Unknown Nonlinear Disturbances Research Articles

Neural adaptive dynamic surface control for position tracking of electrohydraulic servo systems based on extended state observer

High-performance motion tracking of electro-hydraulic servo systems plays an important role in developing industrial technology. However, the system itself has parameter uncertainty, uncertain nonlinearity, and measurement noise, significantly decreasing the performance of conventional controllers designed based on nominal models. To improve the tracking performance of electro-hydraulic servo systems, an adaptive dynamic surface control method combining extended state observer and radial basis function neural network is proposed. Firstly, an extended state observer combining neural network output and parameter adaptation technology is designed without the need for speed and pressure feedback to observe the system’s unknown state variables and nonlinear matched disturbance. The design of neural networks is used to approximate mismatched disturbance with significant nonlinearity online. Then, the proposed extended state observer is introduced into the neural adaptive backstepping design, effectively compensating for simultaneous matched and mismatched disturbances, thereby suppressing most of the uncertain nonlinearity of the system. Meanwhile, online update of uncertain parameters further improves the tracking performance of the controller. In addition, the introduction of dynamic surface overcomes the “explosion of complexity” issue in backstepping design. The rigorous stability of the closed-loop system is proved by the Lyapunov stability theory. Finally, the effectiveness of the proposed controller is validated through comparative experiments on valve-controlled hydraulic cylinder.

Composite control based on FNTSMC and adaptive neural network for PMSM system

In this paper, a novel fixed-time non-singular terminal sliding mode control (NFNTSMC) method with an adaptive neural network (ANN) is proposed for permanent magnet synchronous motor (PMSM) system to improve PMSM performance. For nominal PMSM system without disturbance, a novel fixed-time non-singular terminal sliding mode control is designed to achieve fixed-time convergence property to improve the dynamic performance of the system. However, parameters mismatch and external load disturbances generally exist in PMSM system, the controller designed by NFNTSMC requires a large switching gain to ensure the robustness of the system, which will cause high-frequency sliding mode chattering. Therefore, an adaptive radial basis function (RBF) neural network is designed to approximate the unknown nonlinear lumped disturbance including parameters mismatch and external load disturbances online, and then the output of the neural network can be compensated to the NFNTSMC controller to reduce the switching gain and sliding mode chattering. Finally, the fixed-time convergence property and stability of the system are proved by Lyapunov method. The simulation and experimental results show that the presented strategy possesses satisfactory dynamic performance and strong robustness for PMSM system. And the proposed control scheme also provides an effective and systematic idea of the controller design for PMSM.

Command filter-based event-triggered control for stochastic MEMS gyroscopes with finite-time prescribed performance

This paper proposes an adaptive neural control strategy for stochastic microelectromechanical system (MEMS) gyroscopes, aiming to achieve a prescribed performance in a finite time. The radial basis function neural network is introduced to address the system’s unknown nonlinear dynamics and stochastic disturbances. Then, the technology of finite-time prescribed performance function, along with the method of command-filtered backstepping design, is utilized to ensure both transient and steady-state performance and simultaneously solve the problem of "explosion of complexity." Moreover, a switching threshold event-triggered control law is proposed to cut down on communication resources and eliminate corresponding parametric inequality restrictions. The proposed adaptive state feedback control strategy is able to guarantee that the output tracking error converges to a prescribed, arbitrarily small residual set. Additionally, the closed-loop system’s signals can be semi-globally ultimately uniformly bounded in probability. Finally, numerical simulations demonstrate the effectiveness and superiority of the proposed strategy.

Pitch angle control of wind turbines using model-free auto-tuned fractional order proportional derivative ATFOPD controller

The nonlinear dynamics of wind power plants, in addition to uncertainties such as unknown nonlinear disturbances and parameter variations make the design of a robust controller a difficult task. This paper proposes a robust fractional-order controller for the pitch angle control of a variable-speed wind turbine system. An auto-tuned fractional order proportional-derivative controller ATFOPD is designed taking the objectives of reducing pitch actuator fatigue, limiting output mechanical power and speed at their rated values above the rated wind speed. The phase, phase slope and modulus of the pitch actuator process at the fixed gain crossover frequency are needed in order to tune the fractional order controller parameters. These values are usually derived using a mathematical model of the system, such as a transfer function. A model-free auto-tuning technique that can calculate the controller's parameters from measured variables is used in this work in order to design a robust FOPD controller. Simulation results of the ATFOPD controlled system are compared to integer order proportional-derivative IOPD and IOPID controllers, which show that the ATFOPD controller is more effective than the other controllers and its robustness is verified in the presence of disturbances in addition to system parameters and wind speed variations.

