Tracking Control Scheme Research Articles

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.

Dual-loop PID control strategy for ramp tracking and ramp disturbance handling for unstable CSTRs

Dual-loop PID control strategy for ramp tracking and ramp disturbance handling for unstable CSTRs

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.

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.

Advanced Control Scheme Optimization for Stand-Alone Photovoltaic Water Pumping Systems

This study introduces a novel method for controlling an autonomous photovoltaic pumping system by integrating a Maximum Power Point Tracking (MPPT) control scheme with variable structure Sliding Mode Control (SMC) alongside Perturb and Observe (P&O) algorithms. The stability of the proposed SMC method is rigorously analyzed using Lyapunov’s theory. Through simulation-based comparisons, the efficacy of the SMC controller is demonstrated against traditional P&O methods. Additionally, the SMC-based system is experimentally implemented in real time using dSPACE DSP1104, showcasing its robustness in the presence of internal and external disturbances. Robustness tests reveal that the SMC controller effectively tracks Maximum Power Points (MMPs) despite significant variations in load and solar irradiation, maintaining optimal performance even under challenging conditions. The results indicate that the SMC system can achieve up to a 70% increase in water flow rates compared with systems without MPPT controllers. Furthermore, SMC demonstrated high sensitivity to sudden changes in environmental conditions, ensuring efficient power extraction from the photovoltaic panels. This study highlights the advantages of integrating SMC into Photovoltaic Water Pumping Systems (PV-WPSs), providing enhanced control capabilities and optimizing system performance. The findings contribute to the development of sustainable water supply solutions, particularly in remote areas with limited access to the electrical grid.

Adaptive third-order integral sliding mode control for the mobile petroleum crane-form pipelines system with input bounded

The mobile petroleum crane-form pipelines (MPCFP) system is one of the most important mechanical equipment in the petrochemical industry, consisting of rigid pipelines and rotating elbows, mainly used for loading and unloading petrochemical fluids. This paper proposes a novel neural network adaptive third-order integral sliding mode tracking control strategy with bounded inputs to achieve automatic alignment between the MPCFP system and the inlet of the tank truck. The proposed strategy replaces normal system inputs with the time derivative of the control torque. Although there are discontinuous terms in the derivative of the control torque, the integrated control torque is continuous. This unique feature ensures finite-time convergence of system state errors and eliminates sliding mode chattering. To address the issues of bounded input and modeling error, a state compensation method is introduced, and a neural network adaptive algorithm is utilized to estimate the modeling error. The stability proof process employs the Lyapunov function method, demonstrating that the system state error can converge to zero. Numerous simulation experiments validate the effectiveness of this control strategy.

Development of system analysis code and its application in transient simulation of supercritical CO2 Brayton cycle direct cooled reactors

The supercritical carbon dioxide (s-CO2) Brayton cycle direct cooled reactor system has the characteristics of high cycle efficiency, compact equipment, and simple layout, which has attracted the attention of researchers recently. The use of s-CO2 Brayton cycle and the elimination of intermediate heat exchangers make the transient characteristics of this reactor different from light water reactors which have rich operating experience. Transient simulation and safety analysis with system code is an essential stage in reactor design and safety verification. Therefore, a system code, named NUSAS (NUclear Safety Analysis and Simulation), was developed in this study for the transient simulation of s-CO2 Brayton cycle direct cooled reactor system. This code is developed based on the advanced two-fluid two-pressure seven-equation model and has the capability of modelling the turbomachinery in s-CO2 Brayton cycle direct cooled reactor system. The transient characteristics during core power step and precooler cooling water mass flow rate step were analyzed. The load tracking and operation control strategy of the reactor was designed, and transient simulations of the wide range (100% to 50%) load tracking process were conducted. According to the simulation results of NUSAS, the transient characteristics of s-CO2 Brayton cycle direct cooled reactor were demonstrated when operating conditions change.

