Uncertainties In Dynamic Model Research Articles

Transient acceleration waveform replication of electro-hydraulic systems using convex combined adaptive controller with input shaping technique

Electro-hydraulic systems are extensively utilized to generate desired acceleration waveforms to provide a vibration environment for testing the performance and reliability of objects in various industrial applications. However, as electro-hydraulic systems are often affected by some inevitable drawbacks resulted from hydraulic nonlinearities, unwanted dynamic variations and disturbances, the generated acceleration waveform is generally far behind the expectation. In this paper, a convex combined adaptive controller with input shaping technique is proposed for enhancing the transient acceleration waveform replication accuracy of electro-hydraulic systems. The proposed controller is comprised of a three variable controller at the bottom level, an input shaping technique controller at the middle level, and a convex combined adaptive controller at the upper level. The three variable controller is firstly utilized for the establishment of a fundamental closed-loop acceleration control system, and then the input shaping technique controller is constructed by introducing an offline designed inverse prefilter utilizing the multi-innovation recursive least squares algorithm and the zero magnitude error tracking algorithm. The convex combined adaptive controller at the upper level is comprised of two individual adaptive filters with high and low step sizes, which provides the merits of fast convergence rate and high tracking accuracy, and it is further exploited to address for system’s dynamic variations, model uncertainties and unexpected perturbations. Comparative experiments of the proposed controller with a manually generated random waveform and a recorded earthquake waveform as the testing inputs are conducted on a typical electro-hydraulic test bench, and the corresponding results demonstrate the feasibility and superiority of the proposed controller in improving the transient acceleration waveform replication performance of electro-hydraulic systems.

Hierarchical temporal sequence convergence in prescribed-time control for quadrotor UAVs under unknown dynamic disturbances

This paper studies the trajectory tracking control problem for quadrotor unmanned aerial vehicles (UAVs) under unknown disturbances and model uncertainties. A priori information about disturbances is required for most existing prescribed time-extended state observer (PTESO) methods. For unknown disturbances, a barrier function-based adaptive prescribed-time extended state observer (BFAPTESO) is designed by utilizing the time-domain transformation and adaptive mechanism in the prescribed time and using the barrier function method in the rest time. To improve tracking performance amidst model uncertainty, a finite-gain prescribed-time state observer (FGPTSO) is applied in the position loop. Then, the novel prescribed-time controllers are designed in conjunction with the above observers in the attitude and position loops. The proposed control scheme mitigates unknown dynamic disturbances and model uncertainties, guaranteeing that the tracking error achieves and sustains the preset accuracy within the prescribed time. Finally, numerical results indicate that the proposed control method is effective and robust.

Nonlinear Robust Adaptive Control of Universal Manipulators Based on Desired Trajectory

The introduction of a dynamic model in robot trajectory tracking control design can significantly improve its trajectory tracking accuracy, but there are many uncertainties in the robot dynamic model which can be dealt with through robust control and adaptive control. The prevailing robust control as well as adaptive control methods require real-time computation of robot dynamics, but the extreme complexity of the robot dynamics equations makes it difficult to apply these methods in real industrial systems. To this end, this article proposes a robust adaptive control method based on the desired trajectory, which uses the desired trajectory to compute most of the control terms offline, including the robot’s nominal dynamics and regression matrices, and substantially reduces the need for real-time computation of the feedback signals. The robust term modifies the perturbation of the inertial parameters of the links, the adaptive term learns the friction coefficients of the joints online, and an additional compensation term is designed to satisfy the Lyapunov stability condition of the system. Finally, taking a universal manipulator as the experimental platform, the control performances of different control methods are compared to show the feasibility of the controller and the effective reduction in real-time computational complexity.

Adaptive Neural Network Tracking Control of Robotic Manipulators Based on Disturbance Observer

This article presents an adaptive neural network (ANN) control scheme based on a disturbance observer that can achieve trajectory tracking control of robotic manipulators under external disturbances and dynamic model uncertainties. Firstly, an ANN controller based on full-state feedback is derived using the backstepping technique to achieve an online approximation of uncertainty. The integral sliding mode surface with a position error is introduced into the controller, which reduces the steady-state error of the system and enhances robustness. Then, a novel disturbance observer is designed to estimate both the approximation errors of the ANN and external disturbances, and to provide compensation for the controller, effectively suppressing the trajectory tracking errors caused by approximation errors and disturbances. Subsequently, the Lyapunov stability theory is utilized to demonstrate the stability of the developed control strategy and the boundedness of all closed-loop signals. Finally, numerical simulations are used to confirm the efficacy of the proposed control method.

