Tracking Control Approach Research Articles

Fully distributed data-driven model-free adaptive control for consensus tracking in multi-agent systems.

This paper introduces a fully distributed model-free adaptive control (MFAC) approach for consensus tracking in multi-agent systems (MASs) with compact form data linearization (CFDL). Unlike prior methods that require agents to know the full communication graph, our approach allows each agent to configure its controller using only local information from its neighbors, achieving a fully distributed control. Therefore, our method easily supports scenarios where agents dynamically join or leave MAS. Additionally, our approach does not require a strongly connected communication graph and consensus can be achieved as long as the graph includes a spanning tree with the leader as the root. Simulations demonstrate that this method converges faster to the desired trajectory compared to previous MFAC-based methods.

NMPC-Based Path Tracking Control Through Cascaded Discretization Method Considering Handling Stability for 4WS Autonomous Vehicles Under Extreme Conditions

In the realm of autonomous driving, motion control generally involves two critical aspects—path following and stability control—and an inevitable mutual interference exists between them under extreme conditions. To tackle this challenge, this study proposes a collaborative approach of path tracking and stability control. When designing the path tracking control module, the effects of vertical tire load transfer and road surface adhesion coefficients on tire force calculations were taken into account to mitigate vehicle dynamics model mismatch. Leveraging the receding horizon optimization characteristic of nonlinear model predictive control (NMPC), a cascaded discretization approach was utilized to realize a balance between precision and real-time performance in numerical solutions. Then, a stability controller, which employs rear wheel steering, was designed to prevent excessive increases in the vehicle’s sideslip angle, thereby ensuring the vehicle’s lateral stability. The effectiveness of the proposed strategy is validated through CarSim 8.0/Simulink cosimulation. The outcomes demonstrate that the stability controller significantly enhances vehicle stability under high-speed and low-adhesion conditions. On the premise of stability, the proposed path tracking controller has exhibited significant enhancements in real-time performance, without compromising the accuracy of path tracking.

Observer-Based Prescribed Performance Adaptive Neural Network Tracking Control for Fractional-Order Nonlinear Multiple-Input Multiple-Output Systems Under Asymmetric Full-State Constraints

In this work, the practical prescribed performance tracking issue for a class of fractional-order nonlinear multiple-input multiple-output (MIMO) systems with asymmetric full-state constraints and unmeasurable system states is investigated. A neural network (NN) nonlinear state observer is developed to estimate the unmeasurable states. Furthermore, the barrier Lyapunov functions with the settling time regulator are employed to deal with the asymmetric full-state constraint from the fractional-order MIMO system. On this ground, the prescribed performance adaptive tracking control approach is designed, assuring that all system states do not exceed the prescribed boundaries, and the tracking errors converge to the predetermined compact sets within a predefined time. Finally, two simulation examples are presented to show the effectiveness and practicability of the proposed control scheme.

A Model Predictive Backstepping Control Approach for Angle Tracking of Steer-by-Wire System

A Model Predictive Backstepping Control Approach for Angle Tracking of Steer-by-Wire System

Iterative learning robust security predictive tracking control for small delay batch processes: Deception attacks on unreliable network channel

During the batch process, the need for security control is becoming increasingly urgent with the gradual penetration of network control technology. For small time delay batch processes subject to deception attacks, an iterative learning robust security predictive tracking control approach is developed. Reviewing previous studies for iterative learning control, the issue that the repetitive character of the batch process would mask the characteristics of deception attacks was not considered. Moreover, the gains of iterative learning control law are utilized offline, which makes large deviations for the system state as the operating time increases. This will lead to “excessive-input” conditions for control inputs, which means more consumption of energy and production resources. To address these challenges, we introduce robust security invariant sets to improve the robustness of the system by constraining the system state to be in a safe range. Also, the design of the iterative learning controller incorporates deception attack data for historical batches, which is continuously improved and optimized to withstand deception attacks. Meanwhile, by calculating stability conditions online, the real-time control law gain is obtained, which could make the control inputs adjusted online based on the real-time system’s dynamic characteristics, avoiding excessive inputs and improving the efficiency of energy and resource utilization. Additionally, the stability analysis proves that the system is input to state stability. Ultimately, simulation verifies the effectiveness and feasibility of the developed method.

