motor

In response to the issues of low tracking accuracy and poor robustness in the trajectory tracking control of a permanent magnet spherical motor (PMSpM), an active disturbance rejection control (ADRC) scheme combining neural networks is put forward in this research. The unknown total disturbance is approximated by employing a radial basis function (RBF) neural network, with weights updated by an adaptive law and compensated for through the nonlinear feedback loop. This approach addresses the problem of performance degradation of the extended state observer under severe total disturbance, thereby ensuring accurate tracking of the PMSpM. Comparative simulations are accomplished to evaluate the performance of the RBF-ADRC scheme in enhancing disturbance rejection capability and robustness. Experimental results from the planar circular motion experiment on the PMSpM test platform validate the application value of the scheme.

Controllability measure for disturbance rejection capabilities of control systems with undamped flexible structures

This paper introduces a controllability measure for quantitatively evaluating the disturbance rejection capabilities of control systems with undamped flexible structures. The measure is derived by obtaining the steady-state solution of the degree of disturbance rejection capability (DoDR), a Gramian-based measure used to assess controllability under external disturbances. To address the issue of Gramian matrices diverging over time in undamped systems, we have developed and proven several theorems related to Gramian matrices in undamped systems. The resulting solution, derived using these theorems is represented in a closed-form and expressed in terms of the modal matrix, input matrix, disturbance matrix, and disturbance covariance matrix. Since the derived solution does not require solving Lyapunov equations, which is typically required in most Gramian-based measures, it enables efficient computations, even for high-dimensional systems. Numerical examples confirm that the proposed measure serves as an exact DoDR solution for undamped systems, preserving the previously established physical meaning of DoDR. Control simulations further validate its accuracy in predicting disturbance rejection performance, highlighting its value in actuator allocation.

Finite-time SMC-based admittance controller design of macro-micro robotic system for complex surface polishing operations

In the field of robotic polishing, achieving uniform material removal typically involves addressing the issue of constant contact force control. However, multi-source external disturbances in the polishing scenarios of complex workpiece surfaces can severely affect the robot’s force control accuracy. To enhance the responsiveness and disturbance rejection capabilities of robots in the compliant polishing process, this paper proposes an adaptive admittance controller with practical finite-time stability. A virtual control input is introduced into the basic admittance control framework in light of the state space theory, aiming to provide flexibility for common adaptive law designs. On this basis, a robust sliding mode control (SMC) algorithm is proposed to suppress external disturbances. The force tracking error is theoretically proven to achieve finite-time convergence when applying the proposed control strategy. Experimental results across various polishing scenarios demonstrate that, compared with the existing admittance control strategies, the proposed method can reduce fluctuations of the polishing force and improve the surface quality, thus verifying its effectiveness.

Parameter tuning for active disturbance rejection control of fixed-wing UAV based on improved bald eagle search algorithm

PurposeThis paper aims to develop a method for tuning the parameters of the active disturbance rejection controller (ADRC) for fixed-wing unmanned aerial vehicles (UAVs). The bald eagle search (BES) algorithm has been improved, and a cost function has been designed to enhance the optimization efficiency of ADRC parameters.Design/methodology/approachA six-degree-of-freedom nonlinear model for a fixed-wing UAV has been developed, and its attitude controller has been formulated using the active disturbance rejection control method. The parameters of the disturbance rejection controller have been fine-tuned using the collaborative mutual promotion bald eagle search (CMP-BES) algorithm. The pitch and roll controllers for the UAV have been individually optimized to obtain the most effective controller parameters.FindingsInspired by the salp swarm algorithm (SSA), the interaction among individual eagles has been incorporated into the CMP-BES algorithm, thereby enhancing the algorithm's exploration capability. The efficient and accurate optimization ability of the proposed algorithm has been demonstrated through comparative experiments with genetic algorithm, particle swarm optimization, Harris hawks optimization HHO, BES and modified bald eagle search algorithms. The algorithm's capability to solve complex optimization problems has been further proven by testing on the CEC2017 test function suite. A transitional function for fitness calculation has been introduced to accelerate the ability of the algorithm to find the optimal parameters for the ADRC controller. The tuned ADRC controller has been compared with the classical proportional-integral-derivative (PID) controller, with gust disturbances introduced to the UAV body axis. The results have shown that the tuned ADRC controller has faster response times and stronger disturbance rejection capabilities than the PID controller.Practical implicationsThe proposed CMP-BES algorithm, combined with a fitness function composed of transition functions, can be used to optimize the ADRC controller parameters for fixed-wing UAVs more quickly and effectively. The tuned ADRC controller has exhibited excellent robustness and disturbance rejection capabilities.Originality/valueThe CMP-BES algorithm and transitional function have been proposed for the parameter optimization of the active disturbance rejection controller for fixed-wing UAVs.

