PD Controller Research Articles

Unmanned Agricultural Machine Operation System in Farmland Based on Improved Fuzzy Adaptive Priority-Driven Control Algorithm

Autonomous driving technology for agricultural machinery can maximise crop yield, reduce labour costs, and alleviate labour intensity. In response to the current low degree of automation and low tracking accuracy of driving paths in agricultural equipment, this research proposes an unmanned agricultural machinery operating system based on an improved fuzzy adaptive PD control algorithm. Firstly, mechanical kinematic models and fuzzy adaptive control algorithms are introduced to achieve autonomous driving, and parameter settings and speed adjustments are made while considering errors. Secondly, in the autonomous driving operation system, taking a certain rice machine as an example, perception information, trajectory design, dynamic control, operation supervision, and remote control design are carried out. The experimental results show that the improved fuzzy algorithm exhibits smaller deviation results in driving path tracking, with an average error between the actual path and the expected path of less than 0.001 m. In different testing scenarios, compared with the actual control results, the maximum deviation of the control system platform in straight sections is less than 2.8 m, which is more stable. More than 95% of the lateral deviation results in the road sections are within 0.11 m. And the tracking distance error of the proposed method in the straight and curved segments is relatively small, far smaller than other comparative algorithms. The unmanned agricultural machinery operation system proposed in this study can significantly improve the efficiency and accuracy of agricultural machinery work, promote the development of intelligent and modern agricultural machinery, and provide reference value and important contributions to social and economic development as well as the progress and promotion of related technologies.

Trajectory optimization and stability control for a novel unmanned intelligent wheel-legged vehicles on unstructured terrains

The trend of intelligent vehicles is currently expanding, and a new class of unmanned intelligent vehicles known as wheel-legged vehicles (WLVs) is emerging. WLVs excel in transportation on unstructured terrain by offering a unique combination of the efficiency of wheels on flat ground and the versatility of legs to tackle obstacles. To enhance the locomotion performance of WLVs on unstructured terrains, this paper presents a novel framework for improving their locomotion through nonlinear programming-based (NLP) trajectory optimization and stability control. The framework optimizes the vehicle’s body and wheel positioning while incorporating terrain information and employs a linear rigid body dynamic model for efficient motion planning. The stability control framework combines feedforward control using ground reaction forces with feedback control through joint PD control and utilizes model predictive control (MPC) to adjust the wheel slip ratio to prevent slip on steep slopes. Experimental validation on the real vehicle with torque-controlled wheels demonstrated the capability of driving over a 1 m height with a 30°slope at an average speed of 0.7 m/s and a maximum speed of 1.03 m/s. Our approach also enables the WLV to overcome obstacles, such as inclines, while dynamically negotiating these challenging terrains.

LIDAR adaptive parameters selection and target pointing control for close-proximity space operations

This paper deals with advanced Guidance, Navigation and Control (GNC) functions required to enable autonomous and safe operations of a chaser spacecraft in close-proximity to an uncooperative space target, as in Active Debris Removal or On-Orbit servicing scenarios. Specifically, it presents an original approach to autonomously and adaptively select the field of view and resolution of a scanning LIDAR to improve both state estimation accuracy and computational efficiency of a LIDAR-based relative navigation system. In general, the correct operation of such system is also determined by the capability to keep the boresight axis of the relative navigation sensor aligned with the target geometric center. To address this task, an original control technique, based on the sliding-mode formulation and relying on a reduced attitude representation, is proposed. This control scheme is also compared to state-of-the-art PD and PID approaches in terms of pointing accuracy and control effort. The proposed techniques have been numerically validated in a simulation environment integrating the chaser attitude control and the LIDAR-based relative navigation functions in a closed-loop architecture. The simulation environment realistically reproduces the generation of LIDAR-based point clouds, and the spacecraft relative dynamics in close proximity. Results show that the adaptive selection of the LIDAR operational parameters improves the relative navigation performance, while the proposed sliding-mode control guarantees higher pointing accuracy than PD and PID control approaches.