An adaptive internal model control approach for unmanned surface vehicle based on bidirectional long short-term memory neural network: Implementation and field testing

This paper investigates a practical adaptive internal model control (IMC) for an unmanned surface vehicle (USV) with unknown time-varying nonlinear model parameters and environmental disturbances. Firstly, an internal model of the USV is presented using the Bidirectional Long Short-Term Memory (BiLSTM) neural network. Then, an adaptive neural IMC controller is designed for the trajectory tracking of USV by using the IMC method. The internal model and the controller are updated by an error threshold algorithm. The proposed control scheme comprises of a trajectory guidance module via the Line-of-Sight (LOS) guidance method and a tracking control module designed by IMC theory. Under the proposed control scheme, the development processes of the vehicle platform and the control algorithms are described, and accurate tracking control can be achieved. Finally, the results of simulation and field experiments are presented and discussed to validate the effectiveness of the proposed control scheme.

Cooperative Maritime Search of Multi-Ship Based on Improved Robust Line-of-Sight Guidance

In this paper, an improved robust line-of-sight (RLOS) guidance-based fuzzy sliding mode controller is presented to control underactuated ships to conduct the cooperative maritime search operation under the presented improved creeping line search method. First, considering that the ship cannot perform turning with corners, an improved creeping line search method is presented by integrating the Bezier method into the traditional creeping line search method to smooth the transition points with corners and employing the cubic spline interpolation method to generate continuous reference paths. Second, an improved RLOS guidance method is presented for the first time by exploring the idea of robust adaptive control to mitigate the chattering effect of the RLOS guidance. Third, the fuzzy logic system with approximate ability is integrated into the design of sliding mode controller to handle unknown nonlinear model dynamics and environmental disturbances. Finally, an improved RLOS guidance-based fuzzy sliding mode controller is presented. The closed-loop stability is guaranteed by the Lyapunov theorem. Comparative simulations are conducted to illustrate the advantages and verify the effectiveness of the presented method.

An adaptive dynamic programming method for observer‐based sliding mode control of connected vehicles subject to deception attacks

AbstractThis article investigates the problem of optimal observer‐based sliding mode control (SMC) of connected vehicles subject to deception attacks and disturbances with adaptive dynamic programming (ADP) method. For a group of vehicles with unknown nonlinear dynamics term and disturbance, this article aims to give a control methodology to achieve secure tracking of the desired spacing, velocity and acceleration. A neural network (NN) and an observer are constructed to estimate the unknown nonlinear term and the states, respectively. Then, a SMC scheme incorporating NN approximation is developed and an off‐policy ADP method is used to implement the optimal control of sliding mode dynamics. The proposed method can ensure individual stability and string stability of the set of vehicles. Numerical simulations are conducted to demonstrate the validity of the proposed controller.

Fixed-Time Distributed Time-Varying Optimization for Nonlinear Fractional-Order Multiagent Systems with Unbalanced Digraphs

This paper investigates the problem of fixed-time distributed time-varying optimization of a nonlinear fractional-order multiagent system (FOMAS) over a weight-unbalanced directed graph (digraph), where the heterogeneous unknown nonlinear functions and disturbances are involved. The aim is to cooperatively minimize a convex time-varying global cost function produced by a sum of time-varying local cost functions within a fixed time, where each time-varying local cost function does not have to be convex. Using a three-step design procedure, a fully distributed fixed-time optimization algorithm is constructed to achieve the objective. The first step is to design a fully distributed fixed-time estimator to estimate some centralized optimization terms within a fixed time T0. The second step is to develop a novel discontinuous fixed-time sliding mode algorithm with nominal controller to derive all the agents to the sliding-mode surface within a fixed time T1, and meanwhile the dynamics of each agent is described by a single-integrator MAS with nominal controller. In the third step, a novel estimator-based fully distributed fixed-time nominal controller for the single-integrator MAS is presented to guarantee all agents reach consensus within a fixed time T2, and afterwards minimize the convex time-varying global cost function within a fixed time T3. The upper bound of each fixed time Tm(m=0,1,2,3) is given explicitly, which is independent of the initial states. Finally, a numerical example is provided to validate the results.

