Tracking Control Scheme Research Articles

Sampled-data self-learning observer based attitude tracking control against sensor-actuator faults

Abstract This paper proposes an intermittent measurement-based attitude tracking control strategy for spacecraft operating in the presence of sensor-actuator faults. A sampled-data (self-)learning observer is developed to estimate both the spacecraft’s states and lumped disturbances, effectively mitigating the impact of faults. This observer acts as a virtual predictor, reconstructing states and actuator fault deviations using only intermittent measurement data, addressing the limitations imposed by sensor failures. The control scheme incorporates compensation based on the predictor’s estimates, ensuring robust attitude tracking despite the presence of faults. We provide the first proof of bounded stability for this learning observer utilizing intermittent information, expanding its applicability. Numerical simulations demonstrate the effectiveness of this innovative strategy, highlighting its potential for enhancing spacecraft autonomy and reliability in challenging operational scenarios.

Research on Underground Track Tracking of a New Type Shaft Boring Machine

A trajectory tracking control scheme is proposed in this paper to achieve unmanned underground construction of a new type of shaft boring machine. The body motion model is constructed, and the control relationship between the driving system and the body motion is deduced. MATLAB and Simulink simulate the joint of the track tracking process in the new shaft boring machine. The actual effect of the track tracking control scheme is validated by the track tracking field test of the new shaft boring machine. The results show that the new shaft boring machine can complete various complex track tracking tasks, with the maximum position error being less than 0.005 m. The running track and excavation areas of the boring machine are observed along with the track tracking field test of the new shaft boring machine, demonstrating that the track tracking control scheme makes the new shaft boring machine realize unmanned construction and verifies the co-simulation accuracy. The PID (proportional–integral–derivative control) algorithm can complete complex trajectory tracking tasks and promote the development of unmanned construction technology.

Event-Triggered Bipartite Formation Control for Switched Nonlinear Multi-Agent Systems with Function Constraints on States

A distributed adaptive fuzzy event-triggered bipartite formation tracking control scheme is proposed for switched nonlinear multi-agent systems (MASs) with function constraints on states. Fuzzy logic systems (FLSs) are used to identify uncertain items. To improve the transient performance of the system, a fixed-time prescribed performance function (FTPPF) is introduced to make the formation error converge to a prescribed boundary range within a fixed time. Considering that the state constraint boundary is restricted by multiple pieces of information (historical state, topological relationship, neighbor agent output, leader signal and time), a tan-type barrier Lyapunov function (BLF) is constructed to address the challenges brought by the state function constraint. The shortcoming of the “explosion of complexity” is compensated by fusing the backstepping control and command filter. To mitigate the communication burden while ensuring a steady-state performance, a distributed event-triggered fixed-time bipartite formation control scheme is proposed. Finally, the performance of the proposed control method is verified by an MAS consisting of four followers and one leader.

Composite-Observer-Based Adaptive Consensus Tracking Control for Nonlinear MASs With Unknown Control Directions Against Deception Attacks.

This article primarily studies the adaptive output-feedback consensus tracking control issue for nonlinear multiagent systems (MASs) with unknown control directions against deception attacks. First, a composite observer combining the state observer and the disturbance observer is developed to concurrently estimate the states of confronting deception attacks and unmeasurable disturbances. Moreover, to resolve the unknown gains resulting from deception attacks, the adaptive attack compensator is proposed. Furthermore, in view of the logarithm Lyapunov function in the final step of the design process and the intelligent approximation technique, a new composite-observer-based adaptive consensus tracking control strategy is constructed. The suggested control strategy ensures the boundedness of all the closed-loop signals while also achieving synchronous tracking of the leader's output by the followers. Last but not least, the effectiveness of the suggested control strategy is validated through two simulation examples.