Functional neural substrates of Parkinson’s disease and potential underpinnings of acute responses to acupuncture stimulation

Parkinson’s disease is a heterogenous neurodegenerative disorder with a wide variety of motor and non-motor symptoms. This study used resting-state fMRI to identify the neural substrates of PD and explore the acute neural response to acupuncture stimulation in 74 participants (50 patients with PD and 24 healthy controls). All participants with PD were evaluated for the severity of symptoms using the Unified Parkinson’s Disease Rating Scale and Balance Master. The z-transformed fractional amplitude of low-frequency fluctuation analysis showed significant differences between the PD and healthy controls in the cerebellar regions, which are thought to play a crucial role in PD pathology. Subsequently, seed-based functional connectivity of the cerebellum with the frontal, parietal, and limbic regions was identified as a potential diagnostic marker for PD. In addition, spontaneous neural activity in the precentral gyrus and thalamus was significantly associated with the severity of PD symptoms. Neural activity in the precentral gyrus, precuneus, and superior temporal gyrus showed a significant correlation with Balance Master indicators. Finally, acupuncture stimulation at GB34 significantly reduced the activity of the occipital regions in patients with PD, but this effect was not observed in healthy controls. The mixed-effects analysis revealed an interaction effects between group and acupuncture stimulation, suggesting that the modulatory effects of acupuncture could differ depending on disease status. Therefore, this study suggests the neural substrates of PD and potential underpinnings of acute neural response to acupuncture stimulation.

TPat: Transition pattern feature extraction based Parkinson’s disorder detection using FNIRS signals

Background and ObjectiveParkinson’s Disease (PD) is one of the most commonly observed neurodegenerative disorders worldwide. Many researchers have utilized machine learning (ML) models to detect PD and understand its underlying causes automatically. In this research, our primary objective is to automatically detect PD and extract meaningful results using the proposed ML model. Materials and MethodsIn this study, an FNIRS dataset collected from PD patients and control participants under three conditions—(i) rest, (ii) walking, and (iii) finger tapping—was utilized. A new explainable feature engineering (XFE) model was proposed to detect PD and automatically extract meaningful information under these conditions. The XFE model consists of four main phases: (i) feature extraction using the proposed channel transformation and transition pattern (TPat), (ii) feature selection employing cumulative weighted neighborhood component analysis (CWNCA), (iii) classification using the k-nearest neighbors (kNN) classifier, and (iv) channel network extraction to obtain explainable results. ResultsThe suggested TPat-based XFE model was applied to the FNIRS dataset. This dataset included three distinct cases. Our model achieved over 94% classification accuracy using leave-one-subject-out cross-validation (LOSO CV) and 100% classification accuracy using 10-fold cross-validation. Additionally, channel transitions for each case were identified and discussed. ConclusionsBased on the results and findings, the proposed model demonstrated high accuracy in FNIRS signal classification and provided explainable results. In this regard, the presented TPat-based XFE model contributed significantly to both ML and neuroscience.

Collaborative control of circumferential yarn implantation system for large-scale 3D braiding equipment using fast nonsingular terminal sliding mode

To ensure high performance collaborative control of the circumferential yarn implantation system for a specific large-scale 3D braiding equipment, a multi-motor fast nonsingular terminal sliding mode (FNTSM) controller is proposed using time-delay estimation (TDE) and deviation coupling control (DCC). Then, comparative experiments were conducted among our proposed TDE-based FNTSM controller and PD controller and TDE-based PD controller. As shown in the experimental results, the root-mean-square error (RMSE) and maximum absolute error (MAE) ensured by our FNTSM controller tracking sine signal have decreased by 89.4% and 67.1% compared with the ones by PD controller; while the RMSE of our FNTSM controller tracking slope signal is ensured within 0.03°. Meanwhile, the RMSE and MAE of our FNTSM controller tracking sine signals has been reduced by 48.2% and 56.9% due to DCC, respectively. Afterwards, less than 3% error fluctuation has been observed with load using our FNTSM controller. High control performance and strong robustness have been experimentally observed.

Body image appearance in Parkinson's disease patients on device aided therapies.

Parkinson's disease patients may experience altered body image perception. Advanced Parkinson's disease (aPD) with motor complications often requires surgical and device-aided treatments (DAT), such as levodopa-carbidopa intestinal gel (LCIG) and deep brain stimulation (DBS). Understanding body image perception is crucial when managing these devices. This study aims to explore body image perception in aPD patients, hypothesizing a link between DAT and body image perception. We performed a cross-sectional study including non-demented aPD patients with and without DAT and age- and sex-matched controls. Participants were assessed using the Appearance Schemas Inventory-Revised (ASI-R), including Motivational Salience (MS) and Self-Evaluative Salience (SES) scores. Additional data included age, education, BMI, comorbidities, pharmacotherapy, and psychopathologies. PD patients were also evaluated with UPDRS, Hoehn and Yahr scales and LEDD calculation. 70 aPD and 36 controls were enrolled. No differences in ASI-R scores were found between PD patients and controls, but women with PD had significantly lower MS scores than controls (16.1 ± 5.6 vs 19.7 ± 5.8; p = 0.023). Among aPD patients, those on DAT had longer disease duration, higher Hoehn and Yahr, and lower UPDRS IV scores. The lowest MS was observed in women on LCIG (12.7 ± 3.3; p = 0.001). This study shows low MS ratings driven by female gender and LCIG treatment. Women on LCIG show reduced attention and management of their appearance. This may be influenced by cultural, environmental, and biological factors. Prospective research is needed to understand the impact of DAT on body image.