Adaptive fuzzy neural network finite‐time command filtered control of n‐link robotic systems with actuator saturation

AbstractThe finite time tracking control of n‐link robotic system is studied for model uncertainties and actuator saturation. Firstly, a smooth function and adaptive fuzzy neural network online learning algorithm are designed to address the actuator saturation and dynamic model uncertainties. Secondly, a new finite‐time command filtered technique is proposed to filter the virtual control signal. The improved error compensation signal can reduce the impact of filtering errors, and the tracking errors of system quickly converge to a smaller compact set within finite time. Finally, adaptive fuzzy neural network finite‐time command filtered control achieves finite‐time stability through Lyapunov stability criterion. Simulation results verify the effectiveness of the proposed control.

A Deep Reinforcement Learning-Based Path-Following Control Scheme for an Uncertain Under-Actuated Autonomous Marine Vehicle

In this article, a deep reinforcement learning-based path-following control scheme is established for an under-actuated autonomous marine vehicle (AMV) in the presence of model uncertainties and unknown marine environment disturbances is presented. By virtue of light-of-sight guidance, a surge-heading joint guidance method is developed within the kinematic level, thereby enabling the AMV to follow the desired path accurately. Within the dynamic level, model uncertainties and time-varying environment disturbances are taken into account, and the reinforcement learning control method using the twin-delay deep deterministic policy gradient (TD3) is developed for the under-actuated vehicle, where path-following actions are generated via the state space and hybrid rewards. Additionally, actor-critic networks are developed using the long-short time memory (LSTM) network, and the vehicle can successfully make a decision by the aid of historical states, thus enhancing the convergence rate of dynamic controllers. Simulation results and comprehensive comparisons on a prototype AMV demonstrate the remarkable effectiveness and superiority of the proposed LSTM-TD3-based path-following control scheme.

Robust MPC-based trajectory tracking of autonomous underwater vehicles with model uncertainty

A robust model predictive control (MPC) method with dual closed-loops is presented to handle trajectory tracking of autonomous underwater vehicle (AUV) with uncertain model parameters and random external perturbations. First, constraint conditions are set for the motion state and control input of the underwater vehicle based on its motion characteristics. The position controller takes the velocity increment as input, thus providing a smoothly varying desired velocity for the velocity controller. The velocity controller comprises nominal MPC and a nonlinear auxiliary control law to overcome the effect of random perturbations on AUV tracking control. Then, a finite-time extended state observer (FTESO) is designed to compensate for dynamic model uncertainty. Furthermore, Lyapunov stability theory is employed to analyse the stability of the controller and FTESO. Ultimately, through comparative simulation experiments, the proposed control framework's effectiveness and robustness are verified, proving it to be a feasible AUV trajectory tracking control method.

Research on Monte Carlo simulation of CAT III auto-landing system

Approach/Landing is the phase of most accidents occur to endanger civil aircraft safety. CAT III autoland operation can improve the safety and reliability of aircraft when approach and landing in bad weather conditions, and reduce pilot’s working loads, so CAT III autoland control law design is an important task in advanced flight control system research. However, the most of the related researches are requirement based rather and scenario based. Firstly, the article analyzes the Monte Carlo simulation requirements based on airworthiness requirements of auto-landing system and proposes a Monte Carlo simulation model scheme considering the uncertainty of aircraft dynamic model and the actuator model, the error of sensors and the disturbance of wind, then implements the simulation based on a desktop simulation platform. The simulation result shows that the designed auto-landing system can assure the safe and stable landing of the aircraft on the condition of all kinds of uncertainty, the touchdown point, the vertical landing speed and the attitude are all compliance with the expectation criteria, satisfying the requirement of airworthiness items.

Automatic Detection of Transformer Health Based on Bayesian Network Model

Abstract In order to effectively reduce the increasing operation and maintenance costs of aging power systems and infrastructure, the authors propose a real-time monitoring method of transformer operation state based on dynamic Bayesian network modeling and prediction uncertainty. The transformer fault mode, fault mechanism, different standards and codes, as well as the current transformer operation status are converted into component status, and then these statuses are transmitted to the real-time monitoring system of transformer operation status, the overall risk probability of the transformer or the subsystem risk probability of focus can be calculated according to the Bayesian network, and the elements in the transformer that may cause system failure or have operational risk can be supplemented through appropriate data processing and interpretation. In addition, on the basis of Bayesian network framework, continuous time steps can be added for continuous real-time monitoring of operation status, and a real-time monitoring system of transformer operation status based on dynamic Bayesian network can be built.