A Unified Approach for Tracking Control of MIMO Nonlinear Systems Under Unknown Control Directions and Irregular Constraints

A Unified Approach for Tracking Control of MIMO Nonlinear Systems Under Unknown Control Directions and Irregular Constraints

A lie group PMP approach for optimal stabilization and tracking control of autonomous underwater vehicles

AbstractIn this research, we explore a finite horizon optimal stabilization and tracking control scheme for the dynamical model of a 6‐DOF Autonomous Underwater Vehicle (AUV). Dynamical equations of the AUV are represented in a Lie group () framework, encompassing both translational and rotational motions. Utilizing a left Lie group action on , we define error function for velocities via a right transport map to effectively address optimal trajectory tracking. The optimal control objective is formulated as a trade‐off problem, aiming to minimize both errors and control effort simultaneously. Left action on yields the left trivialized Hamiltonian function from which the concomitant state and costate dynamical equations are derived using Pontryagin's Minimum Principle (PMP). Consequently, the resulting two‐point boundary value problem is solved to obtain optimal trajectories. We demonstrate the optimality of the resulting solution obtained from the derived control law. For ensuring boundedness in the presence of small disturbances, this study incorporates the effects of internal parametric uncertainties associated with added mass and inertia components, along with the influence of external disturbances induced by ocean currents. Through simulation validations, we confirm the alignment of our results with the theoretical developments, demonstrating that the proposed control law effectively mitigates both parametric uncertainties and ocean current disturbances.

Synergetic Control-Based Sea Lion Optimization Approach for Position Tracking Control of Ball and Beam System

Synergetic Control-Based Sea Lion Optimization Approach for Position Tracking Control of Ball and Beam System

A Novel Approach for Trajectory Tracking Control of an Under-Actuated Quad-Rotor UAV

A novel Udwadia-Kalaba approach for the trajectory tracking control of an under-actuated quad-rotor unmanned aerial vehicle (UAV) is presented. Compared to standard control approaches, the desired trajectories are treated as constraints called trajectory tracking constraints in this approach. Neither making any approximations or linearization of the nonlinear system nor imposing any a priori structure on the nature of the nonlinear controller, this methodology provides closedform nonlinear control. The control inputs satisfying the desired trajectory requirements can be obtained explicitly in compact closed form by solving Udwadia-Kalaba equation. Nonlinear dynamics modeling of a quad-rotor UAV is processed and a desired trajectory is given in this paper to illustrate this approach. The theoretical analysis and MATLAB simulation results verify the validity and efficiency of this approach. The real-time servo constraint forces are obtained conveniently and the quad-rotor UAV's movement meets the designed trajectory precisely.

Optimized Backstepping Cooperative Control for Output-Constrained Stochastic Nonlinear Network Systems via a Multibridge-Hole Function.

In this article, a new leader-following tracking control approach is investigated for stochastic multiagent systems with multibridge-hole output constraints. The multibridge-hole output constraints mean that the output of the system is constrained in some intervals and unconstrained in other intervals. The constrained and unconstrained intervals can be set arbitrarily. By designing a new shift function to construct the barrier Lyapunov function, the optimal controller is constructed by combining the backstepping technique with the adaptive dynamic programming technique. The model network is used to estimate the unknown disturbances and uncertainty terms in the system. The critic network and the actor network are constructed such that the designed controller adheres to the Bellman optimality principle and gives the optimal solution of the system. The proposed control method is versatile and compatible with various types of output constrained control problems, such as unconstrained control problems, constrained control problems, and delay constrained problems without changing the structure of the controller. Finally, some simulation results are given to verify the effectiveness of the method.

