Good Tracking Research Articles

Composite-disturbance-observer-based backstepping control for three-phase inverters with multiple disturbances

This paper deals with the output voltage tracking control problem of three-phase inverters with multiple disturbances (including parametric perturbations of filter, abrupt disturbances caused by load switching, and harmonic disturbances brought by unbalanced loads and nonlinear loads). Some novel composite disturbance observers are designed to estimate the above multiple disturbances. Based on the disturbance estimates and backstepping control technique, an innovative feedforward-feedback composite tracking control scheme is proposed. The new control scheme has a good disturbance rejection ability and ensures good tracking performances. In the meantime, rigorous stability proof is presented. Simulation and experimental results are illustrated to verify the feasibility and effectiveness of the proposed control scheme.

A novel wearable pouch-type pneumatic artificial muscle with contraction and force sensing

In this study, a novel pouch-type pneumatic artificial muscle for wearable applications with a high contraction ratio was proposed. It utilizes a rotary plate-belt mechanism to transfer the expansion of the folded pouch into the contraction of the belt. Modular design of the actuation unit enhances the adaptivity to multiple application scenarios. Based on the principle of virtual work, a force-contraction model considering energy loss was established and verified experimentally. Test results indicate that the actuation unit can attain a contraction force of 35.3 N and contraction ratio of 49.27 %. Moreover, self-contained displacement and force sensing were designed, calibrated, and validated for this actuator. The good displacement tracking and force monitoring performance in the primary feedback control test proved the effectiveness of the self-sensing methods. Finally, surface electromyography (sEMG) of biceps brachii with actuator revealed that muscle activation could be reduced by 30.72 % and 41.27 % without and with 3 kg load, respectively.

Computed torque control and force analysis for mechanical leg with variable rotation axis powered by servo pneumatic muscle

It is difficult for a humanoid leg driven by two groups of antagonistic pneumatic muscles (PMs) to achieve a flexible humanoid gait, and its inherent strong coupling nonlinear characteristics make it hard to achieve good tracking performance in a large range of motion. Therefore, a four-bar linkage bionic knee joint structure with a variable axis and a double closed-loop servo position control strategy based on computed torque control are designed to improve anthropomorphic characteristics and the dynamic performance of the bionic mechanical leg powered by servo pneumatic muscle (SPM). Firstly, the relationship between the joint torque, the initial jump angle and the bounce height of the mechanical leg is established, and then we design a double-joint PM bionic mechanical leg containing a four-bar linkage mechanism of the knee joint. Secondly, a cascade position control strategy is developed, which consists of the outer position loop and the inner contraction force loop, and the mapping relationship is designed between joint torque and antagonistic PM contraction force. Finally, we further project bounce action timing of mechanical leg to realize the periodic jumping movement of the mechanical leg, and simulation and physical experiments of the real-style machine platform have been provided to demonstrate the effectiveness of the designed SPM controller.

Eight-Day Typhoon Quantitative Precipitation Forecasts in Taiwan by the 2.5 km CReSS Model, Part II: Reduced Control of Track Errors on Rainfall Prediction Quality for Typhoons Associated with Southwesterly Flow

Due to the enhancement by its steep mesoscale topography, the overall rainfall amount and distribution in Taiwan from typhoons, to a first degree, are determined by the storm track relative to the island. Therefore, the quality of typhoon quantitative precipitation forecasts (QPFs) from numerical models is often controlled by track errors, with better quality from those with smaller track errors. However, the present work demonstrates that in daily QPFs over Taiwan made by a cloud-resolving model during five seasons of 2012–2016, targeted for 84 days during 27 typhoons and at ranges of day one (0–24 h) to day eight (168–192 h), the control of track errors on QPF quality is reduced for typhoons associated with southwesterly flow, compared to those without, and decent QPFs could still be obtained with large track errors in some cases. Subsequently, the circumstances and reasons for good (or bad) QPFs in selected examples are further investigated to deepen our understanding of typhoon QPFs in Taiwan. Some common ingredients are found in three cases where good QPFs were produced at a longer range (day 7 or 8) without a good track: these typhoons passed near northern Taiwan and the southwesterly flow prevailed over much of the island during the accumulation period. Responsible for much of the rainfall in Taiwan, the southwesterly flow was reasonably captured, resulting in good QPFs. In another example where the typhoon moved across southern Taiwan, on the contrary, the rainfall was produced by the storm’s circulation, and the QPF was degraded without a good enough track prediction.

