Tracking Control Algorithm Research Articles

Photovoltaic Maximum Power Point Tracking Technology Based on Power Prediction Algorithm Combined with Variable Step Length Disturbance Observation Method

Under partial shading conditions, the system operating sequence of photovoltaic cells does not fall on a single characteristic curve, and the traditional maximum power point tracking (MPPT) control algorithm is prone to causing the system output power to oscillate around the maximum power point. In the case of multiple peaks, the traditional MPPT algorithm is prone to being trapped in a local optimum solution. This paper proposes an MPPT control algorithm based on the WOA-RF prediction algorithm combined with a variable step disturbance observation method. By establishing a photovoltaic system simulation model, the improved algorithm is verified through simulation. The improved MPPT control algorithm can adaptively adjust the step size under different conditions to ensure that the system can quickly and accurately track the maximum power point. At the same time, by optimizing the algorithm logic and parameter settings, it can effectively avoid problems such as local optimum solutions and instability in the system, and improve the system convergence speed and tracking accuracy.

PID Controller Based on Improved DDPG for Trajectory Tracking Control of USV

When navigating dynamic ocean environments characterized by significant wave and wind disturbances, USVs encounter time-varying external interferences and underactuated limitations. This results in reduced navigational stability and increased difficulty in trajectory tracking. Controllers based on deterministic models or non-adaptive control parameters often fail to achieve the desired performance. To enhance the adaptability of USV motion controllers, this paper proposes a trajectory tracking control algorithm that calculates PID control parameters using an improved Deep Deterministic Policy Gradient (DDPG) algorithm. Firstly, the maneuvering motion model and parameters for USVs are introduced, along with the guidance law for path tracking and the PID control algorithm. Secondly, a detailed explanation of the proposed method is provided, including the state, action, and reward settings for training the Reinforcement Learning (RL) model. Thirdly, the simulations of various algorithms, including the proposed controller, are presented and analyzed for comparison, demonstrating the superiority of the proposed algorithm. Finally, a maneuvering experiment under wave conditions was conducted in a marine tank using the proposed algorithm, proving its feasibility and effectiveness. This research contributes to the intelligent navigation of USVs in real ocean environments and facilitates the execution of subsequent specific tasks.

A Path Planning and Tracking Control Algorithm for Multi-Autonomous Mobile Robot Systems Based on Spatiotemporal Conflicts and Nonholonomic Constraints

A Path Planning and Tracking Control Algorithm for Multi-Autonomous Mobile Robot Systems Based on Spatiotemporal Conflicts and Nonholonomic Constraints

Fuzzy Adaptive Whale Optimization Control Algorithm for Trajectory Tracking of a Cable-Driven Parallel Robot

Fuzzy Adaptive Whale Optimization Control Algorithm for Trajectory Tracking of a Cable-Driven Parallel Robot

Nonlinear terminal sliding mode trajectory tracking control algorithm for industrial robots based on dung beetle optimizer

Abstract To enhance the precision of trajectory tracking control in industrial robots, a nonlinear end-to-end sliding mode trajectory tracking control algorithm for industrial robots based on the dung beetle algorithm is proposed. By employing nonlinear terminal sliding mode control to resolve the issue of inadequate trajectory tracking control performance in industrial robots, and integrating the dung beetle optimizer (DBO) algorithm for control parameter adjustment, significant enhancements are achieved in trajectory tracking accuracy.

Development of a digital twin system for snake endoscope manipulator in fusion reactors

Tokamak fusion is one of the solutions to the energy crisis. The environment of the vacuum chamber as a reaction site is extreme, so it needs to be observed during the operation period. This paper has developed a digital twin system for the snake endoscope manipulator (SEM) system, designed to observe of the fusion reactor vacuum chamber. The system includes a five-dimensional model of the SEM digital twin and a three-category architecture encompassing perception, data, and interaction. Additionally, a visualization and interaction system based on ROS-Unity has been developed to depict the working conditions of the SEM accurately. A terminal traction-based trajectory tracking control algorithm has been designed, enabling the SEM to maneuver flexibly within the confined space of the vacuum chamber to avoid collisions and prevent additional damage to the vacuum chamber. The system has integrated a Python-ROS-Unity-based algorithm for joint simulation channel development, which has been used for the virtual simulation experiments of SEM to validate the algorithms and collect data. Furthermore, a prototype has been constructed for virtual-to-real mapping experiments. Experiments show the digital twin system is feasible, with low latency, high sync, and stable operation, which verifies the feasibility of the system.