Continuous-time robust frequency regulation in isolated microgrids with decentralized fixed structure μ-synthesis and comparative analysis with PID and FOPID controllers

Isolated microgrids, which are crucial for supplying electricity to remote areas using local energy sources, have garnered increased attention due to the escalating integration of renewable energy sources in modern microgrids. This integration poses technical challenges, notably in mitigating frequency deviations caused by non-dispatchable renewables, which threaten overall system stability. Therefore, this paper introduces decentralized fixed structure robust μ-synthesis controllers for continuous-time applications, surpassing the limitations of conventional centralized controllers. Motivated by the increasing importance of microgrids, this work contributes to the vital area of frequency regulation. The research challenge involves developing a controller that not only addresses the identified technical issues but also surpasses the limitations of conventional centralized controllers. In contrast to their centralized counterparts, the proposed decentralized controllers prove more reliable, demonstrating enhanced disturbance rejection capabilities amidst substantial uncertainties, represented through normalized co-prime factorization. The proposed controllers are designed using the D-K iteration technique, incorporating performance weight filters on control actions to maintain low control sensitivity and ensure specific frequency band operation for each sub-system. Importantly, the design considers unstructured uncertainty up to 40%, addressing real-world uncertainties comprehensively. Rigorous robust stability and performance tests underscore the controller's superiority, demonstrating its robustness against elevated uncertainty levels. Robust stability is verified for all controllers, with the proposed controller showing robust stability against up to 171% of the modeled uncertainty. Notably, the controller boasts a fixed structure with lower order compared to other H-infinity controllers, enhancing its practical implementation. Comparative analyses against Coronavirus Herd Immunity Optimizer tuned Proportional-Integral-Derivative (CHIO-PID) controller and CHIO tuned Fractional-Order Proportional-Integral-Derivative (CHIO-FOPID) controller further validate the superior performance of the proposed solution, offering a significant step towards ensuring the stability and reliability of microgrid systems in the face of evolving energy landscapes.

A Hierarchical Control Method for Trajectory Tracking of Aerial Manipulators Arms

To address the control challenges of an aerial manipulator arm (AMA) mounted on a drone under conditions of model inaccuracy and strong disturbances, this paper proposes a hierarchical control architecture. In the upper-level control, Bézier curves are first used to generate smooth and continuous desired trajectory points, and the theory of singular trajectory lines along with a Radial Basis Function Neural Network (RBFNN) is introduced to construct a highly accurate multi-configuration inverse kinematic solver. This solver not only effectively avoids singular solutions but also enhances its precision online through data-driven methods, ensuring the accurate calculation of joint angles. The lower-level control focuses on optimizing the dynamic model of the manipulator. Using a Model Predictive Control (MPC) strategy, the dynamic behavior of the manipulator is predicted, and a rolling optimization process is executed to solve for the optimal control sequence. To enhance system robustness, an RBFNN is specifically introduced to compensate for external disturbances, ensuring that the manipulator maintains stable performance in dynamic environments and computes the optimal control commands. Physical prototype testing results show that this control strategy achieves a root mean square (RMS) error of 0.035, demonstrating the adaptability and disturbance rejection capabilities of the proposed method.