Trajectory Tracking Coordinated Control of 4WID-4WIS Electric Vehicle Considering Energy Consumption Economy Based on Pose Sensors

In order to improve the stability and economy of 4WID-4WIS (four-wheel independent drive—four-wheel independent steering) electric vehicles in trajectory tracking, this paper proposes a trajectory tracking coordinated control strategy considering energy consumption economy. First, a hierarchical chassis coordinated control architecture is designed, which includes target planning layer, and coordinated control layer. Then, the trajectory tracking control is decoupled based on the decentralized control structure. Expert PID and Model Predictive Control (MPC) are employed to realize longitudinal velocity tracking and lateral path tracking, respectively, which calculate generalized forces and moments. In addition, with the objective of optimal overall efficiency, the optimal torque distribution for each wheel is achieved using the Mutant Particle Swarm Optimization (MPSO) algorithm. Additionally, the modified Ackermann theory is used to distribute wheel angles. Finally, the control strategy is simulated and verified using Simulink. Comparing the control results of the average distribution strategy and the wheel load distribution strategy, it can be concluded that the proposed coordinated control not only provides good trajectory tracking but also greatly improves the overall efficiency of the motor operating points, which enhances the energy economy and realizes the multi-objective coordinated control of the chassis.

Control-oriented meta-learning

Real-time adaptation is imperative to the control of robots operating in complex, dynamic environments. Adaptive control laws can endow even nonlinear systems with good trajectory tracking performance, provided that any uncertain dynamics terms are linearly parameterizable with known nonlinear features. However, it is often difficult to specify such features a priori, such as for aerodynamic disturbances on rotorcraft or interaction forces between a manipulator arm and various objects. In this paper, we turn to data-driven modeling with neural networks to learn, offline from past data, an adaptive controller with an internal parametric model of these nonlinear features. Our key insight is that we can better prepare the controller for deployment with control-oriented meta-learning of features in closed-loop simulation, rather than regression-oriented meta-learning of features to fit input-output data. Specifically, we meta-learn the adaptive controller with closed-loop tracking simulation as the base-learner and the average tracking error as the meta-objective. With both fully actuated and underactuated nonlinear planar rotorcraft subject to wind, we demonstrate that our adaptive controller outperforms other controllers trained with regression-oriented meta-learning when deployed in closed-loop for trajectory tracking control.

Research on control of electro-hydraulic servo system based on third-order linear ADRC

PurposeThe purpose of this paper is to design a third-order linear active disturbance rejection controller (LADRC) to improve the response characteristics and robustness of the electrohydraulic servo system.Design/methodology/approachThe LADRC was designed by replacing the nonlinear functions in each part of ADRC with linear functions or linear combinations, and the parameters of each part of the LADRC were connected with their bandwidth through the pole configuration method to reduce the required tuning parameters, and used an improved grey wolf optimizer to tune the LADRC parameters.FindingsThe anti-interference control simulation and experiment on the LADRC, ADRC and proportion integration differentiation (PID) were carried out to test the robustness, anti-interference ability and superiority of the designed LADRC. The simulation and experiment results showed that the LADRC control and anti-interference control had excellent performance, and because of its simple structure and fewer parameters, LADRC was easier to implement and had a better control effect and anti-interference.Originality/valueFor the problems of parameter perturbation, unknown interference and inaccurate model in the electrohydraulic position servo system, the designed third-order LADRC has good tracking accuracy and anti-interference, has few parameters and is conducive to promotion.

Model Predictive Current Control of Permanent Magnet Synchronous Motor Based on Sliding Mode Observer With Enhanced Current and Speed Tracking Ability Under Disturbance

For model predictive current control (MPCC) strategy, its poor dynamic response caused by speed loop PI control and poor parameter robustness are the main factors restricting its development. So this paper proposes a model predictive current control based on sliding mode observer with better tracking ability of current and speed under disturbance. First, this paper analyzes the effect of motor parameter mismatches on current predicted values from multiple perspectives. And a sliding mode disturbance observer is designed to compensate lumped disturbances including parameter mismatches and unmodeled dynamic disturbances into prediction model. Then, the speed loop PI controller in model predictive current control is replaced by a deadbeat predictive controller to improve response speed of the speed loop. Meanwhile, the sliding mode load torque observer is constructed to observe the load torque and sends it into the deadbeat predictive controller to improve the anti-load disturbance performance of the control system. Furthermore, a sliding mode reaching law based on sinusoidal saturation function is proposed to suppress chattering of sliding mode observer. The experiments demonstrate that the proposed control strategy not only has faster dynamic response but also has good speed and current tracking performance under lumped disturbance and load disturbance.

