Disturbance Rejection Capability Research Articles

Optimization of Ship Permanent Magnet Synchronous Motor ADRC Based on Improved QPSO

To address the impact of load variations, external environmental changes, and the tuning of the parameters on Permanent Magnet Synchronous Motors (PMSMs) used in ships, this study proposes an Active Disturbance Rejection Control (ADRC) strategy for PMSMs, optimized by the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, based on the PMSM model, the study addresses the limited disturbance rejection capability of the traditional fal function in the Extended State Observer (ESO) of conventional ADRC. To improve the accuracy of the state observer, the faln function is introduced as a replacement for the traditional fal function. Second, due to the numerous parameters in ADRC, which are difficult to tune, the QPSO algorithm—known for its strong global search capabilities and fast convergence speed—is utilized for parameter optimization. Additionally, the position update formula within the optimization algorithm is revised and optimized. Finally, simulation experiments are conducted using the Matlab/Simulink platform, where practical conditions, such as load fluctuations and random noise, are incorporated. The simulation results demonstrate that, compared to PSO-ADRC control, IPSO-ADRC control, and ICFO-ADRC control, the proposed method offers a superior dynamic response. Specifically, the speed control accuracy is improved by 46.7%, torque ripple is reduced by 50.8%, and harmonic distortion decreases by 23.1%. These results highlight the significant advantages of this method in enhancing system robustness, dynamic response speed, and steady-state accuracy, making it particularly suitable for PMSM control systems in complex dynamic environments, such as those encountered on ships.

Fuzzy Extended State Observer-Based Sliding Mode Control for an Agricultural Unmanned Helicopter

In the context of agricultural unmanned helicopters, the complex wind disturbances over crop fields and structural perturbations due to variations in pesticide container weights present substantial challenges to flight safety. To address these issues, this paper proposes an innovative fuzzy extended state observer-based sliding mode control (FESO-SMC) methodology aimed at enhancing the aircraft’s resilience against such disturbances. Initially, this study adopts a state expansion strategy to integrate both wind and structural disturbances into a comprehensive disturbance model applicable to the agricultural unmanned helicopter. Following this, a sliding mode control law is formulated with consideration for unknown total disturbances, employing specific sliding mode functions alongside exponential reaching laws. An extended state observer is simultaneously implemented within the sliding mode control framework to estimate and mitigate these disturbances, thereby augmenting the disturbance rejection capabilities of the flight control system. Additionally, the integration of fuzzy logic facilitates adaptive parameter adjustment for the extended state observer, leading to more accurate disturbance estimation. Consequently, a trajectory tracking control system tailored specifically for the agricultural unmanned helicopter has been developed, and its performance was evaluated through simulation experiments. The results indicate that, under certain disturbances, the attitude control error of the FESO-SMC controller is reduced to one-fifth that of traditional sliding mode controllers, while position control accuracy is enhanced more than twofold, thus demonstrating that the proposed FESO-SMC controller not only exhibits superior anti-disturbance capability and robustness but also achieves higher tracking accuracy compared to conventional sliding mode controller.

Trajectory tracking control strategy for biaxial systems based on disturbance rejection and axis-coupled interpolation command modifier

This paper proposes a new control strategy for biaxial systems. It improves the trajectory tracking capabilities of biaxial system by enhancing the tracking performance of feed axes, disturbance rejection capabilities of feed axes, and consistency of dynamics between axes. First, the axis position controller is designed, it uses the feedforward controller and the state feedback controller to realize the active control of the low-order resonant modes of the mechanical system. In addition, a reduced-order extended state observer is designed to estimate the unknown state variables and disturbances in real time. The estimated disturbances will be compensated to the control loop to enhance the disturbance rejection capability of the axis. Second, the interpolation command modifier is designed. It is axis-coupled, which can not only compensate for the error caused by the axes themselves, but also compensate for the error caused by the coupling dynamics between axes. Finally, the effectiveness of the proposed integrated control strategy is experimentally verified. The experimental results show that the trajectory tracking accuracy of the biaxial systems and its robustness to load variations are both improved by using the proposed control strategy.

Design of Fractional‐Order Sliding Mode Controller for an Unstable Three‐State Model Jacketed CSTR

ABSTRACTControl of a jacketed continuous stirred tank reactor (CSTR) is challenging due to nonlinear dynamics, complexity, and rapid reactor dynamics under imperfect mixing in the jacket. Current controller designs mainly focus on the two‐state model, neglecting the potential of three‐state models in scenarios with nonperfect mixing and fast reactor dynamics. This study proposes a sliding mode controller (SMC) design scheme based on the transfer function model using a newly developed jellyfish optimisation algorithm. Further, a fractional‐order sliding mode control (FO‐SMC) strategy is proposed, which integrates modifications to the SMC to mitigate chattering, enhance control robustness, and provide better disturbance rejection capability. PID and fractional‐order PID (FOPID) controllers were also designed for comparative analysis. The simulation results demonstrated that FO‐SMC outperformed other designed controllers, shown by a 37.14% reduction in settling time, 10.69% reduction in integral absolute error (IAE), and 19.06% reduction in time‐weighted absolute error (ITAE) compared to SMC and various other improved performance indicators. Parameter variation and noise analysis highlighted the ability of the controller to maintain stability and performance under dynamic conditions.