Backstepping-Based Quasi-Sliding Mode Control and Observation for Electric Vehicle Systems: A Solution to Unmatched Load and Road Perturbations

The direct current (DC) motor is the core part of an electrical vehicle (EV). The unmatched perturbation of load torque is a challenging problem in the control of an EV system driven by a DC motor and hence a deep control concern is required. In this study, the proposed solution is to present two control approaches based on a backstepping control algorithm for speed trajectory tracking of EVs. The first control design is to develop the backstepping controller based on a quasi-sliding mode disturbance observer (BS-QSMDO), and the other controller is to combine the backstepping control with quasi-integral sliding mode control (BS-QISMC). In the sense of Lyapunov-based stability analysis, the ultimate boundedness of the proposed controllers has been detailedly analyzed, assessed, and evaluated in the presence of unmatched perturbation. A modified stability analysis has been presented to determine the ultimate bounds of disturbance estimation error for both controllers. The determination of ultimate bound and region-of-attraction for tracking and estimation errors is the contribution achieved by the proposed control design. The performances of the proposed controllers have been verified via computer simulations and the level of ultimate bounds for the estimation and tracking errors are the key measures for their evaluation. Compared to BS-QISMC, the results showed that a lower level of ultimate boundedness with a higher convergent rate can be reached based on BS-QSMO. However, a higher control effort can be exerted by the BS-QSMO controller as compared to BS-QISMC; and this is the price to be paid by the BS-QSMO controller to achieve lower ultimate boundedness with a faster convergence rate.

Hybrid feedback control of PMSG-based WECS with multilevel flying-capacitor inverter enhanced by fractional order extremum seeking

This paper proposes an original hybrid control strategy for a three-phase multilevel flying capacitor inverter (FCI) used in wind energy conversion system (WECS). A state feedback law, computed using a Lyapunov function and an invariance principle, is designed to manage grid current control and capacitor voltage balancing, guaranteeing the asymptotic stability of the closed-loop system. Additionally, a fractional-order extremum seeking control (FOESC) algorithm for Maximum Power Point Tracking (MPPT) is introduced, which optimizes power capture by adjusting turbine speed from the DC side without requiring a detailed system model (model-free approach). The use of fractional-order calculus leads to improved convergence and more seamless performance while preserving the robust characteristics of the system. The proposed control strategies are validated through simulation, demonstrating improved transient response and stability under varying wind conditions and parameter uncertainties. These results highlight the potential of advanced control algorithms to enhance the efficiency and reliability of wind energy systems, contributing to global efforts to achieve sustainable energy goals.

Model predictive control based integrated fault tolerant controller for autonomous vehicles with four-wheel independent steering, driving and braking systems

This paper presents a fault-tolerant control algorithm for path and velocity tracking of an autonomous vehicle based on four-wheel independent steering, driving, and braking systems. The proposed algorithm is designed to distribute the control effort to normal actuators in the occurrence of faults. The proposed algorithm consists of three modules. The reference velocity and yaw rate to track the reference path are determined by the reference state decision module. The model predictive control (MPC)-based fault-tolerant controller determines the optimal inputs to compensate for the effect of the faulty actuator and maintain the tracking performance. A faulty-factor estimator is proposed based on a recursive covariance estimator to evaluate the performance degradation due to the failure of each actuator. The estimated faulty factors are applied to the objective function of the MPC such that the control input is distributed based on the level of the fault. The performance of the proposed algorithm was verified via performing simulations using MATLAB/Simulink and CarSim. The simulations proved that the proposed controller maintained the control performance of the normal case even in multiple actuator failures.

