Adaptive Neural-Network Boundary Control for a Flexible Manipulator With Input Constraints and Model Uncertainties

RBF neural network disturbance observer-based backstepping boundary vibration control for Euler-Bernoulli beam model with input saturation

The main objective of this paper is to address the issue of vibration control for a class of Euler-Bernoulli beam systems that are subject to external disturbances and input saturation. The proposed controller differs from other backstepping methods in that it employs a radial basis function (RBF) neural network to accurately estimate boundary disturbances and incorporates the hyperbolic tangent function to ensure input constraints. The nonlinear partial differential equation (PDE) model is initially derived based on Hamilton's principle to capture the dominant dynamic characteristics of the flexible beam. In the framework of the Lyapunov direct approach, an adaptive RBF neural network-based law is subsequently designed to estimate the state-related boundary disturbances. The backstepping approach is then developed to propose sufficient conditions for ensuring the stability and convergence of closed-loop systems subject to input saturation. Finally, the effectiveness and superiority of the proposed methodology are further demonstrated by comparing the simulation results of constrained backstepping controllers.

Disturbance Rejection Event-Triggered Robust Model Predictive Control for Tracking of Constrained Uncertain Robotic Manipulators

A novel hierarchical control framework combining computed-torque-like control (CTLC) with disturbance-observer-based event-triggered robust model predictive control (DO-ET-RMPC) is proposed for the trajectory tracking control of robotic manipulators with bounded disturbances and state and control input constraints. The CTLC approach is first used to cancel the exact nonlinear dynamics of the original tracking error system to obtain a set of decoupling linear tracking error subsystems, thus reducing the optimization complexity of model predictive control (MPC). The composite DO-ET-RMPC scheme is then developed based on the so-called dual-mode MPC approach to robustly stabilize the tracking error subsystems, which could improve the robustness of MPC and save its computational resources simultaneously. The continuous-time theoretical properties of the DO-ET-RMPC scheme, considering disturbances and state and control input constraints simultaneously, are provided for the first time, including the avoidance of Zeno behavior, robust constraint satisfaction, recursive feasibility, and stability. In the end, the superiorities of the proposed control scheme are verified by the comparative simulations.

Fixed-Time Composite Neural Learning Control of Flexible Telerobotic Systems

This article is devoted to the fixed-time synchronous control for a class of uncertain flexible telerobotic systems. The presence of unknown joint flexible coupling, time-varying system uncertainties, and external disturbances makes the system different from those in the related works. First, the lumped system dynamics uncertainties and external disturbances are estimated successfully by designing a new composite adaptive neural networks (CANNs) learning law skillfully. Moreover, the fast-transient, satisfactory robustness, and high-precision position/force synchronization are also realized by design of fixed-time impedance control strategies. Furthermore, the "complexity explosion" issue triggered by traditional backstepping technology is averted efficiently via a novel fixed-time command filter and filter compensation signals. And then, sufficient conditions of system controller parameters and fixed-time stability are theoretically given by establishing the Lyapunov stability theorem. Besides, the absolute stability of the two-port networked system under complex transmission time delays is rigorously proved. Finally, simulations are performed with 2-link flexible telerobotic systems under two cases, results are presented to realistically verify the proposed control algorithm available.

Disturbance Observer-Based Adaptive Intelligent Control of Marine Vessel With Position and Heading Constraint Condition Related to Desired Output

