Output Constraints Research Articles

Fault-Tolerant Boundary Control of an Euler–Bernoulli Beam Subject to Output Constraint

In this article, the problem of the adaptive boundary control for Euler–Bernoulli beam systems is concerned, which contains actuator partial time-varying fault, external disturbances, and output constraint. Three control strategies are presented to study these problems. The first boundary controller is designed by constructing an adaptive law to compensate actuator partial time-varying fault. The second adaptive boundary controller is formulated to achieve disturbance rejection, in the case of actuator partial time-varying fault and unknown external disturbances, where a boundary disturbance observer is utilized to compensate the unknown boundary disturbance. The third adaptive boundary controller with a barrier term is proposed to constrain the boundary vibration. For all cases, the state of the beam system is guaranteed to be uniformly bounded. Finally, the simulation results illustrate the validity of the proposed control method.

Fault-Tolerant Control for Uncertain Nonstrict-Feedback Stochastic Nonlinear Systems With Output Constraints

This article investigates the problem of fault-tolerant control (FTC) for nonstrict-feedback (NSF) stochastic nonlinear systems subject to actuator faults and output constraints. Most of the existing backstepping-based FTC techniques have been developed for strict-feedback systems, which may cause algebraic loop problems when applied directly to NSF systems. In this work, unknown nonlinear functions are approximated by neural networks (NNs). Based on the properties of NN basis functions and a barrier Lyapunov function, a novel adaptive FTC strategy for an NSF system is proposed to achieve tracking control without violating the output constraint. Adaptive fault accommodation terms are constructed to compensate for the faults without a priori information to achieve online fault tolerance. The designed controller guarantees the fault-tolerant tracking performance while ensuring that all signals are bounded in probability and that the output is within the specified constraint. The performance of the proposed FTC strategy is illustrated by simulation studies.

Event-Triggered Distributed Consensus Filtering Based on Interval Analysis for Heterogeneous Multienergy Networks With Coupling Output Constraints

This paper addresses the distributed consensus filtering problem of heterogeneous multi-energy networks (MENs), where subnetworks exhibit different dynamics and are coupled by output constraints. The global consensus is reached by propagating constraints between subnetworks in the form of interval vectors. Guaranteed intervals satisfying given constraints are returned, which are then used to correct the local states through the probability density function (PDF) truncation. Given the dependency and wrapping effects during constraint propagation, a hybrid Newton-forward backward propagation (FBP) contractor is proposed for avoiding overly pessimistic estimates. It extends the FBP contractor by replacing the natural inclusion function with centered forms. Additionally, an event-triggered mechanism is designed to enhance the efficiency of constraint propagation, which allows the threshold values to be adaptively adjusted according to interval widths. Finally, the proposal is assessed in a steel industrial gas-electricity network with 25 nodes, and the results verify the performance in estimation accuracy, especially for coupling nodes.

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.

Visual Servoing of Rigid-Link Flexible-Joint Manipulators in the Presence of Unknown Camera Parameters and Boundary Output

Exact position control of the flexible-joint manipulator (FJM) is a challenging task due to the manipulator’s nonlinearity and underactuated characteristic. For the three-dimensional (3-D) rigid-link FJM, the image-based visual servoing (IBVS) approach with link-position output constraint and unknown camera parameters is investigated in this article. The controller is designed to deal with three problems. First, the visual servoing control law is divided into two domains based on the singular perturbation method, where the control input for the fast subsystem is developed to damp out the flexible joint’s vibration. Second, the adaptive updating law for estimating the unknown camera parameters is presented in the slow subsystem. Third, to guarantee that the rigid-link position keep in set constraints, the controller for the slow subsystem is designed by introducing a barrier Lyapunov function. According to the Lyapunov theorem of stability, the proposed controller for the FJM is theoretically proved to be asymptotically stable. Several numerical simulation experiments are provided to illustrate that the presented control scheme is effective.