Adaptive fuzzy fault‐tolerant control for cooperative output regulation with unknown nonlinear disturbances and actuator faults

AbstractThe cooperative output regulation problem with actuator faults and unknown nonlinear disturbances is a challenging research area in multi‐agent systems. This paper proposes an adaptive fuzzy fault‐tolerant controller to address this problem by employing a fuzzy logic system for approximating the unknown nonlinear disturbances. The internal model principle is utilized to transform the cooperative output regulation problem into a stability problem of an auxiliary system, with the aim of compensating for actuator failures in the system. By incorporating fuzzy logic system, the adaptive controller is able to approximate and compensate for the unknown nonlinear disturbances. Extensive simulations are conducted to validate the robustness and performance of the proposed method. The adaptive controller effectively handles actuator faults and achieves output regulation with unknown nonlinear disturbances.

Adaptive stochastic model predictive control via network ensemble learning

This paper proposes a novel ensemble learning-based adaptive stochastic model predictive control (SMPC) algorithm for constrained linear systems with unknown nonlinear terms and random disturbances. The ensemble network combining a feedforward neural network and a Bayesian network is used to offline learn the nonlinear dynamics and disturbance distribution parameters. Then, the mixed-tube scheme is designed to cope with input constraints and state chance constraints while decreasing computational demands and conservativeness. The reliability of the stochastic tube is guaranteed using the Hoeffding inequality-based verification mechanism, which results in a chance constraint with double probabilities. The feasibility and exponential stability of the SMPC are rigorously proven. A numerical example verifies the merits of the proposed algorithm in terms of the control performance and the feasible domain.

Model-free predictive current control of Syn-RM based on time delay estimation approach

Abstract This paper investigates an optimal model-free control design for a synchronous reluctance motor (Syn-RM) with unknown nonlinear dynamic functions, parameter variations, and disturbances. The idea is to combine a predictive control with a time-delay estimation technique (TDE) in order to successfully deal with the system’s uncertainties and make the Syn-RM control scheme easy to implement in real-time. This model-free control strategy comprises two cascade control loops namely outer and inner loops. The outer loop is designed for the mechanical part of Syn-RM to ensure the convergence of the speed dynamics by using a proportional-integral controller while the inner loop is developed to control the uncertain dynamics of currents via an optimal robust controller. In the proposed current loop, the predictive control is enhanced by the inclusion of ultra-local model theory where dynamic functions and disturbances are estimated by instantaneous input-output measurements of the Syn-RM using the TDE approach. Moreover, a particle swarm optimization (PSO) algorithm is also proposed to find the optimal design parameters to improve the dynamic performances of the closed-loop control system. Numerical validation tests of the proposed TDE-based model-free predictive current control (TDE-MFPCC) method are performed in the simulation environment of the Syn-RM system, and the results show the robustness and the effectiveness of the proposed TDE-MFPCC compared to the conventional model-based PCC.

Model-Free Robust Backstepping Adaptive Cruise Control

This paper studies the model-free robust adaptive cruise control problem of a vehicle with unknown nonlinear dynamics and disturbances. First, under backstepping control framework, the position tracking errors with different spacing strategies are used to design a virtual control law, which provides a velocity reference. Then, a novel data-driven sliding surface whose parameters are updated by designing estimation algorithm is developed to handle the unknown uncertainties and disturbances. Finally, the model-free robust backstepping adaptive cruise control (MFRB-ACC) method including PI control, model-free control, and robust control is designed. The novelty of the proposed control technique lies in its strong robustness, which is not based on the precise vehicle model. The designed data-driven sliding surface releases the necessity for the accurate mathematical model of the vehicle and guarantees the inherent robustness of the controller, in particular to uncertainties, modelling error, or external disturbance. Moreover, the designed controller contains three terms such that it has an effective decoupling ability and strong robustness. The effectiveness and superiority of the designed MFRB-ACC method are validated on MATLAB, and the simulation results show that compared to the PID algorithm, the designed MFRB-ACC algorithm can track its preceding vehicle with lower tracking error under different spacing strategies, different operating conditions, and low sampling frequencies. Especially at a sampling frequency of 0.1 s, the error under the PID-ACC increases from 0.2 m at a sampling frequency of 0.01 s to 2 m, and the error under MFRB-ACC has little change compared to the error at a sampling frequency of 0.01 s.

