Implementation Of Adaptive Controller Research Articles

A Survey of Visible-Light-Communication-Based Indoor Positioning Systems.

There is a growing demand for indoor positioning systems (IPSs) in a wide range of applications. However, traditional solutions such as GPS face many technical challenges. In recent years, a promising alternative has been emerging, the visible light communication (VLC)-based IPS, which offers a combination of high accuracy, low cost, and energy efficiency. This article presents a comprehensive review of VLC-based IPSs, providing a tutorial-like overview of the system. It begins by comparing various positioning systems and providing background information on their inherent limitations. Experimental results have demonstrated that VLC-based systems can achieve localization accuracy to within 10 cm in controlled environments. The mechanisms of VLC-based IPSs are then discussed, including a comprehensive examination of their performance metrics and underlying assumptions. The complexity, operating range, and efficiency of VLC-based IPSs are examined by analyzing factors such as channel modeling, signal processing, and localization algorithms. To optimize VLC-based IPSs, various strategies are explored, including the design of efficient modulation schemes, the development of advanced encoding and decoding algorithms, the implementation of adaptive power control, and the application of state-of-the-art localization algorithms. In addition, system parameters are carefully examined. These include LED placement, receiver sensitivity, and transmit power. Their impact on energy efficiency and localization accuracy is highlighted. Altogether, this paper serves as a comprehensive guide to VLC IPSs, providing in-depth insights into their vast potential and the challenges that they present.

A Novel Recursive Algorithm for the Implementation of Adaptive Robot Controllers

In this paper, a novel recursive and efficient algorithm for real-time implementation of the adaptive and passivity-based controllers in model-based control of robot manipulators is proposed. Many of the previous methods on these topics involve the computation of the regressor matrix explicitly or non-recursive computations, which remains as the main challenge in practical applications. The proposed method achieves a compact and fully recursive reformulation without computing the regressor matrix or its elements. This paper is based on a comprehensive literature review of the previously proposed methods, presented in a unified mathematical framework suitable for understanding the fundamentals and making comparisons. The considered methods are implemented on several processors and their performances are compared in terms of real-time computational efficiency. Computational results show that the proposed Adaptive Newton-Euler Algorithm significantly reduces the computation time of the control law per cycle time in the implementation of adaptive control laws. In addition, using the dynamic simulation of an industrial robot with 6-DoF, trajectory tracking performances of the adaptive controllers are compared with those of non-adaptive control methods where dynamic parameters are assumed to be known.

Design and implementation of adaptive control for industrial robot servo trajectory tracking

Abstract To cope with the complex external environment of the robot, an online adaptive control method for industrial robots is implemented by using robot dynamic parameters as control variables. A Newton-Euler method is implemented to establish a rigid body dynamics model for the robot. Subsequently, we confirm the possibility of dynamic parameter adaptive control by employing the Lyapunov stability theory and derive the dynamic parameter estimation law. Three methods are proposed to realize the observation matrix containing reference information in the adaptive control algorithm. Through physical experiments, it is verified that the dynamic parameter adaptive control is superior to traditional PPI control, and its position tracking error and anti-interference ability are significantly improved.

A New Fractional-Order Adaptive Sliding-Mode Approach for Fast Finite-Time Control of Human Knee Joint Orthosis with Unknown Dynamic

This study delves into the implementation of Fast Finite Time Fractional-Order Adaptive Sliding Mode Control (FFOASMC) for knee joint orthosis (KJO) in the presence of undisclosed dynamics. To achieve this, a novel approach introduces a Fractional-Order Sliding Surface (FOSS). In the context of limited knowledge regarding the dynamics of knee joint arthrosis, Fractional-Order Fast Adaptive Sliding Mode Control (FOFASMC) is devised. Its purpose is to ensure both finite-time stability and prompt convergence of the KJO’s state to the desired trajectory. This controller employs adaptive rules to estimate the enigmatic dynamic parameters of KJO. Through the application of the Lyapunov theorem, the attained finite-time stability of the closed loop is demonstrated. Simulation results effectively showcase the viability of these approaches and offer a comparative analysis against conventional integer-order sliding mode controllers.