Iterative Learning Control for Compliant Underactuated Arms

Operations involving safe interactions in unstructured environments require robots with adapting behaviors. Compliant manipulators are a promising technology to achieve this goal. Despite that, some classical control problems such as following a trajectory are still open. A typical solution is to compensate the system dynamics with feedback loops. However, this solution increases the effective robot stiffness and jeopardizes the safety property provided by the compliant design. On the other hand, purely feedforward approaches can achieve good tracking performance while preserving the robot intrinsic compliance. However, a feedforward control framework for robots with passive elastic joints is still missing. This article presents an iterative learning control algorithm for purely feedforward trajectory tracking for compliant underactuated arms. Each arm is composed of active elastic joints and a generic number of passive ones connected through rigid links. We prove the convergence of the iterative method, also in the presence of uncertainties and bounded disturbances. Different output functions are analyzed providing conditions, based on the system inertial properties that ensure the algorithm applicability. Additionally, an automatic selection of the learning gain is proposed. Finally, we extensively validate the theoretical results with simulations and experiments.

Nonlinear model predictive control of vanadium redox flow battery

In this paper, nonlinear model predictive controller (NMPC) is designed for stack voltage control in vanadium redox flow battery (VRFB) and the closed-loop performance is evaluated under different conditions. The controller is designed by using a simplified model of the VRFB system. The flow rate of electrolytes is the manipulated variable and the charging or discharging current is a disturbance. The unmeasured state variables (vanadium ions concentrations) and state of charge of the VRFB are estimated by measuring open-circuit voltage. Numerical simulations show the effectiveness of the NMPC under different operating conditions: when VRFB is balanced and its simplified model is perfectly known, when VRFB is imbalanced and some of its model parameters are not accurately known, and in presence of measurement noise. In all these cases the predictive controller ensures very good tracking performance and rejection of disturbances.

Live Inventory Tracking System Using IOT

Abstract: Inventory Management System software is important for organisations that operate hardware stores, where the store owner keeps track of sales and purchases. Poor inventory management leads to disgruntled customers, excess money in warehouses, and slower sales. With this initiative, paper work, human mistake, manual delay, and process acceleration are all avoided. An inventory management system can help a store owner keep track of sales and available goods, as well as calculate when and how much to repurchase. Inventory Management System is a Windows application for Windows operating systems that focuses on inventory control and the ability to generate all essential reports.

A review of research on directional off-road path planning algorithms

Military orienteering can cultivate the skills of map reading, map use and military charting, and has gradually become a regular military training subject. A good track design plan is the basis for the smooth running of the competition. The current research on cross-country courses is mainly focused on single-track optimization, and there is very little research on multi-track planning, and design. However, in specific events, there are often multiple tracks running at the same time, and there are many restrictions, single track optimization design solutions are often not applicable. In this paper, we review multiple path planning algorithms and summarize the problems of these algorithms. On this basis, the advantages and disadvantages of static and dynamic path planning algorithms are analyzed, and future research trends and methods of path planning algorithms in orienteering are proposed.

Immunity of shunt active power filter based on improved LADRC

To improve the dynamic response speed and performance of active power filter controller, and improve the robustness, tracking ability, and disturbance rejection ability of linear active disturbance rejection controller (ADRC), an improved linear ADRC (LADRC) is proposed. First, a new error adjustment total disturbance is introduced into the linear expansion state observer to adjust the total disturbance of the linear expansion state observer. Then, on the premise of system stability, the controller is applied to the voltage outer loop of shunt active power filter (SAPF), and the mathematical model of active power filter is deduced, and it is controlled according to the first-order linear active disturbance rejection controller. Finally, through frequency domain analysis, the stability, noise immunity, and tracking performance of the improved LADRC are analyzed. Using MATLAB and Simulink simulation platform, the current stability and tracking performance of SAPF under improved LADRC control are simulated and verified, and compared with traditional methods. The simulation results show that the waveform fluctuation of A-phase voltage on the grid side of this method is relatively smooth, and the voltage on the grid side is always kept at ±50 V; the voltage curve of SAPF DC side is kept between the actual set values, about 820 V; the waveform fluctuation of A-phase current on the load side is small, which is ±220 A; the harmonic distortion rate is 4%. It is verified that this method has good tracking effect and anti-interference characteristics, which is of great significance to improve the control performance of active power filter.