Single-parameter-learning-based adaptive tracking control for USVs with unknown sensor measurement sensitivity under actuator saturation

ABSTRACT This paper investigates the adaptive tracking control problem for unmanned surface vehicles (USVs) systems with internal/external uncertainties, unknown sensor measurement sensitivity and actuator saturation. First, the actuator saturation nonlinearity is approximated employing an improved Gaussian error function. The composite uncertain term synthesising unknown sensor measurement sensitivity, parameter uncertainties, and unknown time-varying disturbances, is reformulated into a linear parameterisation-like structure. Then, to reduce the computation burden, a novel adaptive tracking control scheme has been developed utilising the backstepping design paradigm, where only one unknown parameter needs to be updated online. The theoretical framework demonstrates that all signals of USVs are bounded. The efficacy of the proposed scheme is validated through comprehensive simulation analysis.

A New Adaptive Control Design of Permanent Magnet Synchronous Motor Systems with Uncertainties

Symmetry is widely present in science and daily life. And the internal structure of surface-mounted permanent magnet synchronous motors (PMSMs) has good symmetry. This article is dedicated to studying the tracking problem of PMSMs with adaptive and backstepping control methods. The research objective of this study is to design new adaptive controllers Uq and Ud, which enable the state of the motor position servo system to asymptotically and stably track the given signals of the system. They can suppress the impact of changes in B, J, and TL and can also enhance the robustness of the system. (i) The strongly coupled current and speed, variation of parameters over time, and nonlinearity of motor torque objectively pose significant challenges in the design of adaptive tracking controllers for PMSMs. (ii) Adaptive control technology and backstepping control methods are used for designing controllers for the PMSMs. (iii) After rigorous reasoning, an intelligent adaptive tracking control strategy for the PMSMs has been derived, which is for the direct axis current and the angle. (iv) The new adaptive tracking controllers are superior to existing controllers in that they can strongly suppress the disturbance of system parameters J, TL, and B, make the system state asymptotically stable, and achieve good tracking performance for the given signals. The results of the simulation indicate the validity of the designed control strategy.

Adaptive proportional integral derivative fault tolerant control for continuous-time switched systems: A highly maneuverable aircraft technology (HiMAT) vehicle application

This article presents an active fault-tolerant tracking control (FTTC) scheme for switched linear systems with unknown external disturbances and actuator faults. First, a switched adaptive observer (SAO) is designed to simultaneously achieve system state and actuator fault estimation. Second, an active fault-tolerant tracking controller with a reconfigurable adaptive proportional–integral–derivative architecture is proposed based on the results of these estimations. This FTTC technique aims to ensure the tracking of a predefined reference trajectory while also compensating for actuator fault effects. In terms of linear matrix inequalities, less conservatism design conditions for the SAO and fault-tolerant tracking controller are obtained by utilizing the average dwell time technique and multiple Lyapunov functions. Finally, an application to the highly maneuverable aircraft technology vehicle is used to prove the benefits of the presented scheme.

MPC Path Tracking Control Based on GA-PAO

In order to improve the path tracking performance and stability of driverless vehicles. In this paper, a path tracking control strategy based on MPC is proposed to establish a three-degree-of-freedom vehicle dynamical model as a reference model, design the MPC controller, determine the objective function and add constraints. Optimization of important time-domain parameters of model predictive controllers by improved GA-PSO. A Carsim/Simulink co-simulation platform was built to simulate the controller under double-shifted line conditions to verify its effectiveness. Simulation results show that the tracking performance and stability of the optimized MPC controller are much better.

Cooperative maximum adhesion tracking control for multi-motor electric locomotives

PurposeThis study aims to propose a cooperative adhesion control method for trains with multiple motors electric locomotives. The method is intended to optimize the output torque of each motor, maximize the utilization of train adhesion within the total torque command, reduce the train skidding/sliding phenomenon and achieve optimal adhesion utilization for each axle, thus realizing the optimal allocation of the multi-motor electric locomotives.Design/methodology/approachIn this study, a model predictive control (MPC)-based cooperative maximum adhesion tracking control method for multi-motor electric locomotives is presented. Firstly, train traction system with multiple motors is constructed in accordance with Newton’s second law. These equations include the train dynamics equations, the axle dynamics equations, and the wheel-rail adhesion coefficient equations. Then, a new MPC-based multi-axle adhesion co-optimization method is put forward. This method calculates the optimal output torque through real-time iteration based on the known reference slip speed to achieve multi-axle co-optimization under different circumstances.FindingsThis paper presents a MPC system designed for the cooperative control of multi-axle adhesion. The results indicate that the proposed control system is able to optimize the adhesion of multiple axles under numerous different conditions and achieve the optimal power distribution based on the reduction of train skidding/sliding.Originality/valueThis study presents a novel cooperative adhesion tracking control scheme. It is designed for multi-motor electric locomotives, which has rarely been studied before. And simulations are carried out in different conditions, including variable surfaces and motor failing.