A - 96 Cognitive Profiles of Patients with Essential Tremor and Parkinson’s Disease: a Comparative Study

Abstract Objective Essential tremor (ET) is traditionally thought of as a pure motor condition1 defined by isolated action tremor. However, research suggests cognitive decline is more prevalent in ET than originally proposed2. Movement Disorder Society guidelines recommend patients with ET and cognitive decline be classified “ET-Plus3”. ET-related cognitive decline may occur through breakdown of prefrontal integrity via frontocerebellar networks. This is similar to frontosubcortical dysfunction in PD, which may result in similar cognitive changes. The present study compared cognitive profiles of patients with ET to PD and healthy controls (HC). Method Data was obtained through Baylor College of Medicine’s Neuropsychology Repository. 51 ET patients were compared to 190 PD patients and 189 age-matched HC. Group differences were assessed with one-way ANOVAs and post-hoc t-tests. Results ET performed worse than HC on multiple cognitive measures including executive functioning, processing speed, and memory. Largest effects were observed on Trailmaking B (p < 0.001, d = 0.93) and Stroop Word Reading (p < 0.001, d = 1.15). ET performed similarly to PD across most measures, although PD performed worse on Logical Memory I & II (p < 0.001, d = 0.57), and Clock Drawing (p = 0.001, d = 0.49). Rates of mild cognitive impairment were similar in ET (45%) and PD (44%). Conclusion(s) Findings suggest cognitive deficits in ET occur in a similar pattern and at a similar rate as in PD. The findings are consistent with research exploring the phenotypic heterogeneity of ET and provide evidence against the prevailing belief that ET is monosymptomatic.

2D Path Following Control of Motor Graders

Optimal ground conditions in mining are crucial for operational efficiency and safety. Motor graders play a crucial role in achieving precise grading of passages; however, unlike many other mining vehicles, they have not yet been commercially automated. The redundant kinematics of motor graders, including articulation, front axle steering, and blade operations, pose significant challenges for automation. This research explores path following with a 2D kinematic model of motor graders and uses simulation to evaluate and compare the performance of these controllers in terms of step response and operational effectiveness. To address kinematic redundancy, two methods are proposed: Rear Offset Control, which defines the articulation angle using a lateral offset between the front and rear axles, and Single Track Control, which utilizes steering redundancy to adapt existing automation approaches to articulated vehicles. Simulation tests were conducted using two controllers: a Feedback Linearized PD controller (FBL+PD) and a Feedback Linearized Model Predictive Controller (FBL+MPC). Both were tuned for optimal performance on a step input path, with performance assessed based on Root Mean Square Error (RMSE) and control effort. This work introduces two methodologies—Rear Offset Control and Single Track Control—for autonomous motor grader operation. These were implemented with FBL+PD and FBL+MPC controllers on a 2D kinematic model. Future research will focus on dynamic modeling, implementing a Non-Linear Model-Predictive Controller, refining tuning methodologies for Feedback Linearized systems, and integrating these approaches with live vehicles.

Enhanced PD Controller for Speed Control of Electric Vehicle Based on Gorilla Troops Algorithm

Enhanced PD Controller for Speed Control of Electric Vehicle Based on Gorilla Troops Algorithm

Cortical networks of parkinsonian gait: a metabolic and functional connectivity study.

Locomotion is an automated voluntary movement sustained by coordinated neural synchronization across a distributed brain network. The cerebral cortex is central for adapting the locomotion pattern to the environment and alterations of cortical network dynamics can lead to gait impairments. Gait problems are a common symptom with a still unclear pathophysiology and represent an unmet therapeutical need in Parkinson's disease. Little is known about the cortical network dynamics of locomotor control in these patients. We studied the cortical basis of parkinsonian gait by combining metabolic brain imaging with high-density EEG recordings and kinematic measurements performed at rest and during unperturbed overground walking. We found significant changes in functional connectivity between frontal, sensorimotor, and visuomotor cortical areas during walking as compared to resting. Specifically, hypokinetic gait was associated with poor information flow from the supplementary motor area (SMA) to precuneus and from calcarine to lingual gyrus, as well as high information flow from calcarine to cuneus. Our findings support a role for visuomotor integration processes in PD-related hypokinetic gait and suggest that reinforcing visual information may act as a compensatory strategy to allow SMA-mediated feedforward locomotor control in PD.