МОДЕЛИРОВАНИЕ АДАПТИВНОГО РЕЖИМА РАБОТЫ РЕГУЛИРУЕМОГО ПЕРЕСЕЧЕНИЯ В СОСТАВЕ МАГИСТРАЛЬНОЙ АСУДД

It is recommended to use modelling with the help of specialized software products to make managerial decisions about traffic control on urban streets. The object of the study is the regulated intersection of Tobolskiy Trakt St. and Sudoremontnaya St. in Tyumen. During the simulation micromodelling using the SmartAdaptive+ software product for potential implementation of adaptive traffic light control, we show that the current scheme of coordinated control can be supplemented with adaptive control at one of the nodes to minimize delays in both the main and crossing directions. Vehicle delays and maximum queue lengths under adaptive and tight regulation were calculated and compared using the statistical test method. With a maximum permissive phase of 75 s on the major direction and of 20 s on the minor direction, the shortest delays will be observed on both directions of intersection during the day.

Robotic manipulator motion planning method development using neural network-based intelligent system

The research relevance is determined by the constant development of industry and the use of robotic manipulators in production processes. The study aims to develop an approach to planning the trajectory of a manipulator robot using an intelligent system based on neural networks. An analysis method, as well as special methods such as design, machine learning, integration strategies, and optimisation techniques, were used to achieve this goal. The main results of the study cover a wide range of achievements in the development of methods for planning the motion of robotic manipulators and their integration into real production conditions. The analysis of existing methods for planning the motion of robotic manipulators and a review of intelligent control systems provided a comprehensive picture of the current state of the art. The developed methods of robot manipulator trajectory identified effective control strategies that consider both dynamic and static scenarios. Training a neural network to plan the optimal path of movement made it possible to detect, track and avoid obstacles in real-time. Hierarchical path planning, adaptive neural network control, genetic algorithms for path optimisation, and dynamic prediction for obstacle avoidance were used to integrate the developed methods into a real production environment. The optimisation and improvement of the created approaches have shown positive results in improving the safety and performance of robotic manipulators, reducing the risk of collisions, and avoiding damage to robots. In addition, the implementation of hierarchical trajectory planning and adaptive neural network control contributed to a significant increase in the accuracy and stability of manipulator movements in various production process scenarios. The practical significance of the study is to develop an intelligent control system and methods for planning the movement of robotic manipulators, which contributes to the efficiency and safety of their operation in real production conditions

Adaptive differential evolution algorithm with a pheromone-based learning strategy for global continuous optimization

Abstract Differential evolution algorithm (DE) is a well-known population-based method for solving continuous optimization problems. It has a simple structure and is easy to adapt to a wide range of applications. However, with suitable population sizes, its performance depends on the two main control parameters: scaling factor (F ) and crossover rate (CR). The classical DE method can achieve high performance by a time-consuming tunning process or a sophisticated adaptive control implementation. We propose in this paper an adaptive differential evolution algorithm with a pheromone-based learning strategy (ADE-PS) inspired by ant colony optimization (ACO). The ADE-PS embeds a pheromone-based mechanism that manages the probabilities associated with the partition values of F and CR. It also introduces a resetting strategy to reset the pheromone at a specific time to unlearn and relearn the progressing search. The preliminary experiments find a suitable number of subintervals (ns) for partitioning the control parameter ranges and the reset period (rs) for resetting the pheromone. Then the comparison experiments evaluate ADE-PS using the suitable ns and rs against some adaptive DE methods in the literature. The results show that ADE-PS is more reliable and outperforms several well-known methods in the literature.

Integration of triboelectric sensing and electromagnetic energy harvesting for self-adaptive vibration suppression

There has been growing interest in using self-adjusting damping to suppress vibration in precious industrial equipment and transportation. Current vibration suppression strategies focus on improving the transformation mechanism for a specific environment. However, owing to space and power supply limitations, the implementation of energy recovery and adaptive vibration control remains a technical challenge. In this study, a random vibration suppression–energy harvesting coupling system (VS-EHCS) based on self-driven triboelectric sensing is developed. A magnetic-suspension-based energy harvester with a honeycomb structure is designed to enable energy harvesting and passive vibration suppression. Self-powered triboelectric sensors are integrated with the honeycomb to obtain vibration information. The energy harvester output and the sensor output are used as feedback for the adaptive signal tracking control algorithm, allowing a trade-off between active vibration suppression and energy harvesting. The proposed VS-EHCS achieves a vibration suppression efficiency of 96.2 % and peak power exceeding 200 mW under random vibrations of 15.9 g. These are sufficient to charge a 0.1 F super-capacitor to 5 V in 70 s. By combining self-driven monitoring, active-passive control, and energy harvesting, the VS-EHCS provides a new paradigm for efficient vibration suppression and energy recovery in variable-vibration environments.