Towards maximum efficiency of an open-cathode PEM fuel cell system: A comparative experimental demonstration

Despite its high energy density and zero-carbon emissions, the path to large commercialization for Proton Exchange Membrane Fuel Cell (PEMFC) systems faces challenges related to cost reduction, efficiency enhancement, and durability improvement. One key strategy to address these hurdles is the development of reliable control algorithms that ensure operation at the Maximum Efficiency Point (MEP) of PEMFC systems. This optimization is essential for achieving high efficiency, a crucial factor for PEMFC technology commercial viability. This work explores the experimental design, implementation, and evaluation of Maximum Efficiency Point Tracking (MEPT) control algorithms for a PEMFC system. The investigated methods span across five distinct categories: conventional, data-driven, metaheuristics, online model identification, and filter-based approaches. To assess these methods, a real-time small-scale PEMFC system test bench was built. The setup consists of 200 W open-cathode PEMFC, industrial step-up DC–DC converter, programmable load and a cost-effective measurement and monitoring unit. Merits and demerits of each method are then discussed based on different performance metrics such as tracking speed, hydrogen consumption and power stress. Comparative analysis of the results reveals that, while all the algorithms achieved a maximum electrical efficiency of about 48%, data-driven methods exhibited a superior trade-off between performance metrics. This ultimately leads to improved PEMFC durability.

CPS-based power tracking control for distributed energy storage aggregator in demand-side management

The deployment of distributed energy storage on the demand side has significantly enhanced the flexibility of power systems. However, effectively controlling these large-scale and geographically dispersed energy storage devices remains a major challenge in demand-side management. In this paper, we propose a CPS-based framework for controlling a distributed energy storage aggregator (DESA) in demand-side management. Within this framework, a distributed power tracking control algorithm is designed to ensure both power tracking and state-of-charge (SoC) balancing among the energy storage units (ESUs) within the DESA. The proposed algorithm utilizes a distributed observation-based approach that relies solely on local communication. It is demonstrated that the algorithm achieves power tracking convergence within a fixed time, while asymptotically achieving SoC balancing when assuming a connected communication network among the storage units. To validate the theoretical analysis and demonstrate the effectiveness of the proposed control strategy, an example scenario comprising six ESUs is presented.

Target Tracking and Circumnavigation Control for Multi-Unmanned Aerial Vehicle Systems Using Bearing Measurements

This paper addresses the problem of target tracking and circumnavigation control for a bearing-only multi-Unmanned Aerial Vehicle (UAV) system. First, using the bearing measurements, an adaptive algorithm in the form of a Proportional Integral (PI) controller is developed to estimate the target state. Subsequently, a distributed circumnavigation control protocol is established to evenly encircle the target. Then, we use the local information from each UAV in the network to calculate the relative position of the target, and further enhance the accuracy of estimation and circumnavigation algorithms by employing a Kalman filter. Finally, numerical simulation experiments are conducted to validate the effectiveness of the proposed tracking control algorithm.

Self-triggered fuzzy trajectory tracking control for the stratospheric airship

A self-triggered trajectory tracking control algorithm, which considers unknown disturbance and actuator saturation, has been studied to address the energy and onboard equipment life limitation problems of stratospheric airships. The controller is based on a backstepping-sliding mode method, utilizing the fuzzy logic systems and auxiliary systems to handle unknown disturbance and actuator saturation, respectively. Based on the continuity of the control law, the self-triggered condition is designed whose paradigm is used to build the fuzzy logic systems. Compared with the existing stratospheric airships event-triggered controller, continuous state detection and calculation are avoided. It is proved that the tracking errors and the weight matrix errors of the fuzzy logic system are uniformly ultimately bounded both at the trigger moment and within the trigger interval. Additionally, Zeno behavior is avoided without imposing additional parameter restrictions. Simulation results demonstrate the effectiveness of the proposed algorithm. Based on execution frequency, the proposed algorithm can conserve 98.3% of resources compared to the traditional algorithm and 97.5% of resources compared to the event-triggered algorithm.