Dynamic surface event-triggered control for unmanned sailboat path following based on adaptive tanh line-of-sight

In this article, a dynamic surface control algorithm based on the adaptive tanh line-of-sight with speed adjustment is proposed to solve the problem of path-following control of unmanned sailboat with event-triggered input, considering the dynamic model uncertainty and unknown external disturbance. First, the adaptive tanh line-of-sight method is used to improve the following accuracy and robustness, then an extended state observer is used to estimate the external disturbance and the uncertainty of the internal model, the heading error and the yaw angular velocity error are considered to design the recursive sliding mode dynamic surface controller, at the same time, the speed of the unmanned sailboat is constrained to improve its stability. In addition, to reduce the action frequency of the steering gear, the relative threshold event-triggered control is applied to the controller. According to the Lyapunov stability analysis, it is proved that the proposed control scheme can ensure that all errors in the closed-loop system are semi-globally consistent and ultimately bounded, while the Zeno behavior can be avoided. Finally, the effectiveness of the proposed scheme is verified by simulation and comparative experiments.

An adaptive neural network with nonlinear FOPID design of underwater robotic vehicle in the presence of disturbances, uncertainty, and obstacles

An adaptive neural network with nonlinear fractional-order PID (ANNFOPID) controller design is proposed for underwater robotic vehicle (URV) to solve the path tracking problem. The path tracking problem is caused by disturbances and unknown uncertainties of the underwater vehicle dynamic model. The disturbances were presented with the URV model to evaluate the performance of the ANNFOPID controller to reject the disturbances. At the same time, an obstacle avoidance model has been presented with ANNFOPID controller to evaluate the controller ability to maneuver around different obstacle locations. A radial base function (RBF) has been used to estimate both of the disturbances and the unknown uncertainties of the dynamic model. An improved slime mould algorithm (ISMA) is presented to invent a new trajectories for the underwater vehicle and enhance the ability of the URV to overcome the obstacles problems. At the end, the results obtained show that the ANNFOPID controller present an outstanding performance in comparison with other existing works to overcome the disturbances, unknown uncertainties, and obstacles problems effectively.

Cooperative trajectory tracking control of MUSVs with periodic relative threshold event-triggered mechanism and safe distance

This paper addresses the problem of cooperative trajectory tracking control problem for multiple unmanned surface vehicles (MUSVs) with event-triggered mechanism and safe distance, proposes a novel event-triggered adaptive sliding mode control strategy via nonlinear tracking differentiators (NTD) and periodic relative threshold event-triggered mechanism (PRTETM). Firstly, the formation autonomous navigation system is constructed by multiple leader–follower cooperative structure to establish via the synchronization scheme that guarantee the safe distance each others. Next, a novel adaptive sliding mode control with the square root form of position error is introduced to prevent the oscillation or divergence of the system caused by the larger position error. Then, a concise adaptive control strategy to compensate for dynamic model uncertainties and external environmental disturbances. Meanwhile, the NTD is used to quickly track the differential signal and achieve filter performance. Besides, to obtain smaller update frequency and less energy consumption, the PRTETM without Zero behavior and input saturation are introduced to facilitate the triggering of control inputs and avoid the actuator physical constraints. Theoretical analysis proves that the proposed cooperative control system is uniformly ultimately bounded. Finally, numerical simulations and comparisons consolidate that the effectiveness of the above control scheme and superior in the fixed threshold scheme.

Robust Saturated Formation Tracking Control of Multiple Quadrotors With Switching Communication Topologies

This article deals with distributed formation control for a set of quadrotors subject simultaneously to external disturbances and switching communication topologies. The proposed robust controller incorporates position and attitude control laws for each quadrotor. Both control laws have a nominal part for trajectory tracking and a robust part that compensates for the dynamic model uncertainties and time-varying external disturbances. The devised controller guarantees that the thrust force is bounded with respect to the position error by utilizing saturation functions. The directed communication topology among the quadrotors is considered to be switchable. The formation control performance is guaranteed under switching topologies, and it is demonstrated that all error terms, in closed-loop, converge to a ball centered at the origin with an arbitrarily small radius, achieving uniformly ultimate boundedness. Finally, the performance of the proposed strategy is validated through numerous simulation and experimental examples.

