Saturation Constraints Research Articles

Disturbance observer-based adaptive neural control for underactuated surface vehicle with constraint of input saturation

In this paper, a robust adaptive neural control algorithm is provided to solve the problem of path-following control for underactuated surface vehicle (USV) subject to input saturation, in the presence of model uncertainties and unknown marine environmental disturbance.In the algorithm, through the application of neural network (NN), an adaptive disturbance observer (ADO) is proposed to observe the unknown disturbance, which does not need the precise information of the upper bound of the disturbance, and the model uncertainty can be appropriated simultaneously. Specially, since the ADO and control law share the same set of NN, the number of adaptive parameters of the controller can be greatly reduced. After that, dynamic surface control (DSC) method is used to avoid repeated derivative in the process of back-stepping, which solve the “calculation explosion” problem. An auxiliary system is designed to compensate errors caused by input saturation, which solve the input saturation problem of actuators. Through Lyapunov stability analysis, it is proved that all signals of the closed-loop system are semi-globally ultimately uniformly bounded (SGUUB). Finally, some experiments are conducted to demonstrate the effectiveness of the algorithm.

Driveline oscillation attenuation through Clutch Micro-Slip and Model Predictive Control

This paper considers a Model Predictive Control (MPC) approach to micro-slip clutch control for driveline shuffle attenuation and longitudinal acceleration oscillation reduction in vehicles with an Automated Manual Transmission system. We introduce an original MPC formulation to consider the performance tradeoff between the clutch slip regulation, the torsional oscillation attenuation and the jerking reduction. A particular problem to be considered is handling saturation constraints on the transmitted torque. We employ a piecewise-affine linear model of the actuator-driveline system to take care of special manoeuvres that cause inversions in the transmission motion. On the methodological side, we present a switching MPC approach where the underlying optimization problem is a quadratic programme for a fast online controller implementation on real-world transmission control unit platforms. To show the effectiveness of the proposed approach, we present extensive simulation results and experimental tests performed on a prototype vehicle.

Fixed time prescribed performance dynamic positioning control design for surface vessels

This paper addresses the problem of fixed time dynamic positioning (DP) control for surface vessels under the influence of external disturbances, while considering input and output constraints. Using the hyperbolic tangent function, a saturation handling method with an auxiliary control system is constructed, which can handle the saturation constraints of both the control input and its rate of change within a same control framework. By incorporating an auxiliary sliding mode variable, a fixed time disturbance observer (FTDO) is developed to estimate external disturbances. This FTDO ensures that the estimation error converges within a fixed time and provides users with the flexibility to determine the desired convergence time. Moreover, a nonlinear function is introduced in the FTDO to reduce the possibility of singular problems in the control system. Combining the developed FTDO, prescribed performance control strategy and backstepping control technique, a novel DP control strategy is proposed, which not only ensures that the positioning error satisfies the output constraint requirements within a fixed time but also guarantees the fixed time stability of the closed-loop system. Finally, to validate the effectiveness of the proposed DP control law, numerical simulation analysis is carried out on a supply vessel.

Distributed Event-Triggered Control of DC Microgrids With Input Saturation and Time Delay Constraints

This article proposes a novel distributed event-triggered secondary control approach subject to input saturation and time delay in communication links among distributed generators (DGs) to achieve both voltage restoration and proportional current sharing in islanded DC microgrids (MGs). In contrast to periodic communication, sampled-data exchanges only occur when the triggering condition is satisfied. Therefore, communication instants and computation burdens will be reduced significantly. Compared with the existing event-triggered mechanism, this article investigates conditions for achieving consensus in the presence of both input saturation and time delay constraints. The Lyapunov synthesis is employed to derive the stability and convergence analysis of the proposed event-triggering condition and ensure the Zeno-free of the system. Finally, the effectiveness of the proposed controller is validated by simulating its performance under various scenarios in an islanded DC MG using the MATLAB/SimPowerSystems Toolbox.

