Output Constraints Research Articles

Iteration Learning Control for Uncertain Nonlinear Systems with Time-Varying Output Constraint

A novel Adaptive Iteration Learning Control (AILC) method is proposed to solve the trajectory tracking problem for a class of nonlinear uncertain systems with external disturbances. Furthermore, the output of system is required to be bounded by a time-varying function. To this end, a Barrier Lyapunov Function (BLF) term is integrated into the AILC scheme such that the impact of the uncertainties and disturbances are significantly reduced without violating the output constraints. A Barrier Composite Energy Function (BCEF) is utilized to analyze the convergence of state error and the boundedness of output. The validity of the proposed AILC scheme is verified by a numerical example. In addition, a high-fidelity simulation platform that can generate a real-life turbulent flow is utilized to demonstrate the robustness of the algorithm.

Boundary control of a flexible beam with output constraint under saturation input

Boundary control of a flexible beam with output constraint under saturation input

Boundary control of a flexible beam with output constraint under saturation input

Boundary control of a flexible beam with output constraint under saturation input

TDE-Based Adaptive Super-Twisting Multivariable Fast Terminal Slide Mode Control for Cable-Driven Manipulators With Safety Constraint of Error

An adaptive super-twisting multivariable fast terminal sliding mode control scheme based on time delay estimation (TDE) and asymmetric error constraints are proposed to guarantee high-precision trajectory tracking control of cable-driven manipulators under complicated unknown uncertainties. The control system is constrained by joint position errors, which are asymmetric and time-varying. First, the error range of the joints is designed to ensure that the deviation of the joint position from the desired profile is not too large while ensuring the safety performance of the manipulator, and the remaining set-total system dynamics are estimated and compensated using time-delay estimation. Secondly, the design analysis of the angular false expectation using safety constraint functions allows the constraints of different cross sections to be handled in a unified system architecture; the problem of robotic arm motion trajectories with output constraints and uncertainties is addressed through rigorous analysis. The super-twisting adaptive control effectively ensures fast, accurate and robust convergence of the algorithm in satisfying all constraints in operation, and the stability of the closed-loop control system is analyzed using the Lyapunov method. Finally, the effectiveness and superiority of the proposed adaptive hyper-torsional non-singular terminal sliding mode control scheme are verified by simulations and experiments in 2-DOF.

Robust stabilisation for a class of uncertain nonlinear systems with output constraint satisfaction via controller switching

Robust stabilisation for a class of uncertain nonlinear systems with output constraint satisfaction via controller switching

Global Prescribed-Time Second-Order Sliding Mode Controller Design Subject to Asymmetric Output Constraint

Global Prescribed-Time Second-Order Sliding Mode Controller Design Subject to Asymmetric Output Constraint

Robust Adaptive Prescribed Performance Control for Unknown Nonlinear Systems With Input Amplitude and Rate Constraints

A novel approximation-free prescribed performance control scheme for unknown nonlinear systems with amplitude and rate saturation on the control input signal is designed in this letter. The proposed control strategy employs nested smooth saturation functions and introduces a reconciling relaxation of the performance constraints based on the actuator limitations. The straightforward gain selection along with the low complexity of the control scheme, simplifies the practical implementation of our algorithm. The adoption of the adaptive prescribed performance control technique ensures the desired trade-off between input and output constraints for any input-to-state stable (ISS) system. Inevitably, for generic systems, the boundedness properties are guaranteed only locally; thus, we provide a sufficient boundedness condition for all closed-loop signals. Finally, an illustrative simulation study is conducted to demonstrate the efficacy of the proposed approach.

Robust stabilisation for a class of uncertain nonlinear systems with output constraint satisfaction via controller switching

Robust stabilisation for a class of uncertain nonlinear systems with output constraint satisfaction via controller switching

Experimental analysis of Aluminum alloy 8011 using Wire cut Electrical discharge machining

The increased demand for miniaturization of complex systems necessitates the production of smaller components, cutting with precision dimensional accuracy. Furthermore, Wire cut electrical discharge machining (WEDM) process is the prominent to the fastest growing industries because of their accuracy, the possibility to obtain complex shapes, close tolerances and economically. Therefore, the aim of this experimental work is to analyze the impact of pulse on time and wire feed rate on performance characteristics namely, kerf width and heat affected zone (HAZ). In this experimental investigation, micro level slot cutting experiments were carried out on aluminum plate of 2 mm thickness by WEDM with response surface design. Multi-criteria decision making Grey relational analysis (GRA) technique has been utilized to find optimal machine settings of process constraints. Polynomial models were developed for output constraints. From results, it is analyzed that the pulse on time has more dominance on kerf width, whereas the wire feed rate has a higher influence on HAZ. The obtained outcomes will expedite the advanced machining industries to enhance machinability and dimensional accuracy.

