Tracking Control Scheme Research Articles

Energy-Aware Optimization of Connected and Automated Electric Vehicles Considering Vehicle-Traffic Nexus

The driving economy of the connected and automated electric vehicles (CAEVs) is seriously affected by the vehicle-traffic nexus. In this paper, an energy-aware optimization (EAO) strategy for improving the energy efficiency of CAEVs is proposed by considering the vehicle-traffic nexus between the traffic environment's dynamic constraint and the vehicle powertrain's constraint. In order to design the EAO strategy, the parameters of the vehicle dynamics model are identified by a proposed bias deep compensative estimator (BDCE). Based on the identified parameters, the traffic environment's constraint is converted to the powertrain's constraint of CAEV. To obtain the optimal energy efficiency under the powertrain's constraint, a new velocity-torque ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">V</i> - <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> ) coordinate system is constructed to standardize the constraint, and a neighborhood iterative searching (NIS) algorithm is proposed to search the optimal efficiency in the coordinate system. With the searched optimal efficiency, a torque tracking control strategy is designed to regulate the electric powertrain to make it operate in the high efficiency region. The experiment is conducted in the real-world scenario to compare the proposed method with two state-of-art methods. Compared with the state-of-art methods, the relative energy-saving percentage of the proposed method can reach more than 7.5%.

Pinning-Based Neural Control for Multiagent Systems With Self-Regulation Intermediate Event-Triggered Method.

A pinning-based self-regulation intermediate event-triggered (ET) funnel tracking control strategy is proposed for uncertain nonlinear multiagent systems (MASs). Based on the backstepping framework, a pinning control strategy is designed to achieve the tracking control objective, which only uses the communication weight between the agents without additional feedback parameters. Moreover, by designing a self-regulation triggered condition based on the tracking error, the intermediate triggered signal is calculated to replace the continuous signal in the controller, so as to achieve the goal of discontinuous update of the controller signal, and this mechanism does not need to add additional compensation function to the controller signal. At the same time, the funnel method is adopted to restrict the error of step n and avoid the possible negative impact caused by control signal. Furthermore, the nonlinear noncontinuous faults are compensated by the disturbance observer. Then, the Lyapunov stability theorem is used to prove that all signals of the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, some simulation results confirm the effectiveness of the proposed control scheme.

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.

A Novel Guided Deep Reinforcement Learning Tracking Control Strategy for Multirotors

A Novel Guided Deep Reinforcement Learning Tracking Control Strategy for Multirotors

A Nonlinear Trajectory Tracking Control Strategy for Quadrotor With Suspended Payload Based on Force Sensor

A Nonlinear Trajectory Tracking Control Strategy for Quadrotor With Suspended Payload Based on Force Sensor

Enhanced Two-Degree-of-Freedom Integrated Position Tracking Control Strategy for PMSM Servo Systems based on SMCFO-LADRC

Enhanced Two-Degree-of-Freedom Integrated Position Tracking Control Strategy for PMSM Servo Systems based on SMCFO-LADRC

Automatic Tracking Control Strategy of Autonomous Trains Considering Speed Restrictions: Using the Improved Offline Deep Reinforcement Learning Method

Automatic Tracking Control Strategy of Autonomous Trains Considering Speed Restrictions: Using the Improved Offline Deep Reinforcement Learning Method

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.

Path Planning and Trajectory Tracking for Autonomous Obstacle Avoidance in Automated Guided Vehicles at Automated Terminals