Series partial power converter with half bridge LLC series resonant converter for PV application

Abstract This study proposes a series partial power converter with a half-bridge LLC series resonant converter for photovoltaic (PV) applications. The main advantage of the implementation of a partial power converter with LLC series resonant converter is zero voltage switching (ZVS) operation of primary side enables the converter to work at a higher switching frequency. The partial power topology used in the proposed converter enables low power rating switches in the primary, which in other terms reduces the primary current. The proposed converter achieves excellent efficiency due to its ZVS and partial power operating capabilities. The valuation of the proposed converter is done in MATLAB Simulink with a 300 W PV module, which is made to operate in varying irradiance and module temperature of 25 °C. The operational waveforms and the power outputs obtained when the proposed converter is implemented by using perturbation and observation (P&O) maximum power point tracking (MPPT) control algorithm are discussed in this paper.

Safety-Based Speed Control of a Wheelchair Using Robust Adaptive Model Predictive Control.

Electric-powered wheelchairs play a vital role in ensuring accessibility for individuals with mobility impairments. The design of controllers for tracking tasks must prioritize the safety of wheelchair operation across various scenarios and for a diverse range of users. In this study, we propose a safety-oriented speed tracking control algorithm for wheelchair systems that accounts for external disturbances and uncertain parameters at the dynamic level. We employ a set-membership approach to estimate uncertain parameters online in deterministic sets. Additionally, we present a model predictive control scheme with real-time adaptation of the system model and controller parameters to ensure safety-related constraint satisfaction during the tracking process. This proposed controller effectively guides the wheelchair speed toward the desired reference while maintaining safety constraints. In cases where the reference is inadmissible and violates constraints, the controller can navigate the system to the vicinity of the nearest admissible reference. The efficiency of the proposed control scheme is demonstrated through high-fidelity speed tracking results from two tasks involving both admissible and inadmissible references.

A modified handling stability control strategy for steer-by-wire vehicles based on variable steering ratio and active front steering control

Steer-by-wire (SBW) systems with steering actuator motors rotating the front wheels allow more flexibility for handling stability control; however, determining the desired steering angle and accurately tracking the desired steering angle are still key challenges. Therefore, a modified handling stability control strategy for handling stability is proposed in this paper. Firstly, based on the normal function, a unified variable steering ratio (UniVSR) model that varies with vehicle speed and steering wheel angle is developed. Then, the UniVSR model parameters are optimized by particle swarm optimization (PSO) algorithm in speed segments to improve the steering sensitivity at low speeds and the stability at medium and high speeds. Next, the active front steering (AFS) controller that considers the intervention timing and intervention threshold is proposed to improve the stability of the SBW vehicle under the driving conditions of low-adhesion road, split-μ road, and lateral wind interference. Lastly, the linear active disturbance rejection control (LADRC) based angle tracking control algorithm is presented to guarantee the desired angle tracking accuracy of the steering actuator motor under both internal parameter and external torque perturbations. The simulations validate the effectiveness of the proposed UniVSR model and AFS controller in improving handling stability.

Event-Based Nonsingular Fixed-Time Tracking Control of an Uncertain Manipulator System Subject to Full-State Static Constraints.

This article centers around investigating the event-triggered nonsingular fixed-time tracking issue for an n -link rigid robot manipulator with full-state constraints, external disturbances, and model uncertainties. We propose the definition of the constrainedly practically fixed-time stability (CPFTS) and provide a sufficient condition for CPFTS. A novel auxiliary function is developed to address the singularity issue caused by repeated differentiation in achieving the fixed-time tracking control. The uncertain parameters are approximated using the radial basis function neural network (RBFNN). This study proposes the model-based and the neutral network-based tracking control approaches, designed using the scaling function technique and the barrier Lyapunov function, respectively, to ensure that the tracking error systems are CPFTS and the full-state constraints comply. Moreover, the communication transmission load is reduced using the relative threshold event-triggered control strategy. Simulation results demonstrate the effectiveness of the proposed tracking control algorithms.