Finite-Time Adaptive Fuzzy Event-Triggered Control of Constrained Nonlinear Systems via Bounded Command Filter

In this note, finite-time adaptive tracking control is studied for nonlinear systems with unmodeled dynamics, asymmetric time-varying output constraints, and uncertain disturbances. Without any growth conditions, fuzzy logic systems are applied to tackle the unknown complicated functions. A novel backstepping approach is developed by combining barrier Lyapunov functions and bounded finite-time command filter. By regulating the threshold parameters online, a new dynamic event-triggered controller is established to ensure that all the signals of the closed-loop system are bounded and the output can follows the preset signal in finite time. Meanwhile, the output is always staying in an asymmetric time-varying interval all the time. The feasibility of the proposed control algorithm is verified with a numerical example and a practical application.

Adaptive tracking control for a class of nonlinear systems with input dead‐zone and actuator failure

In this paper, the adaptive tracking problem for a class of strictly feedback nonlinear time‐delay systems with output constraints, actuator failures, and input dead zones is studied. First, in the design of controller, the absolute value function and integral term are introduced into the candidate functions to deal with nonlinear factors and distributed time‐varying delays in the system. In addition, an adaptive neural network controller is designed to ensure the stability of the controlled system, and the system tracking error can converge to the origin without violating the constraints. Finally, simulation results verify the feasibility of the proposed control strategy.

Adaptive fault‐tolerant control for multiple Euler–Lagrange systems considering time delays and output constraints

AbstractConsidering the existence of communication delays, the adaptive fault‐tolerant control problems for constrained multiple Euler–Lagrange systems are considered in this paper. First, a communication delays observer is utilized to enable all followers can acquire the leader's state information. Second, based on neural network technique, two distributed coordinated control schemes are designed to make every follower can track the leader. Compared with the first control algorithm, the adaptive fault‐tolerant control problem is further considered in the second control algorithm. The proposed algorithm can simultaneously compensate the actuator bias faults and the partial loss of actuation effectiveness faults. At the same time, a tan‐type barrier Lyapunov function is used to constrain the tracking errors of all followers and the leader. Finally, the simulations show the effectiveness of the proposed control schemes.

Robust adaptive finite-time tracking control for Intervention-AUV with input saturation and output constraints using high-order control barrier function

The Intervention-Autonomous Vehicle (I-AUV) as the effective operation equipment in the deep sea is suppressed to the external and physical constraints, which makes it challenging to achieve underwater missions. Aiming at the trajectory tracking control of I-AUV with input saturation and output constraints, a higher-order control barrier function-quadratic program (HoCBF-QP) based control scheme is proposed in this paper considering the uncertainties, disturbance and dynamic interaction. Firstly, a robust adaptive control is presented to track the desired trajectory in a finite time, where a feedback control term, based on the continuous terminal sliding mode (TSM) technique, is designed to improve the tracking performance under the uncertainties, disturbance and dynamic interaction. Secondly, a time-varying HoCBF is further developed to handle the high relative degree time-varying output constraints, then the multiple high-order control barrier functions can bring a family of safety-critical control input to preserve the reachability of AUV posture and joints motion. Thirdly, a convex quadratic program (QP)-based separation control frame is designed to conduct the tracking problem and output satisfaction separately. Due to its globally optimal advantage, the proposed control design provides a flexible frame to track objectives for the constrained I-AUV systems. Comparable simulation results demonstrate the fast convergence, robustness and high tracking accuracy of the proposed HoCBF-QP control for I-AUV systems with constraints.

End-to-End Deep Neural Network Based Nonlinear Model Predictive Control: Experimental Implementation on Diesel Engine Emission Control

In this paper, a deep neural network (DNN)-based nonlinear model predictive controller (NMPC) is demonstrated using real-time experimental implementation. First, the emissions and performance of a 4.5-liter 4-cylinder Cummins diesel engine are modeled using a DNN model with seven hidden layers and 24,148 learnable parameters created by stacking six Fully Connected layers with one long-short term memory (LSTM) layer. This model is then implemented as the plant model in an NMPC. For real-time implementation of the LSTM-NMPC, an open-source package acados with the quadratic programming solver HPIPM (High-Performance Interior-Point Method) is employed. This helps LSTM-NMPC run in real time with an average turnaround time of 62.3 milliseconds. For real-time controller prototyping, a dSPACE MicroAutoBox II rapid prototyping system is used. A Field-Programmable Gate Array is employed to calculate the in-cylinder pressure-based combustion metrics online in real time. The developed controller was tested for both step and smooth load reference changes, which showed accurate tracking performance while enforcing all input and output constraints. To assess the robustness of the controller to data outside the training region, the engine speed is varied from 1200 rpm to 1800 rpm. The experimental results illustrate accurate tracking and disturbance rejection for the out-of-training data region. At 5 bar indicated mean effective pressure and a speed of 1200 rpm, the comparison between the Cummins production controller and the proposed LSTM-NMPC showed a 7.9% fuel consumption reduction, while also decreasing both nitrogen oxides (NOx) and Particle Matter (PM) by up to 18.9% and 40.8%.