Projection operator‐based robust adaptive control of an aerial robot with a manipulator

AbstractThis paper describes a quadcopter manipulator system, an aerial robot with an extended workspace, its controller design, and experimental validation. The aerial robot is based on a quadcopter with a three degree of freedom robotic arm connected to the base of the vehicle. The work aims to create a stable airborne robot with a robotic arm that can work above and below the airframe, regardless of where the arm is attached. Integrating a robotic arm into an underactuated, unstable system like a quadcopter can enhance the vehicle's functionality while increasing instability. To execute a mission with accuracy and reliability during a real‐time task, the system must overcome the inter‐coupling effects and external disturbances. This work presents a novel design for a robust adaptive feedback linearization controller with a model reference adaptive controller and hardware implementation of the quadcopter manipulator system with plant uncertainties. The closed‐loop stability of the aerial robot and the tracking error convergence with the robust controller is analyzed using Lyapunov stability analysis. The quadcopter manipulator system is custom developed in the lab with an off‐the‐shelf quadcopter and a 3D‐printed robotic arm. The robotic system architecture is implemented using a Jetson Nano companion computer for autonomous onboard flight. Experiments were conducted on quadcopter manipulator system to evaluate the autonomous aerial robot's stability and trajectory tracking with the proposed controller.

Low-Cost Implementation of an Adaptive Neural Network Controller for a Drive with an Elastic Shaft

This paper deals with the implementation of an adaptive speed controller applied for two electrical machines coupled by a long shaft. The two main parts of the study are the synthesis of the neural adaptive controller and hardware implementation using a low-cost system based on an STM Discovery board. The framework between the control system, the power converters, and the motors is established with an ARM device. A radial basis function neural network (RBFNN) is used as an adaptive speed controller. The net coefficients are updated (online mode) to ensure high dynamics of the system and correct work under disturbance. The results contain transients achieved in simulations and experimental tests.

Adaptive proportional derivative decentralized output based controller for a biped robotic device

This paper describes the design and implementation of a novel adaptive control method to track a set of bioinspired reference trajectories. These references define anthropomorphic movements for an exoskeleton robot. The proposed controller implemented the adjustment laws for the variable gains of a state feedback (Proportional-Derivative type) structure. The method to adjust the adaptive gains was determined using a controlled Lyapunov function. The adaptation laws use velocity estimation based on a robust exact differentiator (RED) implemented as a variation of a distributed Super-Twisting algorithm. The adaptive gain controller was evaluated on a simulated exoskeleton structure. The set of simulations considered the presence of external disturbances and modeling uncertainties. The controller proved efficient in rejecting external perturbations/uncertainties affecting the exoskeleton. The proposed controller’s performance was superior to the one obtained if the standard fixed-gain proportional derivative controller was evaluated. As an additional benefit of the adaptive PD controller implementation, a controller power reduction of at least 14 [Formula: see text] concerning the non-adaptive version of the feedback controller was attained. An experimental evaluation of the proposed controller confirmed the benefits of the proposed controller with adaptive gains. The successful tracking of nine different biomechanically inspired reference trajectories justified the exoskeleton application, which could be used as a potential tool for rehabilitation purposes.

Real‐time implementation of adaptive neural backstepping controller for battery‐less solar‐powered PMDC motor

A low-cost battery-less solar-powered PMDC motor using an adaptive backstepping Chebyshev neural network controller to track the desired speed for any change in irradiance and load torque is proposed in this paper. The neural network is used for approximating the variable load torque because of its approximation property. The computational burden of the control law is reduced because of the orthogonal property of Chebyshev polynomials. The asymptotically stable system is obtained by tuning the weights of neurons in accordance with the Lyapunov stability analysis. From the Lyapunov control function of backstepping control design procedure, the control law is obtained by an innovative way of elucidating the cubic equation, in place of resolving the derivative of the control law in the control function. This approach eliminates the constraints caused by the non-strict feedback system for the backstepping control approach and also this reduces ripples in the duty cycle which makes its appropriateness in real-time. To ensure its robustness in tracking the desired speed at a faster time and minimum overshoot, simulations are done for an extensive range of variations in irradiance and load torque, and the obtained results are assessed by comparing it with the PID controller and conventional backstepping controller. Because of the use of neural network the robustness of the proposed controller is ensured with enhanced transient and steady state responses. A prototype is developed in the laboratory and the obtained results are assessed by comparing it with the backstepping controller and PID controller.