Robust H-Infinity Speed Controller based Optimization Technique for Doubly Salient Singly Excited Motor Drive

This research presents a novel robust controller for Doubly Salient Singly Excited motor (DSSEM) speed controlling. This algorithm aims to minimize the influence of disturbances that affect motor function and offer the best duty cycles possible to accomplish tracking performance. The H-infinity () tracking control approach can optimally solve the tracking game algebraic Riccati equation online by using reinforcement learning. This method enhances the Doubly Salient Singly Excited Motor (DSSEM) system model with the reference current model to produce a quadratic value function. The quadratic value function facilitates the implementation of a linear quadratic tracker, similar to control. By partitioning and organizing the nonlinear domain of the DSSEM into a table of nodes, the system can manage complex nonlinearities efficiently. Each node in the table represents a specific region of the control space, allowing for precise and robust tracking of desired reference signals. This structure ensures that the DSSEM achieves optimal performance and stability, adapting dynamically to operational changes. Furthermore, the tracking control is outfitted with a linear interpolation method to facilitate a seamless transition between matrices in the table. Finally, this paper presents simulation results to validate the proposed control algorithm.

Optimizing solar energy efficiency with an improved hill-climbing maximum power point tracking control approach: hardware implementation

Abstract This article implements maximum power point tracking (MPPT) based on the improved hill-climbing algorithm for photovoltaic (PV) systems feeding resistive loads. A direct current-to-direct current boost converter is inserted between the PV system and the load to achieve matching. The converter is managed using MPPT based on the hill-climbing algorithm. The objective of this paper is to optimize the code program to achieve the best compromise between accuracy and rapidity by implementing this algorithm using a microcontroller. Two PV systems are tested under identical meteorological conditions. In the first, an improved hill-climbing MPPT controller is used whereas, in the second, the conventional version is employed. The experimental results obtained show a significant enhancement in terms of speed for the improved algorithm with a value of 0.4 s for the response time and 3% for the oscillation power; those values remain satisfactory in terms of precision of the algorithm compared with the conventional system studied and the compared algorithm from the literature.

A Non-Linear Offset-Free Model Predictive Control Design Approach

This paper presents a non-linear model predictive control approach for offset-free tracking and the rejection of piece-wise constant disturbances. The approach involves augmenting the system’s state vector with the integral of the tracking error, enabling the design of a non-linear model predictive controller for this augmented system. Nominal closed-loop stability is enforced thanks to a terminal equality constraint and proven by a Lyapunov argument. Compared to the existing offset-free approaches in the literature, our method offers greater simplicity, as it does not rely on linear approximations of the system to control. Furthermore, it eliminates the need to estimate disturbances, a task that is especially challenging with non-linear systems. Comprehensive simulations and experimental tests are conducted according to a non-linear, coupled, two-tank laboratory experiment, demonstrating the robustness and effectiveness of the proposed approach.

Cascade MFAC-based data-driven control for aircraft in 4D trajectory tracking

This study aims to explore a trajectory tracking control approach in four-dimensional (4D) trajectory operation by employing cascade model-free adaptive control (MFAC) technique. The primary objective is to tackle the complexities associated with modeling aircraft mechanisms in the context of tracking control. By utilizing measurable input and output data, an equivalent dynamic linearized data model of the aircraft is constructed. Subsequently, position and speed controllers are developed based on MIMO-CFDL-MFAC and SISO-CFDL-MFAC, respectively. These controllers are then integrated to form an active-follower control system. Finally, a numerical simulation was conducted to track the entire 4D trajectory, encompassing climb, cruise, and descent stages, using historical data from flight CX8176 as a reference trajectory. Unlike previous researches, the proposed method eliminates the requirement for an aircraft mechanism model in the controller design, and it is verified using real flight data as a reference trajectory. The findings demonstrate that the cascade MFAC method can effectively minimize trajectory tracking errors, while also offering advantages in terms of energy conservation and emission reduction.

Modified control approach for MPP tracking and DC bus voltage regulation in a hybrid standalone microgrid

This paper proposes modified MPPT control technique for hybrid standalone microgrid. Standalone PV based microgrid has emerged as potential solutions to electricity challenges in the region where grid is not available. The battery energy storage system (BESS) is integrated into the system to facilitate synchronized load control and power flow. Variation in solar irradiation, load demand and fluctuation in battery SOC results in DC voltage fluctuation and maximum power point tracking challenges. Hybrid PV-Battery systems therefore must require control algorithms that can both flexibly adjust power flows as well as stabilize bus voltages. Proposed control technique is successful in regulating DC bus voltage and balancing the power flow in system under various operating conditions. A bidirectional converter is employed to maintain the nominal DC voltage conditions by charging/discharging the battery due to intermittent PV generation. It regulates the voltage of the DC bus to the maximum power point of the PV array. The main goal of this research article is to implement modified MPPT technique which includes Incremental conductance algorithm with double closed loop controller to track maximum power and to regulate the DC bus voltage of the system under different type of dynamic and atmospheric conditions.