Trajectory tracking for autonomous surface ships using Gaussian process regression and model predictive control with BVS strategy

Autonomous navigation is critical to the development of next-generation shipping systems. The proposal of intelligent ships enables innovation in the shipping and shipbuilding industry and increases the safety and efficiency of ship operations. Autonomous control of surface ships to follow a prescribed trajectory is critical in a variety of maritime applications. This paper proposes a trajectory tracking control strategy for autonomous surface ships that combines nonparametric modelling using Gaussian Process Regression (GPR) with the Model Predictive Control (MPC) framework. A Bézier curve-based Virtual Ship(BVS) guidance strategy is proposed to convert dynamic trajectory points into reference heading angles and speeds, such that the trajectory tracking problem can be decomposed into heading control and speed control problems. Gaussian process regression is utilised to identify the correlation between propeller revolution speed and ship speed, as well as the correlation between rudder angle and heading angle based on experimental data. Two GPR models are therefore constructed as the prediction models for designing MPC controllers for heading control and speed control, respectively. Nonlinear optimisation algorithms are utilised to search for optimal control commands in each sampling interval to solve the optimisation problem in MPC with GPR models and input constraints. Simulations are carried out to evaluate the effectiveness of the proposed method.

Path tracking and energy efficiency coordination control strategy for skid-steering unmanned ground vehicle

The skid steering unmanned ground vehicle (SUGV) plays an important role in extremely harsh environments. Improving the autonomous control capability and energy efficiency of SUGV is urgently needed. This article presents a skid steering-based path tracking control strategy. In the upper controller, an improved model-free sliding mode controller (APMS) is used to calculate the yaw moment for tracking control. On the lower controller, the Snow Ablation Optimizer (SAO) is used to distribute the output torque of the drive motors, taking longitudinal force, yaw moment and energy consumption into account. Finally, the designed controller is validated through simulation under different operating conditions. The results show that the proposed coordination controller achieves good control performance, increases energy efficiency and at the same time ensures tracking accuracy.

Barrier function-based prescribed performance trajectory tracking control of wheelchair upper-limb exoskeleton robot under actuator fault and external disturbance: experimental verification

This paper presents an innovative control strategy for the trajectory tracking of wheelchair upper-limb exoskeleton robots, integrating sliding mode control with a barrier function-based prescribed performance approach to handle actuator faults and external disturbances. The dynamic model of the exoskeleton robot is first extended to account for these uncertainties. The control design is then divided into two phases. In the first phase, the sliding mode control technique is applied to ensure robust trajectory tracking by defining the tracking error between the robot’s states and desired trajectories. A sliding surface is constructed based on this error, and to further enhance tracking performance, a prescribed performance control scheme is incorporated, which ensures fast error convergence and improves transient behavior. In the second phase, an advanced barrier function technique is introduced to mitigate the impact of actuator faults and disturbances, enhancing the overall robustness of the system. Stability and tracking accuracy are rigorously verified through Lyapunov theory, ensuring the system's resilience to uncertainties. The combined approach not only guarantees rapid error convergence but also prevents performance degradation due to excessive control action, maintaining system stability. Finally, the effectiveness of the proposed method is demonstrated through extensive simulations and hardware-in-loop experiments, highlighting its practical applicability for real-world exoskeleton systems.

A non-singular predefined-time sliding mode tracking control for space manipulators

This research presents a predefined-time control strategy for the trajectory tracking of a space manipulator subjected to parametric uncertainty and time-varying disturbance. Firstly, a nonlinear disturbance observer with predefined time convergence is constructed to achieve accurate estimation of the lumped disturbance without requiring any prior information. Subsequently, a unique predefined-time terminal sliding surface containing arctan function is proposed, which avoids the singularity problem that exists in traditional terminal sliding surfaces. Afterwards, a novel robust terminal sliding mode control law is designed based on the estimated knowledge. The developed controller ensures the predefined time stability of the closed-loop system, and the convergence time’s upper bound can be explicitly defined in advance through two specific parameters for any initial conditions. Finally, numerical simulations and comparative tests verify the effectiveness and superiority of the suggested controller.