Time‐varying state constrained estimators‐based command filtered quantized consensus control for nonlinear multiagent systems

AbstractThis article investigates the command filtered backstepping quantized consensus control for the time‐varying full‐state constrained nonlinear multiagent systems based on unknown system dynamics estimators (USDE). First, different from the intelligent control and the USDE‐based control methods, a novel time‐varying state constrained (TVSC)‐USDE is developed to estimate the unknown nonlinear dynamics and disturbances exactly with fewer parameters, where the states in TVSC‐USDE satisfy the state constraints requirements. The proposed TVSC‐USDE can reduce the computational burdens that caused by the online estimation of weights. Second, distinguish from current state constraints methods, by designing the integral barrier Lyapunov functions (IBLFs), the conservatism feasibility conditions are removed, which is more reasonable for the physical multiagent systems. Furthermore, the explosion of complexity (EOC) is a vital issue to tackle under the IBLFs backstepping control schemes. For this purpose, this article designs the command filters to gain the derivatives and handle the EOC problem. Further, to counterbalance the filtering errors, the error compensation signals are first developed in IBLFs‐based control method. Finally, the input quantized signals are introduced for the limited bandwidth problem in multiagent network systems, and the proposed control approach can attain consensus tracking under low communication rates and small tracking errors.

Neural-Network-Based Event-Triggered Sliding Mode Control for Networked Switched Linear Systems With the Unknown Nonlinear Disturbance.

The event-triggered sliding mode control (SMC) problem for uncertain networked switched systems with the external unknown nonlinear disturbance is investigated. A neural network (NN) receiving the triggered state is utilized to approximate the external unknown nonlinear disturbance. First, a novel adaptive mode-dependent continuous-time event-triggering scheme (ETS) based on NN weights' estimations is proposed to reduce the burden of the network bandwidth. Then, using the time-varying Lyapunov function method, a novel adaptive NN event-triggered sliding mode controller is established and a dwell-time switching law is obtained, which can guarantee ultimate boundedness, and attain the sliding region around the specified sliding surface for switched systems. Further, a new integral sliding surface that depends on the system states at switching instants and includes the exponential term is proposed. Obtaining the boundary of the sliding mode region relies on the exponential term for continuous-time systems. Moreover, the Zeno behavior can be avoided under the proposed continuous-time ETS by dividing event-triggering signals and switching signals. Finally, a comparative example and a switched Chua's Circuit example are given to illustrate the effectiveness of the proposed method.

Adaptive control for a class of flexible mechanical systems with bending deformation and torsion deformation in the presence of nonlinear time-varying actuator faults and unknown control directions

Vibration control problem of a class of flexible mechanical systems with bending deformation and torsion deformation in the presence of nonlinear time-varying actuator faults and unknown control directions is considered in this paper. The dynamics model of this kind of flexible mechanical system, which includes two kinds of coupled vibration deformation and unknown nonlinear disturbances, is composed of some partial differential equations. In order to overcome the control difficulty brought by unknown control directions and time-varying nonlinear actuator faults, a Nussbaum function based adaptive boundary controller is designed to suppress the bending and twist vibration deformations of the mechanical system. The stability of the system is proved by Lyapunov direct method, and the effectiveness of the proposed control scheme is verified by numerical simulation at the end of this paper.

Fuzzy Adaptive Control for Vehicular Platoons With Constraints and Unknown Dead-Zone Input

In this paper, an adaptive fuzzy control problem is studied for a connected automated vehicles platoon subject to unknown dead-zone input and constraints. To better handle the unknown nonlinear dynamical functions and disturbances, the nonlinear dynamics model is transformed to a new model. Then, the fuzzy logic system (FLS) is used to identify the unknown nonlinear functions. A dead-zone inverse technique is introduced to eliminate the negative effects of the unknown dead-zone input nonlinearity. In the framework of backstepping, the tangent barrier Lyapunov function (BLF) is introduced in this paper, and a distributed adaptive fuzzy control scheme is designed so that the position, velocity and acceleration of the vehicle platoon do not violate the given constrained boundaries. Finally, based on the Lyapunov stability theory, it is noted that all signals in the closed-loop system are bounded and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the proposed approach is validated by simulation results.