Data-driven fractional order phase-lead and proportional–integral feedback control strategy with application to a reluctance-actuated compliant micropositioning system

This paper aims to overcome complex dynamic problems such as low damping resonant frequency, resonant point drift, and uncertainty of dynamic model of the large stroke micropositioning stage, based on Maxwell reluctance actuator. A composite control method of data-driven fractional phase-lead proportional-integral feedback controller with series hysteresis compensator is proposed. Firstly, the working principle of the Maxwell reluctance actuator is briefly explained, a large stroke flexure micropositioning system is built, and its motor constant and stiffness characteristics are experimentally measured. Secondly, aiming at the rapid phase drop caused by low damping resonance, a data-driven fractional order phase-lead and proportional-integral feedback controller is designed, and the parameters of the fractional order feedback controller are experimentally adjusted based on the iterative feedback tuning algorithm. And the intrinsic hysteresis nonlinearity of the reluctance actuator is firstly compensated by a Prandtl–Ishlinskii model hysteresis compensator which is always placed in the feedback loop of the control system. Then, the proposed controller is compared with other controllers. The experimental results show that this method has a significant suppression effect on high-frequency noise, and the tracking error is significantly reduced, which proves the effectiveness of this stage and control method. • Based on the Maxwell reluctance actuator, a large stroke flexure micropositioning system is built. • A fractional order phase-lead proportional integral controller is proposed, and the integer order approximation of the controller is completed by using oustaloup approximation method • The data-driven method of iterative feedback tuning is used to obtain the parameters of the fractional order controller. • Through the analysis and comparison of linearity, sensitivity function and open-loop frequency response, the advantages of the proposed controller are fully proved.

Adaptive Second-Order Fast Terminal Sliding-Mode Formation Control for Unmanned Surface Vehicles

The formation control of unmanned surface vehicles (USVs) while considering communication topology, dynamic model uncertainties, environmental disturbances, and a fast convergence rate is addressed in this paper. First, graph theory is introduced to describe the connective relationships and establish generalized formation errors among USVs. Then, a second-order fast nonsingular terminal sliding-mode control (SOFNTSMC) is designed to guarantee that the system converges quickly and without chatter. An adaptive update law is designed in order to estimate the model uncertainties and external disturbances without the requirement of the boundary information of the system uncertainties. With the application of the adaptive SOFNTSMC (ASOFNTSMC) and graph theory, a distributed control is developed for every USV to perform the desired formation pattern. Finally, the results of simulations and comparisons demonstrate the effectiveness of the proposed method.

Reinforcement Learning Based Underwater Wireless Optical Communication Alignment for Autonomous Underwater Vehicles

With the developments in underwater wireless optical communication (UWOC) technology, UWOC can be used in conjunction with autonomous underwater vehicles (AUVs) for high-speed data sharing among the vehicle formation during underwater exploration. A beam alignment problem arises during communication due to the transmission range, external disturbances and noise, and uncertainties in the AUV dynamic model. In this article, we propose an acoustic navigation method to guide the alignment process without requiring beam directors, light intensity sensors, and/or scanning algorithms as used in previous research. The AUVs need stably maintain a specific relative position and orientation for establishing an optical link. We model the alignment problem as a partially observable Markov decision process (POMDP) that takes manipulation, navigation, and energy consumption of underwater vehicles into account. However, finding an efficient policy for the POMDP under high partial observability and environmental variability is challenging. Therefore, for successful policy optimization, we utilize the soft actor–critic reinforcement learning algorithm together with AUV-specific belief updates and reward shaping based curriculum learning. Our approach outperformed baseline approaches in a simulation environment and successfully performed the beam alignment process from one AUV to another on the real AUV Tri-TON 2.