Decentralized Event-Triggered Tracking Control for Unmatched Interconnected Systems via Particle Swarm Optimization-Based Adaptive Dynamic Programming.

The problem of the large-scale interconnected system (LSIS) control is prevalent in practical engineering and is becoming increasingly complex. In this article, we propose a novel decentralized event-triggered tracking control (ETTC) strategy for a class of continuous-time nonlinear LSIS with unmatched interconnected terms and asymmetric input constraints. First, auxiliary subsystems are established to address the unmatched cross-linking terms. Next, the dynamics states of the tracking error and the exosystem are combined to construct a nominal augmented subsystem. By employing a nonquadratic performance function, the input-constrained decentralized tracking control problem is transformed into an optimal control problem for the nominal augmented subsystem. A group of independent parameters and event-triggered conditions are designed to save communication bandwidth and computational resources. Subsequently, the critic-only adaptive dynamic programming (ADP) method is used to solve the Hamilton-Jacobi-Bellman equation (HJBE) associated with the optimal control problem. To improve training success rate, the weights of the critic neural network (NN) are updated by introducing a particle swarm optimization algorithm (PSOA). The tracking error and the NN weights are proved to be uniformly ultimately bounded (UUB) under the proposed ETTC by using the Lyapunov extension theorem. Finally, the simulation example of an unmatched interconnected system is provided to verify the validity of the proposed decentralized method.

Prescribed Fixed-Time Control for Constrained Uncertain Nonlinear Cyber-Physical Systems Against Deception Attacks.

In this note, a novel prescribed fixed-time adaptive tracking control scheme is developed to cope with the fixed-time tracking control issue for a category of constrained MIMO nonlinear cyber-physical systems (CPSs) with exogenous perturbations, which suffer from deception attacks started in controller-actuator (C-A) channel. Distinguished from the conservative dynamic surface control (DSC) schemes with a linear filter, a novel nonlinear filter is designed in our strategy, which can tackle the intrinsic issue of explosion of computational complexity and promote the system performance. Besides, a new barrier Lyapunov function (BLF) is designed to ulteriorly enhance the tracking performance on the basis of the prescribed performance function (PPF) approach. Prominently, the proposed control strategy could accommodate the exogenous interferences and deception attacks simultaneously. Furthermore, we have substantiated that the developed approach can not only make certain that all the tracking errors of the resulting closed-loop system, including output tracking errors and virtual tracking errors, enter a prespecified small region near equilibrium point with fixed-time convergence rate, but also guarantee them obey the corresponding constraints throughout the entire control operation, where the regulation time and the tracking accuracy level keep prior known and could be prespecified arbitrarily. Finally, the validity and effectiveness of the proposed control scheme are illustrated through a representative application instance.

Reinforcement Learning-Based Predefined-Time Tracking Control for Nonlinear Systems Under Identifier-Critic-Actor Structure.

A novel reinforcement learning-based predefined-time tracking control scheme with prescribed performance is presented in this article for nonlinear systems in the presence of external disturbances. First, by employing the backstepping strategy, an adaptive optimized controller is developed under the identifier-critic-actor framework. Therein, the unknown nonlinear dynamics and the system control behavior can be learned effectively through neural networks. Moreover, aiming at obtaining the preset tracking performance, the prescribed performance control is integrated with the predefined-time control. In contrast to previous studies, the proposed scheme can not only constrain the tracking error rapidly to a prearranged vicinity of origin, but also ensure that the upper bound of convergence time can be adjusted in advance via a separate control parameter. In terms of the predefined-time stability theory, the boundedness of all system states can be proven within a predefined time. Finally, the availability and improved performances of the proposed control scheme are demonstrated by a numerical example and a single-link manipulator example.