An Efficient Underwater Navigation Method Using MPC with Unknown Kinematics and Non-Linear Disturbances

Many Autonomous Underwater Vehicles (AUVs) need to cope with hazardous underwater medium using a limited computational capacity while facing unknown kinematics and disturbances. However, most algorithms proposed for navigation in such conditions fail to fulfil all conditions at the same time. In this work, we propose an optimal control method, based on a receding horizon approach, namely MPC (Model Predictive Control). Our model also estimates the kinematics of the medium and its disturbances, using efficient tools that rely on the use of linear algebra and first-order optimization methods. We also test our ideas using an extensive set of simulations, which show that the proposed ideas are very competitive in terms of cost and computational efficiency in cases of total and partial observability.

Modeling and Adaptive Boundary Robust Control of Active Heave Compensation Systems

Heave compensation systems are essential for operations’ safety, reliability, and efficiency in harsh offshore environments. This paper investigates the vibration suppression problem of a type of deep-sea robot with the length of time variation and harsh operating environments for active heave compensation systems, where hydraulic heave compensators implement actuators with input nonlinearity, model coupling, and unknown nonlinear disturbances. A robust adaptive output feedback control scheme based on the backstepping control method is designed to eliminate deep-ocean robot vibration, where the adaptive law handles the system parameter uncertainty. Meanwhile, a nonlinear disturbance observer (NDO) is introduced to overcome the effects of random disturbances and model coupling. In addition, the stability of the whole system is proved according to Lyapunov’s theory, and the scheme is shown to be feasible by theoretical analysis. Finally, a comparative simulation study was conducted to validate the effectiveness of the proposed controller.

Anti-disturbance attitude quantized control of flexible spacecraft via a triggering and quantization joint dynamic event-triggered mechanism

This article investigates the problem of dynamic event-triggered anti-disturbance attitude quantized control based on the Takagi-Sugeno fuzzy method for flexible spacecraft, where multiple disturbances and parameter uncertainties are simultaneously considered. First, by employing the Takagi-Sugeno fuzzy model to represent the nonlinear disturbances with additional disturbance, a disturbance observer based on the Takagi-Sugeno fuzzy model is constructed under the unmeasurable premise variables to estimate the unknown nonlinear disturbances. Furthermore, a flexible vibration observer is formatted. Subsequently, a dynamic event-triggered mechanism with the quantization parameter is introduced, which can obtain a trade-off between quantization density and event-triggered frequency on the condition that the system stability is ensured. And an event-triggered anti-disturbance quantized controller is constructed, which can make up for Takagi-Sugeno fuzzy modeling disturbances and reject parameter uncertainties and other bounded disturbances. To guarantee the bound of the closed-loop signals, some sufficient conditions are obtained. At last, the efficiency of the suggested control strategy is illustrated via the simulation results.

Adaptive neural control for coordinated rolling motion of vessels with zero speed via composite learning

An intelligent control method under the backstepping and dynamic surface control (DSC) design framework is proposed in this article. The proposed method is aiming at solving the problem of coordinated rolling motion control of the supply and receiving vessels with internal dynamic uncertainty, unknown external nonlinear disturbance, input saturation, and unmeasurable rolling angular velocity. In the control design, a neural-based state observer is constructed to generate the unmeasurable rolling angular velocity, which achieves a separate design from the control law. To compensate the input saturation effect, a second-order auxiliary dynamic system (ADS) is designed, and its states are inset into the transformation of coordinates required by backstepping. To realize the accurate reconstruction of the neural network for the dynamic uncertainty, the adaptive neural and nonlinear disturbance observer (NDO) are used to reconstruct the internal dynamic uncertainty and unknown external disturbance, respectively. And then the serial–parallel estimation model is introduced to establish a predictor of the system state, and a composite adaptive learning law is designed, which includes the velocity error and predictor error. As a result, the idea of classification reconstruction is developed. Finally, an adaptive neural composite learning coordinated rolling motion control solution is developed. Lyapunov stability analysis shows that all signals in the closed-loop control system are bounded. The effectiveness of the control solution is verified by the simulations.