During the operation of automated guided vehicles (AGVs) at automated terminals, the occurrence of conflicts and deadlocks will undoubtedly increase the ineffective waiting time of AGVs, so there is an urgent need for path planning and tracking control schemes for autonomous obstacle avoidance in AGVs. An innovative AGV autonomous obstacle avoidance path planning and trajectory tracking control scheme is proposed, effectively considering static and dynamic obstacles. This involves establishing three potential fields that reflect the influences of obstacles, lane lines, and velocities. These potential fields are incorporated into an optimized model predictive control (MPC) cost function, leveraging artificial potential fields to ensure effective obstacle avoidance. To enhance this system’s capability, a fuzzy logic system is designed to dynamically adjust the weight coefficients of the hybrid artificial potential field model predictive controller, strengthening the autonomous obstacle avoidance capabilities of the AGVs. The tracking control scheme includes a fuzzy linear quadratic regulator based on a fuzzy logic system, a dynamics model as a lateral controller, and a PI controller as a longitudinal tracker to track the pre-set trajectory and speed autonomously. Multi-scenario simulation tests demonstrate the effectiveness and rationality of our autonomous obstacle-avoidance control scheme.

Practical prescribed-time fuzzy tracking control for uncertain nonlinear systems with time-varying actuators faults

The paper investigates the practical prescribed-time fuzzy tracking control problem for a category of nonlinear system subject to time-varying actuator faults. The presence of unknown nonlinear dynamics and actuator faults makes achieving tracking control within a prescribed-time challenging. To tackle this issue, we propose a novel practical prescribed-time fuzzy tracking control strategy, which is independent of the initial state of the system and does not rely on precise modeling of the system and actuators. We apply the approximation capabilities of fuzzy logic systems to handle the unknown nonlinear functions and unidentified actuator faults in the system. The piecewise controller and adaptive law constructed based on piecewise prescribed time-varying function and backstepping technique method establish the theoretical framework of practical prescribed-time tracking control, and extend the range of prescribed-time tracking control to infinity. Regardless of the initial conditions, the proposed control strategy can guarantee that all signals remain uniformly bounded within the practical prescribed time in the presence of unknown nonlinear item and time-varying actuator faults. Simulation example is presented to demonstrate the effectiveness of the proposed control strategy.

Robust trajectory tracking of quadrotors using adaptive radial basis function network compensation control

Radial Basis Function Neural Networks (RBFNN) methods have gained incredible efficiency and applicability in control. This paper presents a nested control strategy for robust trajectory tracking of a quadrotor using adaptive RBF compensation and NN-supervised control embedded with Integrator BackStepping (IBS). The approach addresses the robustness in the presence of modeling uncertainties, sensing noise, and bounded disturbances. The control design is derived from the decentralized inverse dynamics, using adaptive RBFNN for outer-loop disturbance approximation and compensation. In conjunction with an Inner-loop supervised control that stabilizes the quadrotor attitude, preventing initial instability during NN convergence. In addition, an adaptive Extended Kalman Filter (EKF) attenuates noisy signals. Simulation results demonstrate strong adaptability to changes in model parameters, and superior performance when compared to Proportional Integral Derivative (PID), Integrator BackStepping (IBS), and offline decentralized Multi-Layer Perceptron (MLP) algorithms, in terms of parameter convergence, disturbance compensation control, and noise attenuation.

Predefined‐time tracking control of a class of nonlinear systems via a novel C∞ performance function

AbstractIt is well‐known that the finite‐time and the fixed‐time control can ensure the finite‐time and the fixed‐time stabilities of systems, respectively. Therein the finite convergence time depends on both the initial conditions of systems and the controller design parameters, and the fixed convergence time only depends on the controller design parameters. The recently reported predefined‐time control removes the above limitations. Nevertheless, the existing predefined‐time control methods either require sliding modes, or suffer from the high‐gain control problem at the predefined time, or cannot be applied to high‐order systems. This article develops an alternative predefined‐time tracking control scheme of a class of nonlinear systems by introducing a novel performance function which reaches its terminal value at a predefined time. The developed predefined‐time tracking control scheme has higher compatibility, does not cause the high‐gain control problem at the predefined time and is applicable to arbitrary order systems. Theoretical analyses and simulations verify the effectiveness of our developed predefined‐time tracking control scheme.