An experimental study on attitude control of a tailless hummingbird-mimetic flapping-wing robot with defective wings

Hummingbirds demonstrate remarkable robustness to wing defects by maintaining stable hovering during the molting process. However, mimicking these capabilities in flapping-wing robots presents significant challenges. To investigate the effectiveness of various controllers in maintaining the attitude of a hummingbird-mimetic flapping-wing robot against external disturbances under different wing defects, this study implemented a PD controller, a PID controller, and a three-loop feedback controller with a disturbance observer (3L-DOB controller). The flapping-wing robot features a pair of wings and controls its body’s yaw, pitch, and roll rotations by modulating the tension of wing membranes and the neutral positions of wing torsion. The performance of these controllers was evaluated through semi-tethered experiments on a gimbal, employing three Wing Sets: intact wings, one wing was defective, and both wings were defective. The defective wing emulated hummingbird wing during molting, in which the wing area was cropped by 14.1%. As a result, the 3L-DOB controller showed the best performance in terms of responsiveness and accuracy across all Wing Sets, while the PID controller also achieved comparable performance.

A new wake superposition model and its application in collision prevention control of tri-riser array system

In the field of offshore oil exploitation, the compact multi-riser arrangement increases the risk of collision and has an adverse impact on production and the marine environment. The solution to this problem involves accurate motion prediction and collision prevention control under the effect of flow field superposition between risers, but there is little research on this related theory. This paper improves the traditional wake shielding effect model and further proposes a wake shielding superposition effect model for the mutual influence between risers, in order to establish a comprehensive accurate motion control model for tri-riser array system. A solution method is introduced for this model, both vibration and collision analysis are conducted for the system. The study revealed that solely augmenting the spacing inadequately mitigates system collisions, and active control is an inevitable choice. Based on this, this paper further designs an active boundary controller that is easy to implement in engineering, to achieve collaborative collision prevention control for three risers, and verifies its stability. Compared with traditional PD control, the proposed control method exhibits significant superiority, which can effectively reduce vibration amplitude, significantly increase dynamic spacing, and thus effectively prevent collisions between risers.

Enhanced control of double integrating plus time delay systems using a two‐degree‐of‐freedom Smith predictor

AbstractThis article presents a novel control approach using a two‐degree‐of‐freedom (2‐DOF) Smith predictor structure for double integrating plus time delay plants. The PD and PID controllers, integral components of the Smith predictor scheme, serve distinct roles in the control process. The PD controller is responsible for set‐point tracking, while both PD and PID controllers contribute to the rejection of load disturbances. Each controller is designed using the pole placement technique, with an appropriate tuning parameter established to balance performance and robustness effectively. A low‐pass filter is incorporated to improve the response of the set‐point signal by attenuating high‐frequency noise. The proposed method's effectiveness is compared with Skogestad internal model control, a well‐known PID tuning method, to benchmark performance improvements and demonstrate the advantages of the 2‐DOF Smith predictor approach. Simulation and comparison analysis demonstrates that the proposed controller setting is suitable for industrial applications due to its quick set‐point tracking, effective load disturbance rejection, and smooth control signal. The study highlights the practical benefits of the 2‐DOF Smith predictor in enhancing industrial control system performance and suggests directions for future research, including experimental validation and robustness analysis under varying system parameters.

Fault-Tolerant Control for Multi-Quadcopter with Suspended Payload under Wind Disturbance

Delivering payload with multiple quadcopters necessitates a reliable backup system. This research introduces a fault-tolerant design specifically for multi-drone payload transportation. The system employs formation control, ensuring the weight is evenly distributed among all functioning drones. This research tackles the challenge of reliable payload delivery with multi-drone systems. It proposes a new fault-tolerant control system specifically designed for this purpose. The system addresses a limitation in existing solutions by incorporating a simple PD controller alongside a fault-tolerant strategy. This approach allows the system to maintain operation even if a drone malfunctions. The paper further demonstrates the system's effectiveness through simulations. Results show the system's ability to maintain stability with minimal altitude loss (only 6.3cm) and rapid position reconfiguration (within 3.96 seconds) even under windy conditions. These findings highlight the potential of this fault-tolerant design to significantly improve multi-drone payload delivery, especially for missions requiring high levels of stability and redundancy.