This article studies the adaptive control about the geodetic fixed positions and heading of three-degree-of-freedom dual-propeller vessel. During the navigation of a vessel at sea, due to the unpredictable sea, on the one hand, it is important to ensure that the vessel can smoothly follow the desired geodesic fixed position and heading; on the other hand, when the sailing environment is harsh, it is even more important that the vessel can adapt to the desired geodesic fixed position and heading that change at any time for safe driving. Therefore, this article selects the time-varying function related to the desired geodesic fixed position and heading as the constraint condition, and the constraint condition will change in real time as the expected position and heading change. The design of the control strategy is difficult, and the designed control strategy will be more suitable for complex maritime navigation conditions. First, the article constructs a log-type barrier Lyapunov function. Second, by introducing an unknown external disturbance observer, the external disturbances caused by the environment that may be encountered during the vessel's voyage can be observed. Then, combined with the backstepping algorithm, a neural network (NN) control strategy and adaptive law are designed. Among them, for the uncertain function in the process of designing the control strategy, the NN is used to approximate it. Furthermore, through the Lyapunov stability analysis, it is shown that applying the designed control strategy to the vessel system in this article can ensure that the system is closed-loop stable. The final simulation experiment shows the effectiveness of the designed control strategy.

Predefined-Time Hierarchical Coordinated Neural Control for Hypersonic Reentry Vehicle

This paper investigates the predefined-time hierarchical coordinated adaptive control on the hypersonic reentry vehicle in presence of low actuator efficiency. In order to compensate for the deficiency of rudder deflection in advantage of channel coupling, the hierarchical design is proposed for coordination of the elevator deflection and aileron deflection. Under the control scheme, the equivalent control law and switching control law are constructed with the predefined-time technology. For the dynamics uncertainty approximation, the composite learning using the tracking error and the prediction error is constructed by designing the serial-parallel estimation model. The closed-loop system stability is analyzed via the Lyapunov approach and the tracking errors are guaranteed to be uniformly ultimately bounded in a predefined time. The tracking performance and the learning accuracy of the proposed algorithm are verified via simulation tests.

Adaptive NN Tracking Control for Uncertain MIMO Nonlinear System With Time-Varying State Constraints and Disturbances

In this article, an adaptive neural network (NN) tracking control scheme is proposed for uncertain multi-input-multi-output (MIMO) nonlinear system in strict-feedback form subject to system uncertainties, time-varying state constraints, and bounded disturbances. The radial basis function NNs (RBFNNs) are adopted to approximate the system uncertainties. By constructing the intermediate variables, the external disturbances that cannot be directly measured are approximated by the disturbance observers. The time-varying barrier Lyapunov function (TVBLF) is constructed to guarantee the boundedness of the errors lie in the sets. To overcome the potential singularity problem that the denominator of the barrier function term approaches zero in controller design, the adaptive NN tracking control scheme with time-varying state constraints is proposed. Based on the TVBLF, the controller will be designed to guarantee tracking performance without violating the appropriate error constraints. The analysis of TVBLF shows that all closed-loop signals remain semiglobally uniformly ultimately bounded (SGUUB). The simulation results are performed to validate the validity of the proposed scheme.

Backstepping Boundary Control for a Class of Gantry Crane Systems

In this article, two boundary feedback controllers are designed via the backstepping approach for a class of gantry crane systems. To provide an accurate and concise representation of the dynamic behavior, the gantry crane with a flexible cable is described by a hybrid system. The hybrid system is formed by an ordinary differential equation coupled with a partial differential equation. In the first control strategy, a backstepping-based boundary state-feedback controller is proposed for the gantry crane to transport a payload to an expected position with less shaking. In the second control strategy, a boundary output-feedback controller is explored with an observer estimating the inaccessible states. By using the backstepping technique and kernel functions, the original systems with different control strategies are transformed into target systems. By using the operator semigroup and Lyapunov stability theories, the target system is proven to be well-posed and exponentially stable, respectively. Finally, numerical simulations and comparisons are provided to illustrate the efficiency and the advantages of the proposed methods.