Research on Photovoltaic Maximum Power Point Tracking Control Based on Improved Tuna Swarm Algorithm and Adaptive Perturbation Observation Method

In situations where photovoltaic (PV) systems are exposed to varying light intensities, the conventional maximum power point tracking (MPPT) control algorithm may become trapped in a local optimal state. In order to address this issue, a two-step MPPT control strategy is suggested utilizing an improved tuna swarm optimization (ITSO) algorithm along with an adaptive perturbation and observation (AP&O) technique. For the sake of enhancing population diversity, the ITSO algorithm is initialized by the SPM chaos mapping population. In addition, it also uses the parameters of the spiral feeding strategy of nonlinear processing and the Levy flight strategy adjustment of the weight coefficient to enhance global search ability. In the two-stage MPPT algorithm, the ITSO is applied first to track the vicinity of the global maximum power point (MPP), and then it switches to the AP&O method. The AP&O method’s exceptional local search capability enables the global MPP to be tracked with remarkable speed and precision. To confirm the effectiveness of the suggested algorithm, it is evaluated against fuzzy logic control (FLC), standard tuna swarm optimization (TSO), grey wolf optimization (GWO), particle swarm optimization (PSO), and AP&O. Finally, the proposed MPPT strategy is verified by the MATLAB R2022b and RT-LAB experimental platform. The findings indicate that the suggested method exhibits improved precision and velocity in tracking, efficiently following the global MPP under different shading conditions.

Adaptive Optimal Tracking Control of an Underactuated Surface Vessel Using Actor-Critic Reinforcement Learning.

In this article, we present an adaptive reinforcement learning optimal tracking control (RLOTC) algorithm for an underactuated surface vessel subject to modeling uncertainties and time-varying external disturbances. By integrating backstepping technique with the optimized control design, we show that the desired optimal tracking performance of vessel control is guaranteed due to the fact that the virtual and actual control inputs are designed as optimized solutions of every subsystem. To enhance the robustness of vessel control systems, we employ neural network (NN) approximators to approximate uncertain vessel dynamics and present adaptive control technique to estimate the upper boundedness of external disturbances. Under the reinforcement learning framework, we construct actor-critic networks to solve the Hamilton-Jacobi-Bellman equations corresponding to subsystems of surface vessel to achieve the optimized control. The optimized control algorithm can synchronously train the adaptive parameters not only for actor-critic networks but also for NN approximators and adaptive control. By Lyapunov stability theorem, we show that the RLOTC algorithm can ensure the semiglobal uniform ultimate boundedness of the closed-loop systems. Compared with the existing reinforcement learning control results, the presented RLOTC algorithm can compensate for uncertain vessel dynamics and unknown disturbances, and obtain the optimized control performance by considering optimization in every backstepping design. Simulation studies on an underactuated surface vessel are given to illustrate the effectiveness of the RLOTC algorithm.

Adaptive predefined-time tracking control of uncertain permanent-magnet synchronous motor drive chaotic system

A predefined-time tracking control algorithm is formulated for the Permanent Magnet Synchronous Motor(PMSM) driver system subjected to chaotic oscillations and undeterministic parameters, which can determine the upper bound of settling-time by controller gains. Radial Basis Function Neural Networks(RBFNNs) are employed to approximate the unknown nonlinear functions as universal approximators. The innovative predefined-time tracking differentiators are constructed to estimate the derivatives of the virtual inputs, resolving “the explosion of complexity” in traditional backstepping method. Then, an adaptive predefined-time controller is proposed in the backstepping framework. Lyapunov’s theoretical analysis proves that all signals in the closed-loop systems are uniformly ultimately bounded and the output tracking errors can converge to a small neighborhood of the origin within the predefined-time. Finally, simulation studies are performed to demonstrate that the proposed control scheme can quash the chaotic behaviors of the PMSM drive system and ensure fast-tracking performance even under unknown parameters.