Control Architecture for LLC Resonant Converters With High Input Disturbance Rejection Capability Using Output Diode Current

Control Architecture for LLC Resonant Converters With High Input Disturbance Rejection Capability Using Output Diode Current

Intelligent Direct Inverse Control for Nonlinear Systems

In this article, a modified self-recurrent wavelet neural network (MSRWNN) structure is used as a feedforward controller in the direct inverse control (DIC) method. Nonlinear dynamical systems are controlled with the help of this intelligent control strategy. The particle swam optimization (PSO) is used as an efficient optimization tool to determine the ideal MSRWNN parameter settings. A nonlinear dynamical system is considered to demonstrate the efficacy of the suggested control strategy. In addition, the ability of the MSRWNN to effectively regulate the nonlinear system under consideration is specifically assessed in terms of control accuracy and resilience to external disturbances via the execution of many assessment tests. The outcomes of each of these tests have shown the control scheme's effectiveness, with significant improvements in performance metrics. Specifically, the proposed method achieved a 25% reduction in Integral Squared Error (ISE) compared to traditional neural network controllers, and improved disturbance rejection capability by 30%. Furthermore, from a comparative study, the MSRWNN has demonstrated superior control accuracy and performance reliability over other related controllers. Index Terms— particle swarm algorithm, feedforward control, direct inverse control, MSRWNN.

Trajectory tracking of a quadrotor using model predictive and backstepping control with anti-disturbance compensation subjected to variations in the center of gravity

The trajectory tracking control of a quadrotor unmanned aerial vehicle is done in this paper in the presence of changes in the center of gravity due to the variations of a connected load mass and its position, external disturbances, and parametric uncertainties. At first, dynamic equations are obtained using Newton–Euler relations, and then a two-loop control architecture is designed for the trajectory tracking task. Backstepping and model predictive control are used in the inner and outer loops, respectively. An extended disturbance observer is adopted to improve disturbance rejection capabilities in both loops. For the six-degrees-of-freedom unmanned aerial vehicle, the effects of time-varying mass, which leads to the change in the moment of inertia and center of gravity of the system, together with external disturbances and parametric uncertainties are taken into account. It is possible to mount an arbitrary number of different point masses at arbitrary positions on the unmanned aerial vehicle. The proposed control scheme is evaluated using simulation in the presence of uncertainties and external disturbances. According to the simulation results, the developed control scheme can achieve stable flight under different conditions.

Research on composite anti-disturbance adaptive constraint control strategy for quadruped robot

To achieve the stable walking of a quadruped robot, a composite anti-disturbance adaptive constraint control strategy is proposed based on the analysis of the dynamic model. First, the leg control adopts a trajectory tracking constraint control algorithm based on a time-varying tangent-type barrier Lyapunov function, which ensures high-precision control with joint trajectory tracking errors consistently constrained within a time-varying boundary. To further enhance the robustness of the system, an adaptive algorithm is used to compensate for external disturbances and system uncertainties. Second, the body posture control of the quadruped robot adopts the Virtual Model Control algorithm, which achieves stable adjustments of the body posture and soft contact between the foot end and the ground. This further improves its disturbance rejection capability. Finally, a comparative study of control algorithms is being conducted using MATLAB/Simulink. The results show that under this control strategy, the quadruped robot’s motion exhibits good robustness and flexibility.

Design of optimal sliding mode control system for PMLSM

Abstract To enhance the responsiveness and resilience against disturbances in the speed regulation system of Permanent Magnet Linear Synchronous Motors (PMLSM), an advanced control approach leveraging optimized sliding mode control is introduced. Initially, a foundation is laid by designing a PI-based speed control system and a conventional sliding mode control system within the framework of PMLSM vector control. Simulation models are then constructed to validate the efficacy of these systems. Subsequently, focusing on the traditional sliding mode controller, optimizations are implemented to refine the sliding mode convergence dynamics and the switching function, leading to the development of an optimal sliding mode controller model. Ultimately, simulation tests confirm that this optimized sliding mode speed controller achieves seamless transition without overshoot, swift response times, and robust disturbance rejection capabilities. Its performance in terms of responsiveness and anti-interference surpasses both the PI controller and the conventional sliding mode speed controller.

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.

Locomotion Control of a Bipedal Wheeled Robot Using Virtual Model Control and Linear Quadratic Regulator Techniques

This paper aims to develop a balance control technique and investigates its impact on the stability and disturbance rejection capability of a bipedal wheeled robot. The bipedal wheeled robot is equivalent to a wheeled inverted pendulum nonlinear model with a legs-airframe centroid variable rod. The nonlinear model is linearized and decoupled into two subsystems: straight-line control using linear-quadratic regulator (LQR) for balance and speed, and steering control employing proportional integral derivative (PID). Height control adjusts the virtual force with PID-Feedforward, while hip torque is determined by virtual model control (VMC). MATLAB simulation confirms effective control of height, linear motion, and steering, with decoupling enhancing steering performance.

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.

Disturbance observer–based adaptive neural finite-time control for nonstrict-feedback nonlinear systems with input delay

In this paper, the problem of adaptive finite-time tracking control is investigated for nonstrict-feedback nonlinear systems with input delay. First, a novel form of auxiliary systems is presented as a feasible approach to compensate for the influence of input delay. Second, by leveraging a disturbance observer to approximate unknown disturbances, the disturbance rejection capability of nonlinear system is improved. Then, a novel finite-time command filter is introduced to cope with the problem of explosion of complexity, and the filtering errors are compensated within a specific time period by constructing an error compensation signal. Based on the finite-time stability theory, the tracking error is verified to converge to a prescribed performance range within a finite time, and the boundedness of all signals in the closed-loop system is ensured. Finally, simulation results are presented to demonstrate the effectiveness of the developed adaptive control scheme.

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.