Dynamic Surface Neuro-Backstepping Based Flight Control With Asymmetric Output Constraints

Dynamic Surface Neuro-Backstepping Based Flight Control With Asymmetric Output Constraints

Event-Triggered Saturation-Tolerant Control for Autonomous Underwater Vehicles With Quantitative Transient Behaviors

This paper proposes an event-triggered saturation-tolerant solution to the problem of trajectory tracking control with application to autonomous underwater vehicles (AUVs) considering quantitative transient behaviors. To prespecify the transient tracking behaviors quantitatively and avoid the possible violation of behavioral functions induced by input saturation, a saturation quantitative prescribed performance control (SQPPC) is proposed, where auxiliary subsystems are devised to monitor the input saturation and modify the behavioral boundaries, such that SQPPC can automatically adapt to the input saturation without transgressing the exact output constraints. Intermittent measurement-based uncertainty and disturbance estimator (IMUDE) is devised to reconstruct the lumped disturbances with reduced communication resource usage. Building upon the estimation results obtained from IMUDE, an event-triggered control law is designed, attaining ultimately uniformly bounded (UUB) results, where a relative threshold-based event-triggered mechanism (RTETM) is constructed to reduce the communication resource dissipation along the controller-to-actuator channel. To validate the effectiveness of the proposed control strategy in controlling an omni directional intelligent navigator, simulation results are provided and analyzed.

Adaptive Backstepping Control of Quadrotor UAVs with Output Constraints and Input Saturation

The control performance of quadrotor unmanned aerial vehicles (UAVs) in complex environments can be affected by external disturbances and other factors. In this paper, an adaptive neural network backstepping controller based on the barrier Lyapunov function (BLF) is designed for a quadrotor UAV with internal uncertainties, input–output constraints and external disturbances. Radial basis function neural networks are used to approximate the uncertainties in the dynamic model of the UAV, while the minimum parameter learning method is combined to accelerate the adjustment speed of neural network weights. A robust term is designed to balance the total system disturbance and improve the anti-interference performance. The BLF is used to handle the output constraint so that the constrained parameters cannot break the predefined constraints. An auxiliary system is introduced to solve input saturation and avoid the dependence of tracking error on the input amplitude in the method of approximating input saturation using the smoothing function. The stability of the control system is demonstrated by the Lyapunov method. The simulation results show that the proposed method has high tracking accuracy compared with the backstepping dynamic surface control method, and the input and output are in the predefined range.

Output constrained neural adaptive control for a class of KKVs with non-affine inputs and unmodeled dynamics

AbstractIn this paper, an adaptive neural output-constrained control algorithm is proposed for a class of non-affine kinetic kill vehicle (KKV) systems. The key point is that the non-affine control law can be designed and the output of the KKV system conform to the output limit with the aid of the proposed method. Due to the aerodynamic moments, the actual control torque is non-affine, which can be addressed by introducing an integral process to the design of the controller. Besides, in order to improve the control precision, a nonlinear mapping is put forward so that the output constraint can be transformed to the constraint of the introduced dynamic signal that can be simply achieved. From the simulation results it can be concluded that the states of the KKV system can track the desired trajectories in spite of different working conditions and the control precision is higher compared with other control methods.

Adaptive safe control for tethered aircrafts with input and output constraints

AbstractIn this article, an adaptive safe control scheme is presented for tethered aircrafts subject to unknown disturbances, input saturations, and deferred asymmetric time‐varying output constraints. By integrating a shifting function into an asymmetric barrier Lyapunov function, the deferred asymmetric time‐varying output constraints are first solved. Then, to eliminate the conflict between input saturations and output constraints, an auxiliary system is designed to enlarge output constraint boundaries when input saturation occurs and the system has no tolerance for output constraints, which ensures system safety. Furthermore, disturbance observers are employed to estimate unknown disturbances. Finally, both theoretical proof and simulation results demonstrate the validity of the proposed control scheme.