Event‐triggered adaptive tracking control for nonlinear systems with input saturation and unknown control directions

AbstractThis article is concerned with event‐triggered adaptive tracking control design of strict‐feedback nonlinear systems, which are subject to input saturation and unknown control directions. In the design procedure, a smooth nonlinear function is employed to approximate the saturation function so that the controller can be designed under the framework of backstepping. The Nussbaum gain technique is employed to address the issue of the unknown control directions. A predetermined time convergent performance function and a nonlinear mapping technique are introduced to guarantee that the tracking error can converge in the predetermined time with a fast convergence rate and a high accuracy. Then the event‐triggered adaptive prescribed performance tracking control strategy is proposed, which not only ensures the boundedness of all the closed‐loop signals and the convergence of tracking error but also reduces the communication burden from the controller to the actuator. At last, the simulation study further tests the availability of the proposed control strategy.

Adaptive fixed-time consensus control of nonlinear multiagent systems with dead-zone output

This paper proposes a fixed-time leader-follower consensus tracking control scheme for uncertain multiagent systems with unknown dead-zone output. First, a novel approximate dead-zone model was introduced to accurately represent the dead-zone phenomenon in the output channel of nonlinear multiagent systems. Furthermore, the model presented is a smooth function, which greatly simplifies the process of fusing it with the backstepping technique. Second, we present a Nussbaum function to compensate for the uncertain gain from the output dead-zone nonlinearity. Third, unlike previous works, we first consider practical fixed-time consensus issues for multiagent systems with output dead-zone. Finally, according to the fixed-time control theory, the tracking error of the multiagent systems converges to the region around zero within a fixed time, which is confirmed by the simulations.

Resilient adaptive trajectory tracking control for uncalibrated visual servoing systems with unknown actuator failures

A resilient adaptive tracking control scheme for uncalibrated visual servoing system with unknown actuator failures is proposed, where the camera parameters, the robotic physical parameters, and the position information of end-effector cannot be measured accurately. For our scheme, the nonlinear composite Jacobian matrix involving unknown parameters is estimated by an adaptive algorithm based on the depth-independent interaction matrix frame so that the image feature with time-varying depth can be tackled effectively. With the use of projection technique in the adaptive algorithm, the possible singularity of the estimated composite Jacobian matrix can be well circumvented without any stringent assumption, which is critical for establishing the closed-loop stability. Moreover, a novel adaptive resilient control scheme based on dynamic decoupling is developed to separate the design of visual servoing controller and fault compensation, improving the visual servoing system resilience against unknown actuator failures in uncalibrated environment. Through a rigorous Lyapunov argument, the asymptotic convergence of image tracking error to zero is established successfully regardless of the occurrence of unknown actuator failures. The effectiveness of the proposed visual tracking control scheme is confirmed by simulation results.

Integrated control of path tracking and handling stability for autonomous ground vehicles with four-wheel steering

Accurate modelling of an autonomous ground vehicle (AGV) is challenging due to strong nonlinearity, model perturbations and external disturbances. This necessitates the design of path tracking controllers with robustness. In this paper, an integrated control strategy for path tracking and handling stability of AGVs is proposed, leveraging a four-wheel steering (4WS) system. To address the path tracking problem, a fuzzy logic-based steering strategy is developed, which enables parameter self-adjustment based on displacement error. Additionally, a reference model is employed to transform the path tracking problem into a reference signal tracking problem, ensuring the handling stability of vehicles. A sliding mode controller is designed to track the reference signal, utilizing an extended state observer to estimate and compensate for unmodeled dynamics and unknown external disturbances in real-time. Simulation results demonstrate the effectiveness of the proposed strategy in accurately tracking the desired path while maintaining the lateral stability of the vehicle. Furthermore, the strategy exhibits strong robustness against uncertainties and disturbances.