Sustainable Financing Strategies for the SMEs: Two Alternative Models

A sustainable financing strategy for SMEs should aim to enhance a low-cost collateral-free supply of loans to SMEs with good track records of repayments to banks. In this paper, we suggest two alternative financing models for SMEs that address certain borrowing constraints of SMEs. First, the model incorporates institutional mechanisms involving the government, banks, and SMEs. The strategy employs a two-pronged approach: (i) the government enhances the supply of loanable funds to banks, and (ii) identifies good SME borrowers through skills development programs and introduces them to banks. This model will reduce default risk and allow banks to offer lower-interest and collateral-free credit to SMEs, thereby improving their access to finance and performance. Second, the model could be extended to accommodate digital finance using a data-driven credit risk score of the borrowers to reduce banks’ default risks and transaction costs with or without government funds. The proposed model could resolve the moral hazard and selection bias problems. Our proposed models are based on a public-private partnership approach and therefore could solve certain borrowing constraints of SMEs. Our empirical results support the model outcomes and therefore are consistent with the predictions of our theoretical models.

Iterative neural network adaptive robust control of a maglev planar motor with uncertainty compensation ability

In this paper, an iterative neural network adaptive robust control (INNARC) strategy is proposed for the maglev planar motor (MLPM) to achieve good tracking performance and uncertainty compensation. The INNARC scheme consists of adaptive robust control (ARC) term and iterative neural network (INN) compensator in a parallel structure. The ARC term founded on the system model realizes the parametric adaptation and promises the closed-loop stability. The INN compensator based on the radial basis function (RBF) neural network is employed to handle the uncertainties resulted from the unmodeled non-linear dynamics in the MLPM. Additionally, the iterative learning update laws are introduced to tune the network parameters and weights of the INN compensator simultaneously, so the approximation accuracy is improved along the system repetition. The stability of the INNARC method is proved via the Lyapunov theory, and the experiments are conducted on an home-made MLPM. The results consistently demonstrate that the INNARC strategy possesses the satisfactory tracking performance and uncertainty compensation, and the proposed INNARC is an effective and systematic intelligent control method for MLPM.

A novel asymmetric barrier Lyapunov function-based fixed-time ship berthing control under multiple state constraints

The construction of an asymmetric barrier function with internal security set, while ensuring its invariance in the presence of interference, is a challenging task to achieve stability and security. In this paper, a translation transformation is used to construct a novel asymmetric Barrier Lyapunov Function. To the best of our knowledge, this is the first designed continuously differentiable barrier Lyapunov function (BLF) of this type. A new adaptive sliding mode control (SMC) method based on asymmetrical BLF is proposed for uncertain external disturbances. The robust control method can ensure the safety performance in the presence of model uncertainty with bounded tracking error of the ship trajectory and velocity. Its fixed-time convergence is then proved by the Lyapunov theory. The results show that it has stronger anti-interference ability in bad sea conditions, better robustness, and concise form. In addition, the system can deal with unknown disturbances, ensure the safe navigation, and keep the good tracking of the route under wind and current disturbance due to random sea conditions.

Improving the Out-of-Domain Matching Reliability and Positioning Accuracy of Underwater Gravity Matching Navigation Based on a Novel Cyclic Boundary Semisquare-Domain Researching Method

A gravity matching algorithm is a crucial component of a vehicle’s underwater navigation system. This article studies the improvement in the out-of-domain matching reliability and positioning accuracy of underwater navigation. To achieve these goals, a novel cyclic boundary semisquare-domain researching (CBSR) method is proposed. Two times the inertial error is first employed to span an initial small-square domain and find an initial optimal matching position, which results in controlling the matching efficiency. If this optimal position is located on the domain boundary, the cyclic boundary semisquare domain rematching mechanism is triggered. The cyclic boundary semisquare domain is iteratively generated to perform repositioning and obtain a better matching position until it falls into the interior of the matching domain. The final optimal matching position is used to calibrate the sensor’s parameters and aid the navigation of the underwater vehicle. Experimental results prove that the proposed CBSR method has outstanding positioning capacity for underwater gravity matching navigation, and higher matching reliability and lower mismatching probability of the vehicle’s out-of-domain positioning. For excellent and good suitable tracks, the number of out-of-domain mismatches of the CBSR method, compared with TERCOM, are reduced by 97.73% and 86.05%, respectively, while the out-of-domain average matching accuracies are all less than one grid resolution and are improved by 85.23% and 81.07%, indicating the effectiveness and feasibility of the proposed CBSR model for improving the matching reliability and positioning accuracy of out-of-domain underwater navigation.