Active disturbance rejection control for piezoelectric cantilever beam with time delay

This Letter presents an active control method for the vibration of piezoelectric structures. First, a controller based on nonlinear extended state observer is designed to improve the disturbance rejection capability of the vibration structure. Additionally, an improved nonlinear active disturbance rejection control (IMNADRC) method is combined with Smith predictor for time delay compensation, resulting in Smith predictor-based IMNADRC. Finally, numerical simulations and experiments are carried out for active vibration suppression of piezoelectric beam under fixed harmonic excitation and variable harmonic excitation, respectively. The results show that the proposed method has good control effect on structural vibration suppression and is highly adaptable to various external excitations.

A deterministic design approach of tilt integral derivative controller for integer and fractional-order system with time delay

A tilt integral derivative (TID) controller modifies the proportional integral derivative (PID) controller in the fractional domain. It converts the proportional gain as a function of frequency and is thereby capable of achieving optimal system response. The usual practice for the parameter estimation of the TID controller is by minimization of the error-based objective functions using optimization techniques. Although precise results can be achieved, these nature-inspired algorithms are stochastic and hence produce different solutions during different iterations. Therefore, a comparative statistical study is usually necessary to validate the best possible result. This study shows a deterministic analytical procedure for the paramssseter estimation of TID controllers. The magnitude and phase angle criteria, along with the frequency-domain loop shaping specifications, are used for the explicit evaluation of the TID parameters. Because of its model-independent nature, this tuning strategy can be used for a variety of integral and nonintegral order systems with different plant structures. In this article, the authenticity of the applied procedure is demonstrated through suitable numerical examples. The complexity of the design problem is enhanced by using it for both integer and non-integer (fractional) order plus time-delay systems. Further, the robustness of the control system in the presence of a TID controller was examined under the influence of external parameters and input reference changes. Simulation studies validate the supremacy of TID controllers over PID controllers in terms of reference tracking and disturbance rejection capabilities.

Stabilizing Electric Vehicle Systems Using Proximal Policy-Based Self-structuring Control

An active disturbance rejection control (ADRC) has been developed for stabilizing electric vehicle (EV) systems without the need for model identification. The proximal policy optimization (PPO) algorithm, along with actor and critic neural networks, has been used to fine-tune the adjustable parameters of the ADRC controller to achieve optimal performance in a specific case study. The architecture of PPO implements separate neural networks and ameliorates the PPO adaptability to handle continuous action spaces. By maximizing a reward function based on system output, the PPO agent optimally tunes the gains to reduce undesired speed fluctuations of EVs and improve system stability. Performance evaluation under the new European driving cycle and federal test procedure has been conducted to examine the feasibility of the suggested controller. The disturbance rejection capability of the ADRC controller designed by the PPO algorithm has been tested and compared with prevalent control methodologies. Moreover, real-time examinations of the dynamic behavior of EV systems have been made to identify the capability of the suggested controller in real-world hardware. The results show that the suggested controller outperforms other designed controllers in terms of transient behavior and numerical performance metrics.

Adaptive terminal sliding combined super twisting control design and flight tests for automatic carrier landing system

Considering the challenges posed by external disturbances on carrier-based aircraft landing control, higher demands are required for the precision and convergence of the carrier landing control system. First, this paper proposes an Adaptive Terminal Sliding Combined Super Twisting Control (ATS-STC) method to address the issues of low precision, slow convergence, and poor disturbance rejection capability resulting from external disturbances, such as carrier air-wake and deck motion. By introducing a nonlinear term into the sliding surface and employing an integral approach, the proposed ATS-STC method can ensure finite-time convergence and mitigate the chattering problem. An adaptive law is also utilized to estimate the external disturbances, thereby enhancing the anti-disturbance performance. Then, the stability and convergence time analysis of the designed controller are conducted. Based on the proposed method, an Automatic Carrier Landing System (ACLS) is developed to perform the carrier landing control task. Furthermore, a multi-dimensional validation is carried out. For the numerical simulation test, the Terminal Sliding Mode Control (TSMC) method and Proportion Integration Differentiation (PID) method are introduced as comparison, the quantitative assessment results show that the tracking error of TSMC and PID can reach 1.5 times and 2 times that of the proposed method. Finally, the Hardware-in-the-Loop (HIL) test and real flight test are conducted. All the experimental results demonstrate that the proposed control method is more effective and precise.