Composite‐observer‐based asynchronous control for hidden Markov nonlinear systems with disturbances

SummaryIn this article, an asynchronous adaptive tracking control approach is presented for a type of hidden Markov jump nonlinear systems with external disturbances. In this joint jump process model, hidden Markov model signifies the dynamics of the actual system, whereas the signal emits from the detector symbolizes the transmitted information. This leads to the phenomenon of asynchronization between the modes of the system and that of the controller. Accordingly, an asynchronous observer is developed by using the mode information from the detector to develop an asynchronous control approach. The observer contains a disturbance estimation part, to compensate the unknown external inputs. Utilizing the backstepping scheme, a strict‐feedback asynchronous tracking controller is formulated, guaranteeing that all signals within the closed‐loop system are semi‐globally uniformly ultimately bounded in probability. Finally, the validity of the presented methodology is illustrated by means of a simulation example.

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.

Reinforcement learning‐based finite‐time cross‐media tracking control for a cross‐media vehicle under unknown dynamics and disturbances

AbstractThis study proposes a reinforcement learning‐based finite‐time cross‐media tracking control approach for a slender body cross‐media vehicle encountering unknown hydrodynamics, wind, and wave disturbances. Initially, a reinforcement learning framework consisting of the actor neural network and critic neural network is constructed. The critic neural network monitors the actions of the actor neural network and approximates the cost function, while the actor neural network estimates the unknown hydrodynamics and disturbances, minimising the cost function to optimise performance. Subsequently, the command filter featuring finite‐time convergence is formulated, effectively managing the corresponding filter error through a proposed error compensating signal. By integrating these techniques, a reinforcement learning‐based finite‐time control strategy is developed, circumventing the singularity issue inherent in traditional finite‐time backstepping strategies. Comparative analysis with existing methods demonstrates the strong robustness of the proposed scheme against unknown hydrodynamics and disturbances, ensuring finite‐time convergence of the system's states and optimising controller performance. Finally, simulations confirm the effectiveness and superiority of the presented approach.

Dynamics modeling and trajectory tracking control of a life support robot using sliding-mode control with extended state observer

This paper investigates the trajectory tracking control problem of a life support robot chassis under the slipping disturbances. First, a novel dynamics model for the robot chassis is proposed based on Lagrange equation. The constructed model incorporates the effects of rotational kinetic energy from the driving wheels, providing a more comprehensive representation of the chassis dynamics. Then, on the basis of the innovative framework, the dual-loop trajectory tracking control is derived for the chassis, via the kinematics-based backstepping controller and the dynamics-based sliding-mode controller. Moreover, an extended state observer is designed to reduce the influence caused by external disturbances. Finally, the effectiveness and superiority of the proposed tracking control approach are verified through simulation experiments.

Insights of using control theory for minimizing induced seismicity in underground reservoirs

Deep Geothermal Energy, Carbon Capture, and Storage and Hydrogen Storage have significant potential to meet the large-scale needs of the energy sector and reduce the CO2 emissions. However, the injection of fluids into the earth’s crust, upon which these activities rely, can lead to the formation of new seismogenic faults or the reactivation of existing ones, thereby causing earthquakes. In this study, we propose a novel approach based on control theory to address this issue. First, we obtain a simplified model of induced seismicity due to fluid injections in an underground reservoir using a diffusion equation in three dimensions. Then, we design a robust tracking control approach to force the seismicity rate to follow desired references. In this way, the induced seismicity is minimized while ensuring fluid circulation for the needs of renewable energy production and storage. The designed control guarantees the achievement of the control objectives even in the presence of system uncertainties and unknown dynamics. Finally, we present simulations of a simplified geothermal reservoir under different scenarios of energy demand to show the reliability and performance of the control approach, opening new perspectives for field experiments based on real-time regulators.