Adaptive observer-based control of arbitrarily switched fractional-order uncertain nonlinear system subject to input nonlinearities and asymmetric output constraints

This paper addresses an adaptive observer-based control for a switched fractional-order uncertain nonlinear system subject to input nonlinearities and asymmetric output constraints. The switching signal is arbitrary, and the designed controller needs no information regarding this signal. By employing a universal framework, the designed controller can cope with different input nonlinearities in different modes without having any information about their characteristics. It is assumed that only system output is measured, and a designed observer estimates the system states for control implementation. The uncertainties are approximated by intelligent approximators like fuzzy logic systems (FLS) or neural networks (NN). The time-varying asymmetric output constraints have been satisfied using a common barrier Lyapunov function (BLF). Using the common Lyapunov function (CLF) method and the backstepping control technique, the virtual controllers, adaptive law and control input have been designed, and the boundedness of closed-loop signals has been guaranteed. The effectiveness of the designed control scheme has been demonstrated through a numerical and two practical simulation examples.

Dynamical analysis and accelerated adaptive backstepping control of MEMS triaxial gyroscope with output constraints

Dynamical analysis and accelerated adaptive backstepping control of MEMS triaxial gyroscope with output constraints

Finite-time disturbance observers based reliable control for a constrained autonomous flexible manipulator system with mismatched and matched disturbances

This study focuses on a control approach for a constrained autonomous flexible manipulator system (AFMS) with multiple disturbances comprising both matched and mismatched disturbances. First, we propose two disturbance observers to compensate for multiple unknown disturbances in finite time using the finite-time stability theory. Meanwhile, we design the auxiliary systems and barrier Lyapunov functions to tackle input saturations and output constraints. Then based on the proposed disturbance observers, the auxiliary systems, and barrier Lyapunov functions, we propose two reliable control laws to achieve deflection suppression and hub angular displacement tracking. Additionally, the deflection and hub angle tracking error of the AFMS are constrained to a small compact set through controller design. Finally, the deflection suppress control performance of the constrained AFMS with mismatched and matched disturbances is evaluated.

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.

Predefined–Time Adaptive Control of a Piezoelectric–Driven Motion System With Time–Varying Output Constraint

This brief investigates the output feedback adaptive tracking control of a piezoelectric-driven motion (PDM) system with time-varying output constraint. The key features of the developed predefined-time adaptive neural control (PTANC) method are as follows. i) A broad learning system with recurrent enhancement node (RENBLS) is used to construct an RENBLS-based observer such that state information can be estimated; ii) we incorporate a performance regulator and time-varying barrier Lyapunov function (TVBLF) into the controller design. The proposed method can then achieve the control effect in a predefined time without violating the output constraint. Stability analysis of the PTANC strategy is demonstrated in theory. Furthermore, the effectiveness of the proposed control scheme is experimentally verified via the PDM system.

Data‐driven consistent control with data compensation for a class of unknown nonlinear multiagent systems with constraints

AbstractTo solve the problem of longitudinal cooperative formation driving control of multiple vehicles, a novel model‐free adaptive control algorithm with data compensation under constraint conditions (COM‐cMFAC) is proposed in this manuscript. In the COM‐cMFAC algorithm, the pseudo partial derivative (PPD), which is a time‐varying parameter, is used to linearize the nonlinear dynamics of the multivehicle cooperative system by using dynamic linearization technology. Then, a COM‐cMFAC controller is designed. For the case of data packet loss, the proposed controller uses a data compensation mechanism that estimates and compensates for the lost data through the data collected at the previous moment to perform packet loss control. Additionally, the controller considers the constrained input and output problems that will occur in the actual control process and imposes input and output constraints. The main advantage of the COM‐cMFAC algorithm is that the entire control process only needs the input and output data of the multivehicle cooperative system, and it also has a good control effect in the case of packet loss. The stability of the proposed method is verified through strict mathematical analysis, and its effectiveness is verified by semiphysical experiments based on a MATLAB/Simulink and Carsim platform connection environment.

