Full prescribed performance trajectory tracking control strategy of autonomous underwater vehicle with disturbance observer.

This paper investigates trajectory tracking control of the Autonomous Underwater Vehicle (AUV) with the general uncertainty consisting of model uncertainties and unknown ocean current disturbances. A full prescribed performance control strategy based on disturbance observer is developed, which ensures that the tracking error, the velocity error, and the observation error are all constrained. First, under the case of unmeasurable AUV acceleration, a fixed-time observer is constructed to estimate the general uncertainty, which constrains the observation error within the prescribed accuracy by a prescribed performance observer. Then, based on the performance function and corresponding error transformation, a prescribed performance protocol is designed to realize the trajectory tracking control, so that the observation error, the tracking error, and the velocity error are all constrained within the prescribed accuracy range. Simulation results demonstrate the efficiency of the full prescribed performance control strategy while the AUV tracking control with full state constraints is feasible. Moreover, compared with the other two relevant works, this study improves the observation performance by at least 10 %, both in case of deepwater disturbances and near-surface disturbances.

High-performance finite-time adaptive control strategy for three-level T-type converters.

Three-level T-type converters are necessary interfaces for distributed energy resources to interact with the public grid. Naturally, designing a control strategy, featuring superior dynamics and strong robustness, is a promising solution to guarantee the efficient operation of converters. This article presents an improved finite-time control (IFTC) strategy for three-level T-type converters to enhance the dynamic performance and anti-disturbance capacity. The IFTC strategy integrates a dual-loop structure to regulate the dc-link voltage and grid currents. Specifically, the voltage regulation loop employs a finite-time adaptive controller that can counteract load disturbances without relying on current sensors. In the current tracking loop, finite-time controllers combined with a command filter are constructed to obtain fast and accurate current tracking. In this loop, the command filter is utilized to avoid calculating the derivative of current references. Theoretical analysis and experimental results demonstrate the IFTC strategy's effectiveness.

Advanced optimal tracking integrating a neural critic technique for asymmetric constrained zero-sum games.

This paper investigates the optimal tracking issue for continuous-time (CT) nonlinear asymmetric constrained zero-sum games (ZSGs) by exploiting the neural critic technique. Initially, an improved algorithm is constructed to tackle the tracking control problem of nonlinear CT multiplayer ZSGs. Also, we give a novel nonquadratic function to settle the asymmetric constraints. One thing worth noting is that the method used in this paper to solve asymmetric constraints eliminates the strict restriction on the control matrix compared to the previous ones. Further, the optimal controls, the worst disturbances, and the tracking Hamilton-Jacobi-Isaacs equation are derived. Next, a single critic neural network is built to estimate the optimal cost function, thus obtaining the approximations of the optimal controls and the worst disturbances. The critic network weight is updated by the normalized steepest descent algorithm. Additionally, based on the Lyapunov method, the stability of the tracking error and the weight estimation error of the critic network is analyzed. In the end, two examples are offered to validate the theoretical results.

Finite-horizon optimal secure tracking control under denial-of-service attacks.

The finite-horizon optimal secure tracking control (FHOSTC) problem for cyber-physical systems under actuator denial-of-service (DoS) attacks is addressed in this paper. A model-free method based on the Q-function is designed to achieve FHOSTC without the system model information. First, an augmented time-varying Riccati equation (TVRE) is derived by integrating the system with the reference system into a unified augmented system. Then, a lower bound on malicious DoS attacks probability that guarantees the solutions of the TVRE is provided. Third, a Q-function that changes over time (time-varying Q-function, TVQF) is devised. A TVQF-based method is then proposed to solve the TVRE without the need for the knowledge of the augmented system dynamics. The developed method works backward-in-time and uses the least-squares method. To validate the performance and features of the developed method, simulation studies are conducted in the end.

Robust tracking control of flexible manipulators using hybrid backstepping/nonlinear reduced-order active disturbance rejection control.

This study presents a novel hybrid control strategy for single-link flexible-joint robot manipulators, addressing inherent uncertainties and nonlinear dynamics. By integrating nonlinear reduced-order active disturbance rejection control (NRADRC) with backstepping control, the proposed method effectively estimates and mitigates nonlinear dynamics and external disturbances. Utilizing a nonlinear reduced-order extended state observer (NRESO) enhances resilience to internal and external uncertainties. The global stability of the proposed controller is rigorously established using the Lyapunov approach. Numerical comparisons with state-of-the-art nonlinear control methods demonstrate the superior efficiency and robustness of the proposed approach, especially under varying payloads and disturbances, advancing robotic control solutions.

Observer-Based Adaptive Sliding Mode Compensation Position-Tracking Control for Drilling Tool Attitude Adjustment.