Output-Feedback Adaptive Neural Network Control for Uncertain Nonsmooth Nonlinear Systems With Input Deadzone and Saturation

Nonsmooth nonlinear systems can model many practical processes with discontinuous property and are difficult to be stabilized by classical control methods like smooth nonlinear systems. This article considers the output-feedback adaptive neural network (NN) control problem for nonsmooth nonlinear systems with input deadzone and saturation. First, the nonsmooth input deadzone and saturation is converted to a smooth function of affine form with bounded estimation error by means of the mean-value theorem. Second, with the help of approximation theorem and Filippov's differential inclusion theory, the given nonsmooth system is converted to an equivalent smooth system model. Then, by introducing a proper logarithmic barrier Lyapunov function (BLF), an output-feedback adaptive NN strategy is set up by constructing an appropriate observer and adopting the adaptive backstepping technique. A new stability criterion is established to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, comparative simulations through Chua's oscillator are offered to verify the effectiveness of the proposed control algorithm.

Construction and management of smart campus: Anti-disturbance control of flexible manipulator based on PDE modeling

With the rapid development of smart campus, this paper studies the attitude tracking control of flexible manipulator (FM) in colleges and universities under elastic vibration and external disturbances. First, different from the traditional modeling based on ordinary differential equations (ODEs), the partial differential equations (PDEs) dynamic model of a manipulator system is established based on the Hamilton principle (HP). Second, the boundary control condition of the end system of the manipulator is introduced to adjust the vibration of the manipulator. Furthermore, a Proportional-Derivative (PD) boundary control (PDBC) strategy is proposed by the Lyapunov function to suppress the vibration of the manipulator. Finally, a numerical comparison simulation based on MATLAB/SIMULINK further verifies the robustness and anti-disturbance performance of the control method proposed in this paper.

Neural network-based adaptive second-order sliding mode control for uncertain manipulator systems with input saturation

In order to solve the trajectory tracking problem for robotic manipulators with dynamic uncertainty, external disturbance and input saturation, a novel second-order sliding mode control scheme based on neural network is proposed in this paper. First of all, a model-based second-order non-singular fast terminal sliding mode controller (SONFTSMC) is designed to overcome the chattering problem under the consideration of uncertain parameters. Then attention is focused on the scenario that all those nonlinear uncertainties are unknown, and a new fuzzy wavelet neural network (FWNN) is designed to estimate those unknown uncertainties via lumping them into one compounded uncertainty. In addition, all parameters in FWNN are adjusted autonomously by using an adaptive method. The proposed second-order non-singular fast terminal sliding mode (SONFTSM) control method not only improves the convergence speed and tracking accuracy of the robotic manipulator, but also enhances its robustness. Finally, the advantages of SONFTSM control strategy over existing sliding mode control methods are verified with comparative simulations.

Active Obstacle Avoidance Trajectory Planning for Vehicles Based on Obstacle Potential Field and MPC in V2P Scenario

V2P (vehicle-to-pedestrian) communication can improve road traffic efficiency, solve traffic congestion, and improve traffic safety. It is an important direction for the development of smart transportation in the future. Existing V2P communication systems are limited to the early warning of vehicles and pedestrians, and do not plan the trajectory of vehicles to achieve active collision avoidance. In order to reduce the adverse effects on vehicle comfort and economy caused by switching the "stop-go" state, this paper uses a PF (particle filter) to preprocess GPS (Global Positioning System) data to solve the problem of poor positioning accuracy. An obstacle avoidance trajectory-planning algorithm that meets the needs of vehicle path planning is proposed, which considers the constraints of the road environment and pedestrian travel. The algorithm improves the obstacle repulsion model of the artificial potential field method, and combines it with the A* algorithm and model predictive control. At the same time, it controls the input and output based on the artificial potential field method and vehicle motion constraints, so as to obtain the planned trajectory of the vehicle's active obstacle avoidance. The test results show that the vehicle trajectory planned by the algorithm is relatively smooth, and the acceleration and steering angle change ranges are small. Based on ensuring safety, stability, and comfort in vehicle driving, this trajectory can effectively prevent collisions between vehicles and pedestrians and improve traffic efficiency.