Gaussian Process Based Modeling and Control of Affine Systems with Control Saturation Constraints

Gaussian Process Based Modeling and Control of Affine Systems with Control Saturation Constraints

Model-Parameter-Free Prescribed Time Trajectory Tracking Control for Under-Actuated Unmanned Surface Vehicles with Saturation Constraints and External Disturbances

This paper mainly addresses the model-parameter-free prescribed time trajectory tracking control issue for under-actuated unmanned surface vehicles (USVs) that are susceptible to model uncertainties, time-varying disturbances, and saturation constraints. Firstly, a state extension based on coordinate transformation was designed to address the lack of control in the sway channel. Secondly, nonlinear behavior stemming from saturation constraints is not always differentiable. Regarding this, a smooth dead-zone-based model was conducted to fit the behavior, leaving a relatively simple actuator model. Then, an improved prescribed time–prescribed performance function (PTPPF) and error transformation method were utilized to propose a model-parameter-free control algorithm that guarantees user-defined constrained boundaries while ensuring all tracking errors converge within small domains before a preassigned settling time. The theoretical analysis was conducted by the initial value theorem, Lyapunov’s second method, and proof by contradiction, followed by comparative simulation results that verified the effectiveness of the proposed control scheme.

Event-trigger-based composite adaptive fuzzy control for nonlinear time-varying state constraint systems with asymmetric input saturation

In this paper, a novel composite adaptive fuzzy control (AFC) based on the command filtered backstepping and event-triggered mechanism is proposed for nonstrict-feedback nonlinear systems with input saturation and time-varying full-state constraints. A fuzzy observer is designed to estimate the unmeasured states. Simultaneously, by introducing a serial-parallel estimation model, a fuzzy control tactic with the composite adaptive law of parameters is established which greatly increases the precision of unknown term estimation. In addition, the command filtering technique is employed to solve the issue of “explosion of complexity” and compensate for the errors caused by the command filters. The event-triggered mechanism is utilized to effectively decrease the data redundancy. By using the time-varying barrier Lyapunov functions (BLFs), all the states can remain within a prescribed time-varying interval. The designed controller can guarantee the convergence of the tracking error at the origin and the boundedness of all closed-loop signals. Finally, two simulation examples are carried out to validate the effectiveness of the proposed control scheme.

Impulsive consensus of nonlinear multi-agent systems with input and state saturation constraints

This work focus on the study of consensus problems of nonlinear second-order multi-agent system (MAS). By fully taking performance difference constrained by the locations of communication channel into account, and in view that impulsive control scheme can reduce control costs in some degree, input-saturation and state-saturation are integrated into the design of impulsive consensus control protocol, respectively. By doing that, some sufficient conditions that guarantee the consensus of nonlinear MAS are derived by using Lyapunov stability theory. Finally, by giving two examples, some simulation examples are presented to verify the theoretical results.

Predictive Feedback Control for Inventory Systems Subject to Delays and Constraints

In this paper, we discuss the inventory regulation problem of a logistic system composed of a production unit and inventory unit. The production system is characterized by a constant lead time, and the storage unit presents fixed losses due to perishable products. In this study, the system is subject to finite production resources, limited storage capacities, and the customer demands are uncertain in a given range. We address a control strategy to the input time delay system that is subject to positive and saturation constraints and to bounded disturbances. We apply a linear prediction feedback control strategy, in order to stabilize the closed-loop system and to fulfill the system constraints, so that necessary and sufficient conditions for the existence of an admissible control law are formulated.

Event-Triggered Adaptive PID Fault-Tolerant Control of Underactuated ASVs Under Saturation Constraint

This article discusses a control problem of underactuated autonomous surface vehicles (ASVs) subject to internal/external uncertainties, input saturations, and actuator faults, and proposes a novel event-triggered adaptive PID fault-tolerant (ETAPID-FT) control solution. To resolve the design difficulty caused by the underactuated feature, a standard multivariate integral cascade form regarding the mathematical model of underactuated ASVs is established. And then, to facilitate the implementation of PID-based design framework, the nonsmooth actuator saturation nonlinearity caused by the saturation constraint is represented by a smooth saturation model. In the control design, considering full features of the actuator fault and smooth function, an indirect neural approximation with single-parameter-leaning technique is developed, which endows the function of self-tuning control gain for the control solution. To relieve the mechanical wear of actuator, an improved event-triggered protocol by implanting a decreasing function of ASV’s position error is proposed. Moreover, the theoretical results show that the proposed ETAPID-FT control solution can guarantee the boundedness of all the signals in the closed-loop control system of underactuated ASVs. Finally, the validity of the developed control solution is confirmed by the simulation and comparative results

Control for Nonlinear Fuzzy Time-Delay Multiagent Systems: Two Kinds of Distributed Saturation-Constraint Impulsive Approach