An optimization-based method for tuning PI controllers for systems subject to control and output constraints

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Finite‐time adaptive fault‐tolerant control for output‐constrained interconnected systems with unknown control coefficients

AbstractThis article studies the adaptive finite‐time tracking control problem for a class of output‐constrained nonlinear interconnected systems with unknown actuator faults and parametric uncertainties. By integrating the lower bounds of unknown virtual control coefficients into the Lyapunov functions design, an adaptive control strategy is proposed on the basis of adaptive estimations and some smooth functions, which make the controller effectively compensate the effects of unknown virtual control coefficients, unknown interconnected terms, and other uncertain nonlinear functions. Besides, the controller can also compensate the effect of actuator faults by fusing a two‐step design method into the backstepping design framework. Simultaneously, in view of the output constraint requirements, a barrier Lyapunov function is introduced, and it is a more general one that can reckon with time‐varying asymmetric output constraints. Furthermore, in light of the finite‐time stability criterion, all the signals of system are bounded and the tracking performance can be guaranteed in a finite time. Finally, the effectiveness of the proposed method is validated by some simulation results.

Neural Network-Based Adaptive Boundary Control of a Flexible Riser With Input Deadzone and Output Constraint.

In this article, vibration abatement problems of a riser system with system uncertainty, input deadzone, and output constraint are considered. For obtaining better control precision, a boundary control law is constructed by employing the backstepping method and Lyapunov's theory. The output constraint is guaranteed by utilizing a barrier Lyapunov function. Adaptive neural networks are designed to cope with the uncertainty of the riser and compensate for the effect caused by the asymmetric deadzone nonlinearity. With the designed controller, the output constraint is satisfied, and the system stability is guaranteed through Lyapunov synthesis. In the end, numerical simulation results are provided to display the performance of the developed adaptive neural network boundary control law.

Vibration Control for a Nonlinear Three-Dimensional Suspension Cable With Input and Output Constraints

This study is concerned with the dynamical analysis and vibration attenuation for a three-dimensional (3-D) suspension cable system of a helicopter preceded by output constraints and input backlash. First, the dynamic model of a 3-D suspension cable is constructed in accordance with Hamilton’s principle. Then, a backlash inverse compensator is established to tackle the input backlash nonlinearities. Subsequently, three boundary controllers together with output signal barrier functions are proposed to dampen the oscillations while not violating the cable swing constraints. The uniform boundedness of the closed-loop system is guaranteed with recourse to the direct Lyapunov’s method. Finally, the validity and efficiency of the derived control laws are testified through simulation results.

Event-triggered adaptive neural control for state-constrained switched nonlinear systems based on nonlinear shifting function technique

This paper addresses the event-triggered tracking control design for state-constrained switched nonstrict feedback nonlinear systems. With the help of a time-varying nonlinear shifting function (TVNSF) introduced into the switched nonlinear system, the proposed solution is seen as a unified tool regardless of whether the constraint conditions are state constraints, output constraint, or even no constraint. Also, by allowing the triggering error to vary with the switching signal in time, the negative effects of the mismatch between the individual controller and the subsystem on system performance are trumped. Moreover, by using constructed individual Lyapunov function that depends on the lower bound of the control gain function of individual subsystem, a novel switching signal satisfying the average dwell time (ADT) is provided to ensure the boundedness of all variables in the closed-loop system. Finally, the proposed theory is carried over into a mass-spring-damper system to verify its effectiveness.

Practical Fixed-Time Consensus Tracking for Multiple Euler–Lagrange Systems With Stochastic Packet Losses and Input/Output Constraints

In this article we investigate the practical fixed-time consensus tracking problem for multiple Euler–Lagrange systems, which is subject to, concurrently, stochastic packet losses in directed communication networks (DCNs) and input/output constraints. In practice, the packet losses in the communication network may lead to communication failure and control accuracy degradation. To address this impact, a novel fully distributed observer is established to estimate the leader’s states within fixed time. Subsequently, the state constraint function is introduced so that the designed adaptive control protocol can be used in a uniform structure with unconstrained or asymmetric/symmetric output constraints without changing the adaptive structure. Combining the backstepping technique with fuzzy-logic systems, an adaptive fixed-time local control protocol is constructed to drive each agent to track the leader’s states estimated by the observer. Besides, a novel auxiliary system is introduced to solve the constraint problem of the control input. Furthermore, through rigorous theoretical analysis, it is verified that all signals converge to the compact set close to zero with guaranteed fixed-time convergence rate. At last, a representative simulation is carried out to demonstrate the practicability of the proposed method.