Based on graph theory, design of distributed control system for multi-quadcopter UAVs

This paper addresses the formation control system for multi-quadcopter UAVs, proposing a control strategy based on finite-time stability theory. It designs position controllers with a dual power structure and attitude controllers combined with non-singular terminal sliding mode to achieve fast convergence and high-precision tracking. Through Lyapunov stability analysis, the paper proves the finite-time convergence of the control strategy. Simulation experiments validate the method's excellent performance in formation establishment, maintenance, and complex trajectory tracking, demonstrating rapid convergence, high precision, and strong disturbance rejection capabilities. This research provides theoretical and technical support for cooperative control of multi-quadcopters.

Zero-Power Control Strategy and Dynamics Enhancement for Hybrid Maglev Conveyor Cart

This paper presents a novel zero-power controller applied to a four-unit magnetic levitation system, aimed at addressing the challenge of maintaining stability under disturbance loads. The zero-power controller, designed based on a state feedback controller integrated with a position servo integrator, is primarily employed to control the balance of the magnetic levitation (Maglev) unit and eliminate steady-state errors. Subsequently, the zero-power controller operates after the state feedback controller to adjust the Maglev unit to a new equilibrium point, primarily utilizing permanent magnetic force to suspend against gravitational input. When loads change or disturbances occur, the system generates current to maintain balance. All designs have passed validation. Experimental results demonstrate the improved zero-power performance and disturbance rejection capabilities of the proposed Maglev system. During synchronous operation, dynamic characteristics have shown significant improvement, which has been experimentally confirmed.

Load torque observation and compensation for permanent magnet synchronous motor based on sliding mode observer

Background Permanent magnet synchronous motors (PMSM) are widely used in various industries. However, in practical applications, the time-varying nature of load torque may lead to speed fluctuations, negatively impacting the motor's control performance and stability. To mitigate these issues, this paper proposes a load torque observation method for PMSMs based on a sliding mode observer. Methods The sliding mode observer is designed to estimate the load torque and convert it into a current, which is fed back as a feedforward compensation to the q-axis current in the current closed-loop control system. The observer's dynamic performance is optimized using a genetic algorithm to minimize the Integral of Time-weighted Absolute Error (ITAE) between the observer and the actual system state. Experimental tests are conducted on a motor torque testing platform. After stabilizing the motor at 800rpm, a sudden torque of 0.5Nm is applied. Results Compared to the situation without load torque compensation, the motor speed fluctuations are reduced by approximately 60% after adding load torque compensation. Conclusions This enhancement improves the system's speed control performance during torque variations and increases the system's robustness and disturbance rejection capability.

Autonomous swimming on limit cycles with disturbance rejection capability for a fish-inspired robot

Autonomous swimming on limit cycles with disturbance rejection capability for a fish-inspired robot

A stability improvement control strategy research for LLC resonant converter with single neuron PI-I double closed loop control

LLC resonant converters are widely used in the aerospace field. However, under traditional single-loop control, LLC resonant converters are prone to instability in the face of high-power load disturbances, which fails to meet the high stability requirements of the aerospace field. In order to enhance the stability and disturbance rejection capability of the LLC resonant converter, this paper proposes a stability improvement strategy with additional resonant voltage integral control based on the generalized state-space aver-aged model of the LLC resonant converter. By combining eigenvalue sensitivity analysis and eigenvalue analysis theory, the system stability is further optimized. To achieve control parameter adaptive adjustment, a single neuron algorithm is introduced, resulting in an LLC resonant converter with a single neuron PI-I double closed-loop control. Simulation results indicate that the proposed LLC resonant converter with single neuron PI-I double closed-loop control exhibits improved stability compared to the LLC resonant converter under traditional single-loop control.