Optimal Tracking Control of a Nonlinear Multiagent System Using Q-Learning via Event-Triggered Reinforcement Learning

This article offers an optimal control tracking method using an event-triggered technique and the internal reinforcement Q-learning (IrQL) algorithm to address the tracking control issue of unknown nonlinear systems with multiple agents (MASs). Relying on the internal reinforcement reward (IRR) formula, a Q-learning function is calculated, and then the iteration IRQL method is developed. In contrast to mechanisms triggered by time, an event-triggered algorithm reduces the rate of transmission and computational load, since the controller may only be upgraded when the predetermined triggering circumstances are met. In addition, in order to implement the suggested system, a neutral reinforce-critic-actor (RCA) network structure is created that may assess the indices of performance and online learning of the event-triggering mechanism. This strategy is intended to be data-driven without having in-depth knowledge of system dynamics. We must develop the event-triggered weight tuning rule, which only modifies the parameters of the actor neutral network (ANN) in response to triggering cases. In addition, a Lyapunov-based convergence study of the reinforce-critic-actor neutral network (NN) is presented. Lastly, an example demonstrates the accessibility and efficiency of the suggested approach.

Dynamic modeling and infinite-dimensional observer-based control for manipulation of flexible beam by a multi-link robot

This paper concerns an infinite-dimensional observer for manipulation of flexible beam by a rigid arm robot. The complex dynamic of the system is described by distributed parameter model in terms of ordinary differential equations and partial differential equation. A novel infinite-dimensional observer is proposed to estimate the vibration information of the flexible object. In addition, an observer-based independent joint controller is designed to achieve the position control and vibration suppression, which do not need end-point boundary control. The semigroup theory and LaSalle’s invariance principle are adopted to prove the asymptotic stability of the robot system. The efficiency of the observers and the proposed control strategy are demonstrated by numerical simulations.

A Bus-Scheduling Method Based on Multi-Sensor Data Fusion and Payment Authenticity Verification

It is of great significance to ensure public transportation management capabilities by improving urban public transport services. One method is to solve the problems related to the quality of data submitted for public funding as well as the accuracy and transparency of the supervision and review processes; moreover, improving public-transportation-service systems is a viable method to solve such problems. With technological advancements and the application of new technologies such as automatic driving and multiple payment, it has gradually become difficult for user-data verification systems, based on the original single bus payment method, to cater to these new technologies. Diversified payment and complex management methods have highlighted the need for new verification methods. Firstly, in this paper, we constructed the Origin–Destination (OD) model of bus-passenger flows by using real-time transmission of passenger-multiple-payment data, on-board-video passenger flow detection data and vehicle real-time positioning data. On this basis, the bus waybill data of other intelligent bus systems and the wait data of bus stations were integrated, so as to establish the travel chain theory by matching passenger flow and the temporal and spatial distribution model. Then, an OD analysis of public-transport passenger flows could be carried out, with a detailed analysis of vehicle, station and line-passenger flow, and the travel characteristics of public transport passenger flow could be excavated. Then, according to the means-end chain theory, the spatiotemporal distribution of the passenger flow data was obtained to carry out an OD analysis of the passenger flow, so as to perform a refinement analysis of the vehicle, station, and passenger flow. Thereby, the characteristics of the passenger flow were explored. Subsequently, payment-authenticity-verification models were established for the data-validity assessment, video-data analysis, passenger-flow estimation, and early warnings in order to determine the authenticity of the payment data. Lastly, based on the multi-sensor passenger flow data fusion and the data authenticity verification models, combined with the application of new technologies such as the use of autonomous buses, the test was promoted. That is, by taking intelligent bus scheduling as the scenario, the research method was tested and verified with real-time passenger flow data according to historical data. The results showed that the method accurately predicted the passenger flow, and had a positive role in improving the efficiency of payment-data-authenticity verification. The application of the method can enhance the management and service quality of public transportation.