This study develops an adaptive sliding mode control approach for a drilling tool attitude adjustment system, aiming at solving the problems of model uncertainties and insufficient ability of disturbance suppression during the regulation behavior. To further improve the performance of the position-tracking loop in terms of response time, tracking accuracy, and robustness, a state observer based on an improved radial basis function is designed to approximate the model uncertainties, a valve dead-zone compensate controller is used to reduce control deviation, an adaptive sliding mode controller is designed to improve the position-tracking precision and attenuate sliding mode chattering. Finally, simulation and experimental results are carried out to verify the observability of the model uncertainties and position-tracking errors of the drilling tool attitude adjustment system, which can effectively improve the position-tracking performance and robustness of the drilling tool attitude adjustment system.

Resilient exponential tracking for disturbed systems with communication links faults.

Resilience is to appraise the ability of disturbed systems to recover cooperative performance after suffering from failures or disturbances. In this paper, the improvement on the exponential tracking resilience for disturbed Euler-Lagrange systems is explored by settling the unknown time-variant faults imposed on the communication interaction between agents. First, we transform the resilient exponential tracking problem into designing the trajectory and velocity observers for leaders, and showcase that the proposed observers are resilient to communication interaction malfunctions. Second, a disturbance observer is manifested to estimate disturbances precisely, which is needless to know the upper bound of disturbance. The reliable observers and estimator are incorporated into the resilient tracking control frame. Further, the global exponential stabilization of the tracking systems is performed by utilizing the Lyapunov theory. Moreover, benefiting from feasible and reliable observation and estimation results, the proposed control framework enables to realize a satisfactory resilient exponential tracking performance even in the case of communication links faults (CLFs) and disturbances. Comprehensive studies are executed on a group of satellite systems, and the simulations results verify the effectiveness of the proposed resilient approaches in a time-variant tracking case.

Neural Q-learning for discrete-time nonlinear zero-sum games with adjustable convergence rate.

In this paper, an adjustable Q-learning scheme is developed to solve the discrete-time nonlinear zero-sum game problem, which can accelerate the convergence rate of the iterative Q-function sequence. First, the monotonicity and convergence of the iterative Q-function sequence are analyzed under some conditions. Moreover, by employing neural networks, the model-free tracking control problem can be overcome for zero-sum games. Second, two practical algorithms are designed to guarantee the convergence with accelerated learning. In one algorithm, an adjustable acceleration phase is added to the iteration process of Q-learning, which can be adaptively terminated with convergence guarantee. In another algorithm, a novel acceleration function is developed, which can adjust the relaxation factor to ensure the convergence. Finally, through a simulation example with the practical physical background, the fantastic performance of the developed algorithm is demonstrated with neural networks.

Fast and accurate tracking control of robotic manipulators subject to state constraints and input saturation by effectively integrating planning strategies.

This paper presents a two-loop control framework for robotic manipulator systems subject to state constraints and input saturation, which effectively integrates planning and control strategies. Namely, a stability controller is designed in the inner loop to address uncertainties and nonlinearities; an optimization-based generator is constructed in the outer loop to ensure that state and input constraints are obeyed while concurrently minimizing the convergence time. Furthermore, to dramatically the computational burden, the optimization-based generator in the outer loop is switched to a direct model-based generator when the tracking errors are sufficiently small. In this way, both a high tracking accuracy and fast dynamic response are obtained for constrained robotic manipulator systems with considerably lower computational burden. The superiority and effectiveness of the proposed structure are illustrated through comparative simulations and experiments.

Time-varying gain extended state observer-based adaptive optimal control for disturbed unmanned helicopter.

In this paper, the robust adaptive optimal tracking control problem is addressed for the disturbed unmanned helicopter based on the time-varying gain extended state observer (TVGESO) and adaptive dynamic programming (ADP) methods. Firstly, a novel TVGESO is developed to tackle the unknown disturbance, which can overcome the drawback of initial peaking phenomenon in the traditional linear ESO method. Meanwhile, compared with the nonlinear ESO, the proposed TVGESO possesses easier and rigorous stability analysis process. Subsequently, the optimal tracking control issue for the original unmanned helicopter system is transformed into an optimization stabilization problem. By means of the ADP and neural network techniques, the feedforward controller and optimal feedback controller are skillfully designed. Compared with the conventional backstepping approach, the designed anti-disturbance optimal controller can make the unmanned helicopter accomplish the tracking task with less energy. Finally, simulation comparisons demonstrate the validity of the developed control scheme.

Asymptotical tracking control for the complex network based on the dynamic topology.