Constrained Moving Path Following Control for UAV With Robust Control Barrier Function

This paper studies the moving path following (MPF) problem for fixed-wing unmanned aerial vehicle (UAV) under output constraints and wind disturbances. The vehicle is required to converge to a reference path moving with respect to the inertial frame, while the path following error is not expected to violate the predefined boundaries. Differently from existing moving path following guidance laws, the proposed method removes complex geometric transformation by formulating the moving path following problem into a second-order time-varying control problem. A nominal moving path following guidance law is designed with disturbances and their derivatives estimated by high-order disturbance observers. To guarantee that the path following error will not exceed the prescribed bounds, a robust control barrier function is developed and incorporated into controller design with quadratic program based framework. The proposed method does not require the initial position of the UAV to be within predefined boundaries. And the safety margin concept makes error-constraint be respected even if in a noisy environment. The proposed guidance law is validated through numerical simulations of shipboard landing and hardware-in-the-loop (HIL) experiments.

Codesign of Quantized Dynamic Output Feedback MPC for the Takagi–Sugeno Model

We present a co-design of measurement quantized dynamic output feedback model predictive control (DOFMPC) for Takagi-Sugeno model with bounded disturbance. The system output is quantized by a dynamic quantizer before it is transmitted to the DOFMPC controller. Hence, we utilize the dynamic output feedback control law with quantized output signal and consider the mixed input and quantized output constraint for the controller design. By optimizing the quantizer and controller paramters on-line, the control performance is enhanced. Moreover, we formulate a 2-leveled optimizations, with the upper level optimizing the performance index and the lower level optimizing the soft constraint in a lexicographic order, for the co-design of the DOFMPC controller and dynamic quantizer. Thus, there are more degrees of freedom for tighting the soft constraints. The recursive feasibility and stability of the proposed approaches are guaranteed. The applicability of the proposed approach is illustrated by a simulation example.

A Singular Perturbation Theory-Based Composite Control Design for a Pump-Controlled Hydraulic Actuator with Position Tracking Error Constraint

Pump-controlled hydraulic actuators (PHAs) contain slow mechanical and fast hydraulic dynamics, and thus singular perturbation theory can be adopted in the control strategies of PHAs. In this article, we develop a singular perturbation theory-based composite control approach for a PHA with position tracking error constraint. Disturbance observers (DOBs) are used to estimate the matched and mismatched uncertainties for online compensation. A sliding surface-like error variable is proposed to transform the second-order mechanical subsystem into a first-order error subsystem. Consequently, the position tracking error constraint of the PHA is decomposed into the output constraint of the first-order error subsystem and the stabilizing of the first-order hydraulic subsystem. Slow and fast control laws can be easily designed without using the backstepping technique, thus simplifying the control design and reducing the computational burden to a large extent. Theoretical analysis verifies that desired stability properties can be achieved by an appropriate selection of the control parameters. Simulations and experiments are performed to confirm the efficacy and practicability of the proposed control strategy.

Event‐triggered adaptive fuzzy control for nonstrict‐feedback nonlinear systems with output hysteresis

AbstractThis paper concentrates upon the issue of adaptive fuzzy tracing control for a class of nonstrict‐feedback nonlinear systems output with hysteresis via an event‐triggered strategy. To handle the difficulty caused by the nonstrict nonlinear systems, the variable separation technique is introduced. The design difficulty of output hysteresis is addressed by employing a hysteresis inverse function and Nussbaum function to compensate unmeasurable state signal. Meanwhile, the fuzzy logic system (FLS) is used to estimate the unknown function at each step of recursion. Moreover, by devising the relative threshold event‐triggered mechanism (ETM), the frequency of actuators and controllers can be largely decreased. Thus, the adaptive fuzzy event‐triggered tracing control strategy is proposed by combining the barrier Lyapunov function and backstepping technique. With the proposed scheme, it is theoretically demonstrated that all signals in the closed‐loop system are bounded, and the tracing errors are driven to a small neighborhood of the origin under the output constraint. Eventually, two examples demonstrate the efficacy of the proposed control strategy.