Intelligent Bus Scheduling Control Based on On-Board Bus Controller and Simulated Annealing Genetic Algorithm

The stable and fast service of a bus network is one of the important indicators of the service quality and management level of urban public transport. With the continuous expansion of cities, the bus network complexity has been increasing accordingly. The application of new technologies such as self-driving buses has made the bus network more complex and its vulnerability more obvious. Therefore, how to collect information on passenger flow, traffic flow, and transport distribution using intelligent means, and how to establish an effective intelligent bus scheduling control method have been important questions surrounding the improvement of the level of urban bus operation. To address this challenge, this paper proposes the design method of a bus controller based on data collection and the edge computing requirements of autonomous driving buses; and installs them widely on buses. In addition, an intelligent bus control scheduling method based on the simulated annealing genetic algorithm was developed according to the current scheduling requirements. The proposed method combines the strong local search ability of the simulated annealing algorithm, which prevents the search process from falling into a local optimum, and the strong search ability of the genetic algorithm in the overall search process, leading an intelligent bus control scheduling method based on the simulated annealing genetic algorithm. The proposed method was verified by experiments on the optimal scheduling of multi-destination public transport as an example, we verified the research method, and finally, simulated it using historical data. There is good model prediction of the experimental results. Therefore, the intelligent traffic control can be realized through efficient bus scheduling, thus improving the robustness of the bus network operation.

Development of Diagnosis Model for Early Lung Nodules Based on a Seven Autoantibodies Panel and Imaging Features

Background

There is increasing incidence of pulmonary nodules due to the promotion and popularization of low-dose computed tomography (LDCT) screening for potential populations with suspected lung cancer. However, a high rate of false-positive and concern of radiation-related cancer risk of repeated CT scanning remains a major obstacle to its wide application. Here, we aimed to investigate the clinical value of a non-invasive and simple test, named the seven autoantibodies (7-AABs) assay (P53, PGP9.5, SOX2, GAGE7, GUB4-5, MAGEA1, and CAGE), in distinguishing malignant pulmonary diseases from benign ones in routine clinical practice, and construct a neural network diagnostic model with the development of machine learning methods.

Method

A total of 933 patients with lung diseases and 744 with lung nodules were identified. The serum levels of the 7-AABs were tested by an enzyme-linked Immunosorbent assay (ELISA). The primary goal was to assess the sensitivity and specificity of the 7-AABs panel in the detection of lung cancer. ROC curves were used to estimate the diagnosis potential of the 7-AABs in different groups. Next, we constructed a machine learning model based on the 7-AABs and imaging features to evaluate the diagnostic efficacy in lung nodules.

Results

The serum levels of all 7-AABs in the malignant lung diseases group were significantly higher than that in the benign group. The sensitivity and specificity of the 7-AABs panel test were 60.7% and 81.5% in the whole group, and 59.7% and 81.1% in cases with early lung nodules. Comparing to the 7-AABs panel test alone, the neural network model improved the AUC from 0.748 to 0.96 in patients with pulmonary nodules.

Conclusion

The 7-AABs panel may be a promising method for early detection of lung cancer, and we constructed a new diagnostic model with better efficiency to distinguish malignant lung nodules from benign nodules which could be used in clinical practice.

An Improved Proximal Policy Optimization Method for Low-Level Control of a Quadrotor

In this paper, a novel deep reinforcement learning algorithm based on Proximal Policy Optimization (PPO) is proposed to achieve the fixed point flight control of a quadrotor. The attitude and position information of the quadrotor is directly mapped to the PWM signals of the four rotors through neural network control. To constrain the size of policy updates, a PPO algorithm based on Monte Carlo approximations is proposed to achieve the optimal penalty coefficient. A policy optimization method with a penalized point probability distance can provide the diversity of policy by performing each policy update. The new proxy objective function is introduced into the actor–critic network, which solves the problem of PPO falling into local optimization. Moreover, a compound reward function is presented to accelerate the gradient algorithm along the policy update direction by analyzing various states that the quadrotor may encounter in the flight, which improves the learning efficiency of the network. The simulation tests the generalization ability of the offline policy by changing the wing length and payload of the quadrotor. Compared with the PPO method, the proposed method has higher learning efficiency and better robustness.