A tracking control scheme is proposed for complex dynamic network (CDN), where the CDN is regarded wholly as a dynamic composite system which consists of two mutually coupled subsystems. One subsystem consists of all nodes and the other consists of all links, and consider the weights of the links to be state variables in the latter subsystem. There are two parts in the structure of the proposed tracking control scheme: the designed controller of nodes and the synthesis of the coupling term in links. These two parts can guarantee two subsystems to asymptotically track the given reference targets, respectively. This approach originates from the communication transmission network (CTN). In CTN, for the network optimization management, the reference network topology (NT), such as the star topology, is required as a target for tracking in communication transmission (links) when each node in network needs to track its own target. The control scheme provided of this paper coincides with above requirement. And finally, a comparative simulation example is given for illustrating the effectiveness of the provided control scheme.

A novel stability criterion and precise control for linear time delay systems based on regional configuration of the poles.

This paper presents the concept of region stability and provides criteria for region stability of linear time delay systems, which can reveal the dynamic and steady-state performance of the systems more precisely. Corresponding design schemes for stabilization and tracking control that can accurately control various performance of time delay systems have also been explored. First, in the light of the connection between the poles and the dynamic properties of the system, the concept of region stability is given to describe the finer dynamic behavior of time delay systems. The criteria for the region stability are also presented. Second, the region stabilization methods are investigated, which can ensure that the system satisfies a certain dynamic performance by setting the eigenvalues in a certain convex region. Third, a precise tracking control of the linear time delay systems is addressed as an application of region stabilization. It can control the steady state performance and transient response of the tracking signal more precisely. Finally, three instances are provided to display the superiority of the new method for the performance indexes of the linear time delay systems.

Design and Control of Upper Limb Rehabilitation Training Robot Based on a Magnetorheological Joint Damper.

In recent years, rehabilitation robots have been developed and used in rehabilitation training for patients with hemiplegia. In this paper, a rehabilitation training robot with variable damping is designed to train patients with hemiplegia to recover upper limb function. Firstly, a magnetorheological joint damper (MR joint damper) is designed for the rehabilitation training robot, and its structural design and dynamic model are tested theoretically and experimentally. Secondly, the rehabilitation robot is simplified into a spring-damping system, and the rehabilitation training controller for human movement is designed. The rehabilitation robot dynamically adjusts the excitation current according to the feedback speed and human-machine interaction torque, so that the rehabilitation robot always outputs a stable torque. The magnetorheological joint damper acts as a clutch to transmit torque safely and stably to the robot joint. Finally, the upper limb rehabilitation device is tested. The expected torque is set to 20 N, and the average value of the output expected torque during operation is 20.02 N, and the standard deviation is 0.635 N. The output torque has good stability. A fast (0.5 s) response can be achieved in response to a sudden motor speed change, and the average expected output torque is 20.38 N and the standard deviation is 0.645 N, which can still maintain the stability of the output torque.

Composite RISE control for vehicle-mounted servo system with unknown modeling uncertainties and unknown time-varying disturbances.

In light of the problem of trajectory tracking control in vehicle servo systems with system model uncertainty and external time-varying disturbance, an effective trajectory tracking control method that can handle system model uncertainty and external time-varying disturbances is proposed. To achieve this goal, a novel composite robust integral of the sign of the error (RISE) control method is introduced that combines a multi-layer neural network and an extended state observer. Specifically, multi-layer neural networks are utilized to approximate the uncertainty of the system model, while an extended state observer is employed to estimate the fitting near-error and the external time-varying interference, which are used as feedforward compensation. Finally, the RISE controller is implemented as a robust feedback controller. By applying Lyapunov theory for stability analysis and conducting experiments, the results demonstrate that the proposed approach exhibits excellent performance and robustness in addressing the uncertainties and disturbances involved in trajectory tracking control for vehicle servo systems.

State observer-based Physics-Informed Machine Learning for leader-following tracking control of mobile robot.

In this paper, the novel leader-following tracking control method is proposed for mobile robots, which consists estimation technique of the speed of the leader robot (LR), and a parameter-dependent controller for the follower robot (FR). To estimate the speed of LR, a novel Physics Informed Machine Learning (PIML) is proposed to learn the dynamics of the state observer via the error state model. The dynamics of the state observer in PIML play a significant role for stable learning and state estimation of uncertain models. The gain of the parameter-dependent controller is determined by the convex combination of the robust control technique via the polytopic model. Finally, the tracking performance of the proposed method is verified through the simulation and experiment.

Optimal tracking control of an Autonomous Underwater Vehicle: A PMP approach.

This paper addresses the optimal tracking control problem of an Autonomous Underwater Vehicle (AUV) using Pontryagin's Minimum Principle (PMP). The formulation uses a choice of Hamiltonian function using the desired control objective with AUV dynamics acting as dynamic constraints. We first develop PMP based on AUV kinematics and then extend it to the dynamics to arrive at optimal thrusts and moments. Necessary conditions for optimality are derived for both the models using PMP, which results in optimal trajectories that simultaneously minimize the tracking error as well as the control cost, thereby arriving at energy optimality. It was observed that the adjoint variables (costates) are indeed the momenta in the inertial and body-fixed frames. At the kinematic level, this forms a stable solution. The developed methodology is applied to both 2D and 3D AUV model with disturbances due to inputs and ocean currents. Numerical simulations are carried out with the derived control laws for a given trajectory tracking target. Quantitative evaluation of the performance and comparison of the controller is done using Mean Square Error(MSE) and Total Variation(TV) measures. The proposed control laws are found to achieve the desired control objectives.