Learning torsional eye movements through active efficient coding

The human eye has three rotational degrees of freedom: azimuthal, elevational, and torsional. Although torsional eye movements have the most limited excursion, Hering and Helmholtz have argued that they play an important role in optimizing visual information processing. In humans, the relationship between gaze direction and torsional eye angle is described by Listing’s law. However, it is still not clear how this behavior initially develops and remains calibrated during growth. Here we present the first computational model that enables an autonomous agent to learn and maintain binocular torsional eye movement control. In our model, two neural networks connected in series: one for sensory encoding followed by one for torsion control, are learned simultaneously as the agent behaves in the environment. Learning is based on the active efficient coding (AEC) framework, a generalization of Barlow’s efficient coding hypothesis to include action. Both networks adapt by minimizing the prediction error of the sensory representation, subject to a sparsity constraint on neural activity. The policies that emerge follow the predictions of Listing’s law. Because learning is driven by the sensorimotor contingencies experienced by the agent as it interacts with the environment, our system can adapt to the physical configuration of the agent as it changes. We propose that AEC provides the most parsimonious expression to date of Hering’s and Helmholtz’s hypotheses. We also demonstrate that it has practical implications in autonomous artificial vision systems, by providing an automatic and adaptive mechanism to correct orientation misalignments between cameras in a robotic active binocular vision head. Our system’s use of fairly low resolution (100 × 100 pixel) image windows and perceptual representations amenable to event-based input paves a pathway towards the implementation of adaptive self-calibrating robot control on neuromorphic hardware.

The Implementation of Fuzzy PSO-PID Adaptive Controller in Pitch Regulation for Wind Turbines Suppressing Multi-Factor Disturbances

The stability of wind turbine output is affected by multi-factor disturbances, which makes traditional proportion-integral-differential (PID) control strategy hardly achieve a satisfactory effect. Hence, a pitch control strategy based on fuzzy adaptive particle-swarm-optimization (PSO)-PID is proposed in this paper. Firstly, the models of wind turbine, pitch regulator, and permanent magnet synchronous generator (PMSG) are built. Then, the influence of various disturbances, such as blade deformation, installation errors, and external wind speed, on the wind wheel speed is analyzed. Furtherly, a wind turbine pitch control strategy is devised in detail, in which the PID parameters are initially optimized by PSO and adaptively adjusted by fuzzy controller. Simulations under gust wind and turbulent wind conditions are carried out, and the results show that compared with fuzzy PID and PID, the fuzzy adaptive PSO-PID could reduce errors of wind wheel speed and wind turbine output power effectively.

Real-time implementation of adaptive nonlinear control of Buck-Boost DC-DC power converter with a continuous input current for fuel cell energy sources

Classical Buck-Boost converter (BBC) topologies are known to drain a pulsating current from the input power source which accelerates the aging rate of the fuel cell (FC). In this paper, a Buck-Boost DC-DC converter topology with continuous input current is used. In addition to its input current, the converter offers the advantage of using a reduced number of electronic components. The point is that the internal voltage of the fuel cell is not accessible for measurement. Therefore, based on a nonlinear model of the FC-BBC system, an adaptive nonlinear controller based on the Backstepping technique is developed to meet the following requirements: (i) tracking the DC-bus voltage to its desired value despite the load uncertainties, (ii) asymptotic stability of the closed-loop system using Lyapunov stability tools. The advantage of the proposed control strategy lies in its simplicity compared to the existing nonlinear approaches. Detailed analysis, simulation, and experimental tests show that the proposed controller meets all control objectives, namely: tight dc-bus voltage regulation and asymptotic stability of the closed-loop system. The effectiveness of the proposed approach is verified by simulation using MATLAB® / Simulink®, and by experimental tests using DS 1202 MicroLabBox.

Adaptive control of DC motor without identification of parameters

Parameter identification is a major problem in industrial environments where it might be difficult or even impossible in some situations. Moreover, non-measurable and unknown variations of system parameters can affect the performance of conventional proportional-integral (PI) controllers. The concept of developing a controller that does not depend on the system parameters seems very interesting. Therefore, this paper deals with the experimental implementation of model reference adaptive control of a DC motor without identifying parameters. Adaptive control is considered an online solution to control a system without knowing system parameters since it can be adjusted automatically to maintain favorable tracking performance. The simulation and experimental results are presented to demonstrate the effectiveness of the proposed control method.