Inductor Current-Based Control Strategy for Efficient Power Tracking in Distributed PV Systems

This paper presents an inductor current-based maximum power point tracking (IC-MPPT) strategy and a single-inductor multi-input single-output (SI-MISO) structure with energy storage battery for distributed photovoltaic (PV) systems. In this study framework, the duty cycle of each PV channel can be controlled independently based on the presented IC-MPPT strategy, and the components/sensors costs are reduced through the presented SI-MISO PV system structure. In addition, a model predictive control (MPC) method is presented to regulate DC bus voltage, by controlling the bidirectional converter in the battery circuit. The presented control strategies have been rigorously derived and experimentally validated, and the experimental results demonstrate that each PV module can rapidly and efficiently track to the maximum power point in less than 0.016 s, while the bus voltage is stabilized near the set value, with an overshoot of less than 2.6%.

Discrete‐Time Integral Fast Terminal Sliding Mode Predictive Control for Dynamic Positioning Ships With Lumped Uncertainties and Input Constraints

ABSTRACTThis paper proposes a robust discrete‐time integral fast terminal sliding mode predictive control (DIFTSMPC) scheme for DP ship trajectory tracking under the consideration of unmodeled dynamics, environmental disturbance, and input constraints. An improved single closed‐loop control strategy is designed by adopting the earth‐fixed reference velocity as the virtual velocity. The improved nonlinear PID discrete‐time integral fast terminal sliding mode is designed to achieve faster convergence. The unknown nonlinear dynamics and the environmental disturbance are combined as a lumped uncertainty term and estimated using the one‐step delay observation method. Then the corrected sliding model state is optimized to track the reference sliding reaching law in the prediction horizon. The lumped uncertainty estimation is integrated into the sliding mode prediction to guarantee the robustness of the control scheme. The stability of the proposed scheme has been verified. Comparison results demonstrate the effectiveness and superiority in chattering suppressing and constraint handling of the proposed scheme.

Adaptive Fuzzy Tracking Control for Switched Nonlinear Systems Under FDI Attacks and Input Saturation: A Flexible Transient Performance Approach.

The present study proposes an adaptive fuzzy tracking control strategy for switched nonlinear systems, capable of effectively addressing false data injection (FDI) attacks and input saturation, while achieving flexible prescribed performance control as well as semi-global uniform ultimate boundness for the resultant system. Compared to the previous work, the provided control strategy exhibits two notable strengths: 1) it introduces a novel modified fixed-time pregiven performance function to effectively balance input saturation and output constraint and 2) the detrimental impacts resulting from FDI attacks are successfully mitigated by implementing the fuzzy logic systems approximation technique in the backstepping procedure. A set of switching fuzzy observers are established to estimate the unobservable states while a first-order differential filter is utilized to handle the complexity explosion problem. Finally, the mass-spring-damper system is given to substantiate the developed approach.

Optimal Fully Actuated System Approach-Based Trajectory Tracking Control for Robot Manipulators.