Nonlinear dynamic modeling and model-based AI-driven control of a magnetoactive soft continuum robot in a fluidic environment.

In recent years, magnetoactive soft continuum robots (MSCRs) with multimodal locomotion capabilities have emerged for various biomedical applications. Developments in nonlinear dynamic models and effective control methods for MSCRs are deemed vital not only to gain a better understanding of their coupled magneto-mechanical behavior but also to accurately steer the MSCRs inside the human body. This study presents a novel dynamic model and model-based AI-driven control method to guide an MSCR in a fluidic environment. The MSCR is fully exposed to fluid flows at different rates to simulate the biofluidic environment within the body. A novel nonlinear dynamic model considering the effect of damping and drag force attributed to fluidic flows is first developed to accurately and efficiently predict the response of the MSCR under varying magnetic and mechanical loading. Fairly accurate correlations were observed between the theoretical responses based on the developed magneto-viscoelastic model and the experimental data for various scenarios. A novel model-based control algorithm based on a fractional-order sliding surface and deep reinforcement learning algorithm (DRL-FOSMC) is subsequently developed to accurately steer the magnetoactive soft robot on predefined trajectories considering varying fluid flow rates. A fractional-order sliding surface and a compensator, trained using the deep deterministic policy gradient algorithm, are designed to mitigate the amount of chattering and enhance the tracking performance of the closed-loop system. The stability proof of the developed control algorithm is also presented. A hardware-in-the-loop experimental framework has been designed to assess the effectiveness of the proposed control algorithm through various case studies. The performance of the proposed DRL-FOSMC algorithm is rigorously assessed and found to be superior when compared with other control methods.

Event-triggered low-computation adaptive output-feedback fuzzy tracking control of uncertain nonlinear systems.

A fuzzy adaptive tracking control scheme is studied for a family of uncertain systems with immeasurable system states. The controller takes up few computation and transmission resources to achieve prescribed boundaries of the dynamic and steady-state performance indicators. Compared with the existing schemes, the low computational complexity is reflected in the following two points: (1) a fuzzy state observer is introduced, where only the estimation of states are incorporated into the input space of fuzzy logic systems (FLSs). (2) The problem of complexity explosion can be avoided without utilizing additional command filters or auxiliary dynamic surface control techniques. In addition, using the event-triggered control scheme, the data in the transmission is significantly reduced. Finally, the effectiveness of the scheme is fully verified by simulation.

Off-policy two-dimensional reinforcement learning for optimal tracking control of batch processes with network-induced dropout and disturbances.

In this paper, a new off-policy two-dimensional (2D) reinforcement learning approach is proposed to deal with the optimal tracking control (OTC) issue of batch processes with network-induced dropout and disturbances. A dropout 2D augmented Smith predictor is first devised to estimate the present extended state utilizing past data of time and batch orientations. The dropout 2D value function and Q-function are further defined, and their relation is analyzed to meet the optimal performance. On this basis, the dropout 2D Bellman equation is derived according to the principle of the Q-function. For the sake of addressing the dropout 2D OTC problem of batch processes, two algorithms, i.e., the off-line 2D policy iteration algorithm and the off-policy 2D Q-learning algorithm, are presented. The latter method is developed by applying only the input and the estimated state, not the underlying information of the system. Meanwhile, the analysis with regard to the unbiasedness of solutions and convergence is separately given. The effectiveness of the provided methodologies is eventually validated through the application of a simulated case during the filling process.

Distributed estimation and control over mobile sensor networks with jointly connected topology: Event-triggered approach.

This article deals with the problem of distributed event-triggered tracking control in mobile sensor networks (MSNs) with a jointly connected topology (JCT). Two schemes are proposed for linear and Lipschitz nonlinear MSNs to estimate and track a mobile target. The proposed schemes are established using an event-triggered method to avoid continuous exchange of information between sensor nodes. In comparison with the other research under event-triggered communication strategies where states of the target are available, this paper considers that the states of the target are not available and two event-triggered algorithms are established for sensor nodes to estimate and follow the states of the continuous-time targets that can be seen in various real-world applications. Also, the proposed schemes are designed for the JCT with disconnected graphs which means the communication topology of the MSN is not required to be connected for all time instants. By employing the Cauchy convergence criterion and a common Lyapunov function, sufficient conditions are also established to ensure event-based tracking control subject to JCT. The effectiveness of the proposed work is verified by presenting simulation examples.