Modelling, simulation and implementation of a hybrid model reference adaptive controller applied to a manipulator driven by pneumatic artificial muscles

AbstractThe present research aims to model, simulate and implement a new hybrid control approach based on a combination of proportional integral derivative (PID) Controller and Model Reference Adaptive Controller (MRAC), in which Lyapunov’s theory is used to ensure asymptotic stability to control a two degrees of freedom (DoF) manipulator driven by McKibben’s artificial pneumatic muscles. The MRAC controller works as a nonlinearity compensator and PID controller works during the transient period, as the MRAC performs poorly in this regime. This new approach is entitled Hybrid Model Reference Adaptive Controller (H-MRAC) and it has an unprecedented topological structure based on three terms. The feedforward term acts in disturbances rejection, the derivative term in oscillations damping and the feedback term acts in error convergence to zero. In this article, a control system dedicated to pneumatic manipulators was developed. As a result, proof of asymptotic convergence was performed for the proposed topological approach, which was validated on a two DoF manipulator. The proposed mechanism satisfactorily met the ISO/TS 15066 standard, and the position tracking obtained a global error of 37.69% and 51.01% smaller than found in the literature examples, entitled MRAC and A-PID, respectively, for simulations and 37.46% and 30.25% for experiments.

A distributed control wireless system for environmental humidity determination based on adaptive controller model

Environmental conditioning is a major research application area in the concept of control theory. The parameter of interest is mostly measured and evaluated against a reference in determining the initialisation threshold for the designed systems. This paper has implemented a system for controlling the heat parameters of a dwelled environment based on the integration of Global System for Mobile Communication (GSM) features into a Wireless Sensor Network (WSN) system. A model is developed to describe the temperature evolution in the constituent electronic units of the thermally active system, as a function for determining the environmental humiture impact. The generated heat and the forced cooled air by convective flow across the neighbouring devices, and consequently, the networked wireless nodes within the defined space are also modelled and analysed for adaptive control implementation. Both linear and non-linear terms are presented in the discrete set of equations representing the mathematically modelled system. Two scenarios were simulated and analysed: centralized model adaptive control (CMAC) and decentralized model adaptive control (DMAC). The evaluation of the control methods is ascertained by comparing with classic P/PI and on/off controllers, using a very low inoccupation temperature reference while increasing the occupation period in the adaptive control prediction horizon. The decentralized model adaptive controller provides improved performance in the reduction of the consumption index by about 7.2%, while the distributed and centralized schemes show an improvement in the thermal comfort with about 39.2% and reduction in the consumed energy by about 12.2%. A suitable conclusion is being made on the optimal placement of the nodes to ensure maximum convective cooling of the space, derived from the simulated results of the models and the adjourning tempering algorithm. The uniqueness and existence of the solutions are established through the convergence of the steady-state coefficients. The research has introduced a novel designer-friendly relative humidity-enabled adaptive control with significant range extension for acceptable indoor conditions in the design of low-energy naturally-conditioned facilities in sub-Saharan Africa.

Youla–Kucera based multi-objective car following controller

This paper presents the design and implementation of a novel multi-objective Adaptive Cruise Controller (ACC). The proposed control strategy is based on Youla–Kucera parametrization to interpolate two ACC controllers with conflicting objectives: (1) a performant controller with fast preceding tracking capabilities; and (2) a robust controller with perception noise attenuation capabilities within the system bandwidth. The proposed multi-controller structure uses tools of both performance and robust control in order to adapt car-following behavior to perception system status. The solution is implemented in an automated Renault ZOE, and compared to H∞ controller, providing encouraging results.

Design and implementation of event-triggered adaptive controller for commercial mobile robots subject to input delays and limited communications

Nowadays, tracking control of mobile robots under network circumstances is a fertile topic of great research interest in control and robotics communities. In this paper, an adjustable event-triggered mechanism based on Lyapunov analysis is proposed to reduce the communication burden in the controller-to-robot channel. Besides, a compensation system is introduced to deal with input delays. Unlike other dynamic models of mobile robots that consider torques or voltages as control inputs, the derived dynamic model admits direct commands of desired linear and angular velocities which is highly desirable for commercial mobile robots available in the market. Due to the unavailability of internal parameters of the robot, an adaptive law is designed to estimate them on-line during the operation. The proposed event-triggered adaptive control scheme (ETAC) is experimentally validated on a commercial robot (PatrolBot). The obtained results are consistent with simulations and show a significant saving in bandwidth usage compared to traditional time-triggered implementation.