RBFNN and projection operator based robust adaptive control with input saturation of a quadrotor equipped with two manipulators constrained to a high voltage line

The aim of this paper is to propose a robust adaptive control for a quadrotor equipped with two manipulators constrained along a high voltage line (HVL). Due to the urgent need of the HVL to inspect, the quadrotor compared to humans has many advantages such as cost and less time, higher accuracy, access to impassable points, and most importantly, reducing human victims. In this paper, two manipulators are used to hang the quadrotor from the HVL and move along it such that the quadrotor can perform monitoring operations more accurately. The motion equations are derived for the constrained quadrotor by the Lagrangian method. A control law is derived based on inverse dynamics of reduced-form equations. A robust adaptive controller is proposed for control of the constrained quadrotor in the presence of dynamic uncertainties and external disturbances. The nonlinear terms in the dynamic model are approximated by radial basis function neural network (RBFNN) and adaptive laws based on the projection operator. Moreover, saturation functions are considered for inputs in the controller by an auxiliary system. Stability analysis of the closed-loop system is performed by the Lyapunov theory. In order to confirm the effectiveness of the proposed method, some numerical simulations are carried out. The simulation results show the quadrotor can move along the HVL in the presence of the dynamic model uncertainties and the external disturbances and satisfy the considered constraints.

Workspace analysis and motion control strategy of robotic mine anchor drilling truck manipulator based on the WOA-FOPID algorithm

The manipulator is the key component of the anchor drilling robot to automatically complete the anchoring operation underground. Due to the complexity of its motion equation and the limitations of its control strategy, the real-time pose and the positioning accuracy of the manipulator are inferior, which seriously restricts the safety, efficiency, and speed of roadway excavation. In order to improve the positioning accuracy and realize the optimal efficiency of the manipulator, this article designs a manipulator structure with four degrees of freedom. With the help of the D-H method and the intelligent parameter setting method, this article carries out the basic theoretical research on the kinematics and the fractional order FOPID control algorithm of the manipulator of the mining roof bolter, and formulates a manipulator motion control strategy. At the same time, combined with numerical simulations and field experiments, we explore the robustness and control efficiency of the hydraulic system of the manipulator under the working conditions of a harsh environment and limited space, and reveal that the intelligent optimization algorithm can control the motion state of the manipulator more accurately and stably after the parameters of the fractional order FOPID controller are positively determined. This study effectively solved the dynamic model uncertainty caused by time-varying internal parameters and external loads of the hydraulic servo system, optimized and reconstructed the structure and motion coefficient parameters of the manipulator, and revealed the control mechanism of a precise spatial positioning and online trajectory planning of the hydraulic servo system of the manipulator. Compared with the traditional PID control algorithm, this algorithm has a faster response speed and better expected track tracking ability. This research lays a theoretical foundation for the precise positioning and automatic support of the manipulator, and also provides a reference for the design of similar motion control algorithms.

Trigger-Based K-Band Microwave Ranging System Thermal Control with Model-Free Learning Process

Micron-level accuracy K-band microwave ranging in space relies on the stability of the payload thermal control on-board; however, large quantities of thermal sensors and heating devices around the deployed instruments consume the precious inner communication resources of the central computer. Another problem arises, which is that the payload thermal protection environment can deteriorate gradually through years operating. In this paper, a new trigger-based thermal system controller design is proposed, with consideration of spaceborne communication burden reduction and actuator saturation, which guarantees stable temperature fluctuations of microwave payloads in space missions. The controller combines a nominal constant sampling PID inner loop and a trigger-based outer loop structure under constraints of heating device saturation. Moreover, an iterative model-free reinforcement learning process is adopted that can approximate the estimation of thermal dynamic modeling uncertainty online. Via extensive experiment in a laboratory environment, the performance of the proposed trigger thermal control is verified, with smaller temperature fluctuations compared to the nominal control, and obvious efficiency in system communications. The online learning algorithm is also tested with deliberate thermal conditions that deviate from the original system—the results can quickly converge to normal when the thermal disturbance is removed. Finally, the ranging accuracy is tested for the whole system, and a 25% (RMS) performance improvement can be realized by using a trigger-based control strategy—about 2.2 µm, compared to the nominal control method.

Robust and Adaptive Sampled-Data Control of Twisted and Coiled Artificial Muscles

Twisted and Coiled Artificial Muscles (TCAMs) are lightweight actuators providing high power/weight ratio, and can substitute heavy electromagnetic motors and pneumatic artificial muscles in applications that require low weight and high contractile work. In this letter we present a robust and adaptive output feedback control strategy for electro-thermally actuated TCAMs. The controller adjusts the voltage applied to the TCAM in order to match desired muscles contraction/expansion. The proposed controller compensates for disturbances and uncertainties in the TCAM's dynamic model. The robustness and stability analysis of the controller takes into account a digital implementation of the control algorithms. Performance are derived in terms of the sampling time of the CPU and the sensors available. The efficacy of the controller is validated through experimental tests.