The impulsive consensus problems of nonlinear time-delay multi-agent system under communication constraints are analyzed and investigated in the paper. In view of the limited communication channel, global input saturation constraint is integrated into the design of impulse control protocol. Furthermore, in order to reduce energy consumption and communication cost, two kinds of impulsive control protocols are designed which conclude general impulsive control protocols and event-triggered impulsive control protocols. For event-triggered impulsive control protocols, by setting event decision time at impulse time, this novel consensus control protocols not only decrease communication frequency and energy consumption, but also avoid “Zeno behavior”. Through theoretical derivation, some sufficient conditions that guarantee the leader-following consensus of multi-agent system are presented. In the end, for the sake of explaining the superiority of impulse control combined with event-triggered control strategy in more detail, the simulations of two control methods are given respectively for the same multi-agent system, which can be better compared and explained.

Adaptive Neural Network Output-Constraint Control for a Variable-Length Rotary Arm With Input Backlash Nonlinearity.

This article studies the problem of deformation reduction and attitude tracking for a rotated and extended flexible crane arm with input backlash-saturation and output asymmetrical constraint. By employing Halmilton's principle, the arm system model is formulated by a set of partial and ordinary differential equations (ODEs). Given the modeling inaccuracy, a radial neural network (RNN) is used to approximate system parameters. To better design the controllers, the backstepping technique is applied to the control design. For input nonlinearities with backlash and saturation, we reversely transform them as an asymmetric saturation constraint via a virtual input. A barrier Lyapunov function (BLF) containing logarithmic terms is constructed to guarantee the asymmetric output constraints and the uniformly ultimate boundedness and stability of the arm system are proved. Finally, to testify the effectiveness of the proposed controllers, numerical simulations are carried out, and responding simulation diagrams are displayed.

Event-triggering Control of Networked Control Systems Under Random Deception Attacks: An Adaptive Triggering Strategy With Saturation Constraint

Event-triggering Control of Networked Control Systems Under Random Deception Attacks: An Adaptive Triggering Strategy With Saturation Constraint

Adaptive optimized consensus control for a class of nonlinear multi-agent systems with asymmetric input saturation constraints and hybrid faults

This article studies the adaptive optimized leader–follower consensus control problem for a class of discrete-time multi-agent systems with asymmetric input saturation constraints and hybrid faults based on the optimized backstepping technique. Different from the conventional saturation model, we consider an individual asymmetric saturation constraint for each actuator instead of a common upper and lower bound for all actuators. Besides, a set of hybrid faults is also considered, with the main focus on the partial fault and bias fault. To eliminate the effects of saturation and faults, a simplified smooth function is constructed to approximate the asymmetric saturation model, and designed compensation signals are used to cope with the two main types of faults to improve the fault-tolerance and system performance. Subsequently, long-term strategic utility functions and virtual control signals are approximated to the optimal levels by adopting the actor–critic neural network (NN) framework, and the actor–critic NN weights are adjusted in the light of a gradient descent method. According to the forward difference Lyapunov function approach, it is proved that the closed-loop system can be stabilized and all errors are semiglobally uniformly ultimately bounded. Finally, the validity of the proposed control scheme is demonstrated through two simulation examples.

Fuzzy adaptive tracking control of fractional-order multi-agent systems with partial state constraints and input saturation via event-triggered strategy

This paper addresses the problem of adaptive tracking control for fractional-order multi-agent systems subject to input saturation and partial state constraints, utilizing an event-triggered strategy with output feedback. The interconnected graph of multiple agents is assumed to be directed and contain a spanning tree. Since only the system output is available, a fractional-order state observer is designed to obtain the unmeasurable states. The nonlinear dynamics of the system are assumed to be unknown and are approximated by a fuzzy logic system. By utilizing the barrier Lyapunov function, backstepping procedure and dynamic surface control technique, an event-triggered adaptive control scheme is proposed to ensure that the constrained states remain within their bounds, all system signals are bounded, tracking error converges to a bounded set containing the origin, and no Zeno behavior occurs. Finally, the validity of the presented method is verified by simulation.