Improved indirect adaptive line-of-sight guidance law for path following of under-actuated AUV subject to big ocean currents

This paper presents a novel indirect adaptive disturbance observer (IADO)-based line-of-sight (LOS) guidance law for path following of under-actuated AUV in the presence of time-varying big ocean currents. The influences of ocean currents on AUV are expressed in the kinematic model. An indirect adaptive disturbance observer is designed and utilized to estimate and compensate for the ocean currents-induced uncertainties when following a curved path. In addition, an adaptive look-ahead distance is proposed for better following the curved path with sharp turnings. Then, an improved LOS guidance law is designed to follow the desired path accurately by introducing an auxiliary variable to the conventional LOS and utilizing the indirect adaptive disturbance observer to estimate the ocean current-induced disturbances in the kinematic model. Furthermore, the closed-loop cascade IADO-LOS guidance system, composed of a tracking error subsystem and estimation error system, is proven to be globally uniformly asymptotically stable according to the Lyapunov stability theory. The proposed guidance law is intended for maneuvering AUVs in the horizontal plane to follow a predefined curved path with sharp turning under time-varying big ocean currents. Extensive simulation studies demonstrate that the proposed approach has good tracking accuracy, low chattering, and anti-jamming ability.

A four-line active shunt filter to enhance the power quality in a microgrid

In recent years, power quality has become a major concern for electric network managers. Active filtering control schemes ensure improved power quality of the electric network and are able to maintain a desired voltage level at the point of connection, regardless of the current absorbed by nonlinear loads. Harmonics can cause vibrations, equipment distortion, losses and sweatiness in transformers. The main objective of this work is to enhance the quality of energy in a microgrid consisting of 100 kW photovoltaic (PV) system and a 50 kW battery storage connected to nonlinear and unbalanced loads. This paper proposes a the four-arm parallel active filter with a on Proportional-Integral (PI) controller to mitigate the harmonic problems in a microgrid. In addition, an algorithm has been designed to eliminate the neutral current. The identification function is one of the most particular approach for extracting harmonics, it involves providing a current reference imposed by the active filter in order to carry out the filtering operation. Both the performance and the quality of the current harmonic compensation's depend strongly on the strategy adopted for the generating the current reference. In this work, the instantaneous power strategy p-q is chosen outstanding the simplicity and effectiveness in implementation. The proposed control strategy has been tested under simulations and the results have shown good tracking of the references and a significant reduction in the Total Harmonic Distorsion (THD) level under highly unbalanced conditions of the nonlinear loads. The current THD is reduced from 43.64 before filtering to 3.74% after the application of the four-arm filter, following the recommendations of IEEE-519 standard (THD less than 5%).

A composite adaptive robust control for pneumatic servo systems with time-varying inertia

This article proposes a composite adaptive robust control for a pneumatic servo system with time-varying inertia to achieve a good tracking performance. The proposed design not only preserves the merits of both direct adaptive robust control and indirect adaptive robust control but also obtains a better performance of the working condition with time-varying inertias. Firstly, prescribed tracking performance is guaranteed without knowing the bounds of parametric and non-parametric uncertainties by leveraging a discontinuous projection and deterministic robust control technique. And a nonlinear tracking differentiator is used to obviate differential signal in backstepping. Secondly, the online parametric estimation algorithm is developed by intelligently integrating tracking error dynamics and physical plant dynamics. By effectively using the information of both tracking error and prediction error, a high adaptation gain can be used for achieving a faster parameter convergence and smaller tracking error. Moreover, exponential convergence of both tracking error and prediction error can be obtained under certain conditions. Lastly, comparative experiments on different conditions are carried out for a single-rod pneumatic cylinder driven by a proportional directional valve to demonstrate the superiority of the proposed method.