In this article, a trajectory tracking control strategy is proposed for robot manipulators via a fully actuated system (FAS) approach, which has shown its simplicity and flexibility for most of the nonlinear controller design. However, the motion control for robot manipulators is more complicated since unknown dynamical model, external disturbances, friction forces, and various physical constraints are required to be considered. Therefore, the FAS approach cannot be straightforwardly applied. To address these challenges, the dynamic model of robot manipulators is established via model identification methods. Furthermore, based on the identified model, an FAS composite control strategy with simple structure is designed, which is achieved by integrating a high-order disturbance observer (HODO) in the inner loop, with an FAS trajectory tracking controller in the outer loop. Specifically, the HODO is utilized for handling the uncertain dynamics and external disturbances. Moreover, the controller gains are optimized using a gradient-based optimal parameter tuning method (OPTM). By imposing joint angle constraints, joint angular velocity constraints, and input torque limits into the formulation, the OPTM also ensures the satisfaction of these physical constraints. Numerical simulations and experiments are provided to validate the performance of the proposed controller.

Tracking control strategy of tendon driven robotic arm under adaptive neural network

IntroductionWith the rapid optimization and evolution of various neural networks, the control problem of robotic arms in the area of automation control has gradually received more attention.MethodsTo improve the control performance of robotic arms under complex dynamic models, this study proposes an adaptive affective radial basis function network control strategy. Firstly, the kinematic and dynamic mathematical models of the tendon driven robotic arm are constructed. Then, by integrating the affective computing model and the radial basis function network, an adaptive affective radial basis function network control algorithm is constructed.Results and DiscussionThe research results indicate that the designed algorithm significantly outperforms the other two compared algorithms in terms of control accuracy and stability. In benchmark performance testing, the designed algorithm has a error accuracy of up to 0.97 and a steady state of up to 0.95. In the simulation results, the maximum torque change of the designed algorithm is only 3.8 Nm, which is much lower than other algorithms. In addition, the control error fluctuation range of this algorithm is between −0.001 and 0.001, almost close to zero error. This study provides a new optimization strategy for precise control of tendon driven robotic arms, and also opens up new avenues for the application of artificial intelligence technology in complex nonlinear system control.

Review on Maximum Power Point Tracking Control Strategy Algorithms for Offshore Floating Photovoltaic Systems

Floating photovoltaic systems are rapidly gaining popularity due to their advantages in conserving land resources and their high energy conversion efficiency, making them a promising option for photovoltaic power generation. However, these systems face challenges in offshore environments characterized by high salinity, humidity, and variable irradiation, which necessitate effective maximum power point tracking (MPPT) technologies to optimize performance. Currently, there is limited research in this area, and few reviews analyze it comprehensively. This paper provides a thorough review of MPPT techniques applicable to floating photovoltaic systems, evaluating the suitability of various methods under marine conditions. Traditional algorithms require modifications to address the drift phenomena under uniform irradiation, while different GMPPT techniques exhibit distinct strengths and limitations in partial shading conditions (PSCs). Hardware reconfiguration technologies are not suitable for offshore use, and while sampled data-based techniques are simple, they carry the risk of erroneous judgments. Intelligent technologies face implementation challenges. Hybrid algorithms, which can combine the advantages of multiple approaches, emerge as a more viable solution. This review aims to serve as a valuable reference for engineers researching MPPT technologies for floating photovoltaic systems.

Event-triggered adaptive dynamic programming for cable-driven parallel rehabilitation system with uncertainties

An event triggered adaptive dynamic programming (ET-ADP) controller is proposed for the cable-driven parallel rehabilitation system (CDPRS). Firstly, an error dynamics of CDPRS considering composite uncertainty is established. Secondly, to reduce the computational burden, a single critic network ADP method is proposed to obtain the approximate optimal cost function, and an event-triggered mechanism is introduced to save the communication resources simultaneously. Thirdly, considering the unidirectional force characteristics of the cables, the null-space reallocation method is adopted to optimise the tension of the cables. Then, based on Lyapunov stability theory, the uniformly ultimately boundedness (UUB) of the closed-loop system is proved. Finally, effectiveness of the proposed optimal tracking control strategy is verified based on the Matlab/simulink platform.