Fully distributed bipartite output formation tracking for heterogeneous singular multiagent systems on signed digraphs

AbstractThis article investigates the bipartite output formation tracking problem of heterogeneous singular multiagent systems (MASs) over a signed digraph on which two types of cases without and with input saturation are considered, respectively. First, a novel formation tracking protocol is presented for heterogeneous singular MASs in the absence of input saturation constraints. In the proposed protocol, a distributed observer with adaptive updating mechanism is constructed for each follower, which does not involve global information of communication graph. Based on the observer, the designed controller addresses bipartite output formation tracking of heterogeneous singular MASs in a fully distributed fashion. Then, by the output regulation theory and low‐gain feedback technique, a low‐gain formation tracking protocol is proposed to achieve semi‐global bipartite output formation tracking for heterogeneous singular MASs subject to input saturation constraints. Finally, two simulation examples are given to illustrate the feasibility of theoretical results.

Command filter‐based adaptive control of flexible‐joint manipulator with input saturation and output constraints

AbstractThis paper focuses on the topic of adaptive neural tracking control for flexible‐joint manipulator systems with output restrictions and input saturation. With the aid of the error compensation mechanism, command filter‐based adaptive neural control is proposed for robotic systems driven by permanent magnet synchronous machine (PMSM). An auxiliary signal produced by the first‐order linear system designed by the property of unmodeled dynamics is used to erase the dynamical uncertainties. The input saturation of system is estimated by ‐function with mathematical transformation. Using barrier Lyapunov function (BLF), each component constraint of output is tackled. During the virtual control design phase, radial basis function neural networks (RBFNNs) may reliably assess the unknown nonlinear continuous function. All the variables in the closed‐loop system are proved to be semiglobally uniform ultimate bounded (SGUUB) by integrating all compensation signals into the overall Lyapunov function and using the defined compact set in the stability analysis of dynamic surface control (DSC). An applied example on robotic system is used to verify the effectiveness of the constructed design idea.

Constrained Model Predictive Control With Integral Action for Twin Rotor MIMO Systems

Abstract This paper describes the design and successful implementation of a constrained model predictive controller with integral action for the control of a Twin Rotor MIMO System (TRMS). The integral action guarantees zero steady-state error in set-point tracking with robustness toward perturbations of the nominal system parameters. In addition to saturation constraints on the input variables, hard constraints are imposed on the controlled output variables, i.e., on pitch and yaw angular positions, to avoid collisions with obstacles. The model predictive controller was designed using a high-fidelity nonlinear model of the TRMS developed in previous work. As an intermediate step, exact linearized models of the TRMS are obtained and their closed-form expressions are reported. The controller was tested experimentally, also showing its effectiveness in ensuring actual collision avoidance by the TRMS when physical obstacles were present.

Adaptive model predictive control based on neural networks for Hover attitude tracking with input saturation and unknown disturbances

The anti-disturbance control of quadrotor attitude tracking under saturation constraints is a difficult problem. In this paper, a neural network-based model predictive controller for quadrotor systems with input saturation and external disturbances is developed. The unmodeled dynamics and external disturbances of the system are simplified to the disturbance superimposed on the nominal system, and the gradient descent neural networks are used to complete the estimation and compensation of the disturbance. The adaptive model predictive controller is designed based on the nominal system. The disturbance value estimated by the neural network adaptively adjusts the control constraints in the model predictive controller. The robustness and anti-disturbance of the designed controller are analyzed. The experiments show that, compared to the robust model predictive control, the algorithm proposed in this paper reduces the steady-state mean errors of the yaw, pitch, and roll attitude channels of the Hover system. Specifically, the algorithm results in a decrease of 2.622%, 2.292%, and 1.192% without external disturbances and 2.056%, 4.17%, and 0.956% with outside disturbances. Experimental results confirm the effectiveness of the proposed method.

Distributed robust model predictive control-based formation-containment tracking control for autonomous underwater vehicles

The autonomous underwater vehicle (AUV) formation may be carried out in a complex maritime environment. The formation-containment tracking problem is faced with challenges due to limited input saturation and maritime safety constraints. Based on communication-measurement union, a navigation control integration framework is proposed to solve the poor observability of underwater acoustic communication interactions. Moreover, a control protocol based on distributed robust model predictive control is designed to efficiently coordinate cooperation between AUVs. With the proof of the Lyapunov theory, the proposed control method can achieve formation-containment tracking even in the presence of external disturbances. Finally, a 7-AUV formation-containment tracking numerical simulation is designed to verify the effectiveness of the proposed method.