Dynamic Analysis and PD Control in a 12-Pole Active Magnetic Bearing System

This paper conducts an in-depth study on the dynamic stability and complex vibration behavior of a 12-pole active magnetic bearing (AMB) system considering gravitational effects under a PD controller. Firstly, based on electromagnetic theory and Newton’s second law, a two-degree-of-freedom control equation of the system, including PD control terms and gravitational effects, is constructed. This equation involves not only parametric excitation, quadratic nonlinearity, and cubic nonlinearity but also a more pronounced coupling effect between the magnetic poles due to the presence of gravity. Secondly, using the multi-scale method, a four-dimensional averaged equation of the system in Cartesian and polar coordinates is derived. Finally, through numerical analysis, the system’s amplitude–frequency response, motion trajectory, the relationship between energy and amplitude, and global dynamic behaviors such as bifurcation and chaos are discussed in detail. The results show that the PD controller significantly affects the system’s spring hardening/softening characteristics, excitation, amplitude, energy, and stability. Specifically, increasing the proportional gain can quickly suppress the rotor’s motion, but it also increases the system’s instability. Adjusting the differential gain can transition the system from a chaotic state to a stable periodic motion.

Model-based controllers for CubeSat ORU installation: A comparative study

The increasing space debris population in critical orbits due to spacecraft failure dictates the need for action. On-Orbit Servicing (OOS) has been proposed as a method for mitigating this trend by repairing existing space assets. The development of large servicing spacecraft has been given significant attention. In this paper, we are approaching the problem with a new paradigm for a servicing spacecraft by proposing a CubeSat class servicer equipped with a one-degree-of-freedom (DoF) robotic arm. Considering the OOS context, we choose the Orbital Replacement Unit (ORU) installation on a target spacecraft as a challenging benchmark task for the proposed servicer configuration. To carry out this task, we formulate and compare performance of four controllers to achieve coordinated control of both the CubeSat and the robotic arm for the installation task. We start with a basic PD controller and progress to the more advanced impedance controller, Model Predictive Control (MPC) and Model Predictive Impedance Control (MPIC) designs. The controllers are evaluated and compared across several relevant metrics for the ORU installation and their merits for practical implementation are discussed.

Load frequency control in interconnected microgrids using Hybrid PSO–GWO based PI–PD controller

Load frequency control in interconnected microgrids using Hybrid PSO–GWO based PI–PD controller

Implementation of partially tuned PD controllers of a multirotor UAV using deep deterministic policy gradient

The present methodology employed in classical control systems is characterized by high costs, significant processing requirements, and inflexibility. In conventional practice, when the controller exhibits instability after being implemented on the hardware, it is often adjusted to achieve stability. However, this approach is not suitable for mass-produced systems like drones, which possess diverse manufacturing tolerances and delicate stability thresholds. The aim of this study is to design and evaluate a controller for a multirotor unmanned aerial vehicle (UAV) system that is capable of adapting its gains in accordance with changes in the system dynamics. The controller utilized in this research employs a Simulink-constructed model that has been taught by reinforcement learning techniques, specifically employing a deep deterministic policy gradient (DDPG) network. The Simulink model of the UAV establishes the framework within which the agent engages in learning through interaction with its surroundings. The DDPG algorithm is an off-policy reinforcement learning technique that operates in continuous action spaces and does not require a model. The efficacy of the cascaded PD controllers and neural network tuner is evaluated. The results revealed that the controller exhibited stability during several flight phases, including take-off, hovering, path tracking, and landing manoeuvres.

How the brain can be trained to achieve an intermittent control strategy for stabilizing quiet stance by means of reinforcement learning

The stabilization of human quiet stance is achieved by a combination of the intrinsic elastic properties of ankle muscles and an active closed-loop activation of the ankle muscles, driven by the delayed feedback of the ongoing sway angle and the corresponding angular velocity in a way of a delayed proportional (P) and derivative (D) feedback controller. It has been shown that the active component of the stabilization process is likely to operate in an intermittent manner rather than as a continuous controller: the switching policy is defined in the phase-plane, which is divided in dangerous and safe regions, separated by appropriate switching boundaries. When the state enters a dangerous region, the delayed PD control is activated, and it is switched off when it enters a safe region, leaving the system to evolve freely. In comparison with continuous feedback control, the intermittent mechanism is more robust and capable to better reproduce postural sway patterns in healthy people. However, the superior performance of the intermittent control paradigm as well as its biological plausibility, suggested by experimental evidence of the intermittent activation of the ankle muscles, leaves open the quest of a feasible learning process, by which the brain can identify the appropriate state-dependent switching policy and tune accordingly the P and D parameters. In this work, it is shown how such a goal can be achieved with a reinforcement motor learning paradigm, building upon the evidence that, in general, the basal ganglia are known to play a central role in reinforcement learning for action selection and, in particular, were found to be specifically involved in postural stabilization.