Fully distributed event-triggered secondary voltage resilient tracking control for microgrids

This paper aims to design the fully distributed event-triggered secondary voltage resilient tracking control strategy for microgrids subject to denial-of-service (DoS) attacks. The fully distributed method is employed to remove the requirement of the smallest nonzero eigenvalue of the Laplacian matrix, which is the global information. In addition, an attack compensation with the latest successful transmission state information from neighbor's as the attack compensation term is designed for both the event-triggered scheme and the controller to save communication resources and mitigate the negative effects caused by DoS attacks. Finally, the effectiveness of the designed fully distributed event-triggered resilient control method subject to DoS attacks is demonstrated via a simulation example with comparisons.

Finite-time trajectory tracking control of quadrotor UAVs based on neural network disturbance observer and command filter

The paper proposes a novel finite-time control strategy for quadrotor UAV trajectory tracking using a neural network disturbance observer and a command filter. This method is used to address input saturation and disturbances, ensuring that the UAV can accurately follow the desired trajectory in finite time. The neural network disturbance observer is crucial for approximating external disturbance signals within a finite time, while the finite-time backstepping scheme accelerates the convergence of tracking errors. The command filtering technique is employed to avoid the complex derivation of virtual control laws, simplifying the controller design. The importance of this method lies in its ability to achieve fast, disturbance-resistant trajectory tracking for UAVs, making the control system more robust in practical applications. Simulations were conducted, showing that the proposed control strategy enables the quadrotor UAV to track its desired trajectory effectively, with improved anti-jamming capability. Both filtering and observation errors converged to the equilibrium point, validating the effectiveness of the approach. However, internal factors like actuator failure were not considered, pointing to future work in refining the method and applying it in real-world UAV experiments.

Disturbance Observer‐Based Neural Adaptive Command Filtered BacksStepping Funnel‐Like Control for the Chaotic PMSM With Asymmetric Prescribed Performance Constraints

ABSTRACTThis paper suggests a neural adaptive command filtered backstepping tracking control strategy for the chaotic permanent magnet synchronous motors with asymmetric prescribed performance constraints. Therefore, enable chaotic permanent magnet synchronous motors (PMSM) to obtain good robustness and better universality in practical industrial environments, and realizes more accurate control effect. The main challenge lies in devising a valid funnel‐like solution within the backstepping frame to handle the asymmetric performance constraints that traditional solutions cannot solve. To achieve this, a novel funnel‐like function is introduced, integrating a performance boundary function independent of initial output error, thereby transforming the system into an unbounded one. Additionally, the “explosion of complexity” with conventional backstepping is mitigated by introducing command filtering and constructing an error compensating system to reduce errors. By combining the theory of Lyapunov function and backstepping technique, the virtual controller and the real controller with adaptive law ensure the stability of the system. The disturbance observer and neural network solve the external disturbance and the uncertain nonlinear problem, respectively. Simulation comparisons confirm the robustness of the proposed control scheme and demonstrate its superiority over existing solutions.

Adaptive Two-Degree-of-Freedom Robust Gain-Scheduling Control Strategy

This study introduces a novel tracking control strategy tailored to aeroengines, which are highly nonlinear and characterized by significant uncertainty. The proposed method entails a robust extended Kalman filter (REKF) enhanced by a forgetting factor for improved performance. An accompanying augmented, mixed onboard adaptive model based on the REKF precisely estimates and manages engine performance degradation. This advanced model effectively counters the degradation term in the perturbation block of the engine’s uncertain model. Using this strategic approach, a robust gain-scheduling controller was constructed and was found to outperform its predecessors, marking a notable advancement in control system design. Controlling twin rotor multi-input, multi-output (MIMO) systems is a highly complex process due to model uncertainties and unpredictable external disturbances. To address these challenges, we constructed an adaptive two-degree-of-freedom robust gain-scheduling controller (ATDF-RGSC) using a mixed sensitivity approach. Rigorous performance analysis confirms that this controller offers enhanced robustness, faster tracking, and more precise disturbance attenuation compared to other methods. These advanced control strategies successfully manage uncertainties and disturbances, improving performance metrics in both simulated and experimental scenarios. The proposed method may significantly enhance the safety and reliability of aeroengines and MIMO systems in practical applications.