A stochastic variance reduction gradient-based GSO-ANFIS optimized method for maximum power extraction of proton exchange membrane fuel cell

Proton Exchange Membrane Fuel Cells (PEMFCs) play a pivotal role in the clean energy landscape, yet their efficiency is contingent upon effective power optimization. This paper presents Maximum PowerPoint Tracking (MPPT) control schemes for PEMFCs, focusing on a ground-breaking methodology. Traditional MPPT controllers are instrumental in maintaining optimal performance; however, they often struggle with dynamic operating conditions. In response to this challenge, this research work presents a pioneering MPPT control scheme employing a stochastic variance reduction gradient system. The novelty of this approach lies in its fusion with the Glow Swarm Optimization (GSO) and the Adaptive Neuro Fuzzy Inference System (ANFIS), resulting in a robust hybrid controller. In the pursuit of optimizing the PEMFC system, the proposed GSO-ANFIS controller is subjected to rigorous testing under dynamic variations in both PEMFC temperature and load. Notably, PEMFCs, due to fluctuations in pressure and temperature, exhibit stochastic behaviour, forming a Gaussian surface. In this research, the popular Perturb and Observe (P&O) and Incremental Conductance methods are evaluated alongside the newly introduced GSO-ANFIS model. The proposed GSO-ANFIS controller outperforms its counterparts, showcasing an impressive accuracy level of 89.97%. In contrast, the Artificial Neural Network (ANN) achieves 80.33% accuracy, and the standalone ANFIS controller attains 86.5% accuracy. This disparity underscores the efficacy and potential of the novel hybrid approach, which not only adeptly handles the stochastic nature of PEMFCs but also significantly enhances accuracy in power optimization. This research not only contributes a valuable addition to the field of MPPT control but also offers a promising trajectory for the future development of efficient and reliable PEMFC systems.

Robust [formula omitted] control for line power flow tracking in power systems

Traditionally, the problem of congestion arising in power system networks due to various contingencies and varying renewable generation is addressed by optimal power flow-based methods, once in every 5–30 min. As these techniques are performed for a block period of time using forecasted data, they are not effective in real-time congestion mitigation through corrective actions. This paper proposes a controller-based approach for real-time congestion management through line power flow regulation. We consider a robust H∞ power flow tracking control strategy to regulate the line power flows to the specified limit, while attenuating the effect of disturbances on the system. Using synchrophasor measurements from the lines, the proposed controller can detect line overloading and automatically re-dispatch the selected generators and battery energy storage systems to maintain power flow within the desired limit. The robust line power flow tracking problem with disturbance rejection is formulated as a dynamic game between the controller and disturbance entities, and robust static output feedback H∞ controller gains are obtained via an iterative algorithm. The efficacy of the proposed controller in regulating line power flow and mitigating congestion is verified by extensive simulations on a 16-machine 68-bus test system and Kundur’s 4-machine 2-area test system. It is shown that the designed controller can regulate line power flows within 40–60 s, which is fast enough to avoid overheating of transmission lines. Furthermore, the proposed strategy is simple from the perspective of design and implementation, and outperforms the existing congestion management techniques.

Adaptive tracking control of a unicycle subject to uncertainties and perturbations

An adaptive trajectory tracking control scheme is proposed for the command of a unicycle robot which is subject to dynamic uncertainties (inertia, mass, viscous parameter, etc), unmodeled dynamics, unknown disturbances, slipping effects, and loss of effectiveness (LOE). The latter are adaptively estimated and compensated in control laws. Moreover, projection algorithms are added to bound estimators, preventing them from drifting. The control objective is developed in two steps. First, a kinematic control law is designed to compute the necessary velocities (linear and angular velocities) that allow the robot to reach the desired trajectory. Afterward, sliding mode based adaptive control torques are developed in order to track velocities computed in the first step. The proposed control strategy guarantees the stability of the closed-loop system by ensuring the asymptotic convergence to both desired trajectory and necessary velocities. Finally, simulation results illustrate the applicability and effectiveness of the proposed control scheme.