Multi-Agent Reinforcement Learning with Optimal Equivalent Action of Neighborhood

In a multi-agent system, the complex interaction among agents is one of the difficulties in making the optimal decision. This paper proposes a new action value function and a learning mechanism based on the optimal equivalent action of the neighborhood (OEAN) of a multi-agent system, in order to obtain the optimal decision from the agents. In the new Q-value function, the OEAN is used to depict the equivalent interaction between the current agent and the others. To deal with the non-stationary environment when agents act, the OEAN of the current agent is inferred simultaneously by the maximum a posteriori based on the hidden Markov random field model. The convergence property of the proposed methodology proved that the Q-value function can approach the global Nash equilibrium value using the iteration mechanism. The effectiveness of the method is verified by the case study of the top-coal caving. The experiment results show that the OEAN can reduce the complexity of the agents’ interaction description, meanwhile, the top-coal caving performance can be improved significantly.

Toward Optimal Control of a Multivariable Magnetic Levitation System

In the paper, a comparative case study covering different control strategies of unstable and nonlinear magnetic levitation process is investigated. Three control procedures are examined in order to fulfill the specified performance indices. Thus, a dedicated PD regulator along with the hybrid fuzzy logic PID one as well as feed-forward neural network regulator are respected and summarized according to generally understood tuning techniques. It should be emphasized that the second PID controller is strictly derived from both arbitrary chosen membership functions and those ones selected through the genetic algorithm mechanism. Simulation examples have successfully confirmed the correctness of obtained results, especially in terms of entire control process quality of the magnetic levitation system. It has been observed that the artificial-intelligence-originated approaches have outperformed the classical one in the context of control accuracy and control speed properties in contrary to the energy-saving behavior whereby the conventional method has become a leader. The feature-related compromise, which has never been seen before, along with other crucial peculiarities, is effectively discussed within this paper.

Finite-time stabilization and H∞ control of Port-controlled Hamiltonian systems with disturbances and saturation

The finite-time stabilization and finite-time H_∞ control problems of Port-controlled Hamiltonian (PCH) systems with disturbances and input saturation (IS) are studied in this paper. First, by designing an appropriate output feedback, a strictly dissipative PCH system is obtained and finite-time stabilization result for nominal system is given. Second, with the help of the Hamilton function method and truncation inequality technique, a novel output feedback controller is developed to make the PCH system finite-time stable when IS occurs. Further, a finite-time H_∞ controller is designed to attenuate disturbances for PCH systems with IS, and sufficient conditions are presented. Finally, a numerical example and a circuit example are given to reveal the feasibility of the obtained theoretical results.

The Boundary Proportion Differential Control Method of Micro-Deformable Manipulator with Compensator Based on Partial Differential Equation Dynamic Model

It is challenging to accurately judge the actual end position of the manipulator—regarded as a rigid body—due to the influence of micro-deformation. Its precise and efficient control is a crucial problem. To solve the problem, the Hamilton principle was used to establish the partial differential equation (PDE) dynamic model of the manipulator system based on the infinite dimension of the working environment interference and the manipulator space. Hence, it resolves the common overflow instability problem in the micro-deformable manipulator system modeling. Furthermore, an infinite-dimensional radial basis function neural network compensator suitable for the dynamic model was proposed to compensate for boundary and uncertain external interference. Based on this compensation method, a distributed boundary proportional differential control method was designed to improve control accuracy and speed. The effectiveness of the proposed model and method was verified by theoretical analysis, numerical simulation, and experimental verification. The results show that the proposed method can effectively improve the response speed while ensuring accuracy.