Current Harmonic Suppression in Maritime Vessel Rudder PMSM Drive System Based on Composite Fractional-Order PID Repetitive Controller

To address the control performance and harmonic suppression issues in maritime vessel rudder permanent magnet servo systems, a fractional-order PID controller was introduced into the existing improved repetitive control strategy. We used the Oustaloup approximation algorithm and particle swarm optimization for tuning the fractional-order PID controller. The optimized parameters substantially improved the control performance. By integrating the fractional-order PID controller with the improved repetitive controller, a composite fractional-order PID repetitive control strategy was formed. Finally, MATLAB/Simulink simulations were conducted to compare and verify the disturbance rejection and harmonic suppression capabilities of the improved control strategy. The results demonstrate its superior control performance, thereby increasing the practicality of the control system in dealing with various situations.

Sliding Mode Speed Control for PMSM Based on Model Predictive Current

To enhance the dynamic performance and disturbance rejection capability of the permanent magnet synchronous motor speed control system, a novel speed control method based on a novel sliding mode control (NSMC) and load torque observer is proposed on the basis of model predictive current control (MPCC) with a sliding mode disturbance observer. First, on the basis of MPCC, the influence of parameters such as resistance, inductance, and flux linkage on MPCC is analyzed. To address the aggregated disturbance caused by parameter mismatches, a piecewise square-root switching function sliding mode disturbance observer (SMDO) is designed to enhance the robustness of the parameters. To address the poor dynamic performance and inadequate robustness resulting from the proportional-integral-controller (PI) velocity loop control in the MPCC, a novel NSMC velocity control method is proposed. This method utilizes the hyperbolic sine function and fractional-order integral sliding mode surface, resolving the dilemma faced by traditional slide mode controllers (SMC) in balancing fast response and reduced vibration. Additionally, to enhance the system’s disturbance rejection capability, a sliding mode torque observer (SMTO) is designed to continuously update the observed load torque value into the NSMC controller, achieving speed compensation control. Finally, through comparative experiments among the proportional integral controller (PI), SMC, NSMC, and NSMC + SMTO, the results indicate that the proposed NSMC + SMTO exhibits the best speed response, steady-state characteristics, and disturbance rejection capability.

Dual internal model control with improved load current feedback for enhanced dynamic performance in aircraft high‐voltage direct current brushless generator

AbstractIn order to enhance the dynamic performance of aircraft high‐voltage direct current wound rotor synchronous generator (WRSG) system under load variations, a dual internal model control (DIMC) method utilising improved load current feedback (ILCF) is proposed. It employs internal model controllers in both voltage and current loops, thereby significantly augmenting the system's disturbance rejection capabilities. Improved load current feedback is incorporated between the DC voltage loop and the field current loop. Additionally, a method for designing a feedback fitting function is established, enhancing the system's adaptability to changes in operational speed and load. The authors outline parameter design methods for voltage and current loops in DIMC, based on the internal model control principle. Adjustments to these parameters can further enhance the system's disturbance rejection capabilities. Finally, comparative experiments between the PI method with ILCF and the DIMC method with ILCF are conducted on a 15 kW WRSG experimental platform. These experiments demonstrate that the proposed DIMC method with ILCF optimises dynamic performance across the entire speed range, enhancing the competitiveness of WRSG systems in aircraft applications.

Depth control of ROV using the improved LADRC based on nutcracker optimization algorithm

Aiming at the problem of automatic depth control for Remotely Operated Vehicle (ROV) under complex disturbed environment, this paper designs a control scheme used the improved Linear Active Disturbance Rejection Controller (LADRC) based on nutcracker optimization algorithm (NOA). Firstly, in order to solve the disturbance problem in the depth control process of ROV under complex disturbed environment, the LADRC is applied to the depth control of ROV to improve the disturbance rejection capability of the system. After that, due to the difficulty and uncertainty of LADRC parameter tuning, the global search capability of nutcracker optimization algorithm is used to optimize the LADRC controller. It realizes the automatic tuning of LADRC parameters, thus realizes the fast and high-accuracy depth control. Finally, the simulation and experimental verification are completed. The overshooting amount of the NOA-LADRC control scheme in the depth control of ROV is almost zero, the control accuracy and disturbance rejection capability are improved by about 50%.