International Journal of Robust and Nonlinear Control最新文献

This paper investigates a nonlinear two-player non-zero-sum differential game with state and input constraints. To solve this problem, this paper constructs a neural network (NN) framework to approximate the solution of the Hamilton-Jacobi-Isaacs (HJI) equation. The adaptive dynamic programming (ADP) method is utilized where each player only needs one critic NN. To solve the issue of state and input saturations, this paper develops a novel constrained system for the differential game, firstly to make the states within the predetermined constraint set. Then, the non-quadratic expression is used to substitute the traditional quadratic expression for the two-player non-zero-sum differential game, and both of the inputs of the two players are constrained. With these treatments, the control input and the system are more in line with real-world applications. Moreover, the stability of the system is also analyzed using the Lyapunov theorem. Two numerical examples are presented to illustrate that the critic NN weights estimation errors and the system are uniformly ultimately bounded (UUB), and the state and input constraints can be achieved.

This paper suggests a neural adaptive command filtered backstepping tracking control strategy for the chaotic permanent magnet synchronous motors with asymmetric prescribed performance constraints. Therefore, enable chaotic permanent magnet synchronous motors (PMSM) to obtain good robustness and better universality in practical industrial environments, and realizes more accurate control effect. The main challenge lies in devising a valid funnel-like solution within the backstepping frame to handle the asymmetric performance constraints that traditional solutions cannot solve. To achieve this, a novel funnel-like function is introduced, integrating a performance boundary function independent of initial output error, thereby transforming the system into an unbounded one. Additionally, the “explosion of complexity” with conventional backstepping is mitigated by introducing command filtering and constructing an error compensating system to reduce errors. By combining the theory of Lyapunov function and backstepping technique, the virtual controller and the real controller with adaptive law ensure the stability of the system. The disturbance observer and neural network solve the external disturbance and the uncertain nonlinear problem, respectively. Simulation comparisons confirm the robustness of the proposed control scheme and demonstrate its superiority over existing solutions.

In this article, we develop a novel adaptive extremum-seeking control (AdESC) algorithm with robustness guarantees and without persistence of excitation (PE). Specifically, this builds on a proportional-integral (PI)-like parameter estimator. A zeroth-order optimization framework is used, where the optimizer/agent can only query the numerical value of the cost function at the current coordinate given an unmodeled bounded disturbance. Since parameter estimation plays a decisive role in the stability and convergence properties of AdESC algorithm, it is also well established in the existing literature that to ensure parameter convergence a stringent PE condition is required. Here, we eliminate the need for a stringent PE condition by utilizing a novel set of weighted integral filter dynamics, while ensuring sufficient richness using a milder condition, called initial excitation (IE). Moreover, to validate the robustness guarantees towards unmodeled bounded disturbance, a detailed Lyapunov function based analysis is performed to establish the closed-loop stability and convergence in the form of uniform ultimate boundedness (UUB). Furthermore, an experimental study using a unicycle wheeled mobile robot (WMR) is carried out as a proof-of-concept considering disturbance and disturbance-free scenarios.

This article addresses the asynchronous guaranteed cost control (GCC) problems for interval type-2 (IT2) fuzzy continuous-time Markov jump systems (C-TMJSs) with quantization and non-consecutive transmission. A dynamic event-triggered mechanism (ETM) for IT2 fuzzy C-TMJSs is designed to address the limited bandwidth and communication burden, which incorporates mode-dependent thresholds and weighting matrices. An asynchronous IT2 fuzzy guaranteed cost controller possessing unmatched premise variable information is developed to stabilize the considered system, achieving desired GCC performance. The relationship between system mode and controller mode is described by the hidden Markov model (HMM). A quantizer is employed to quantize the signal prior to transmission to the next node, modeled as a stochastic quantization utilizing the HMM. Furthermore, an improved mode-dependent and fuzzy-dependent Lyapunov function is constructed for stability analysis, and the conditions of optimal GCC performance are derived. Finally, two examples are performed to validate the effectiveness of the proposed algorithms.

This article presents an anti-disturbance constrained control scheme for the pitch channel of autonomous underwater gliders subject to internal and external disturbances, as well as actuator saturation. First, the integral control technique is employed to develop a disturbance observer to estimate the overall effect of possible uncertainties and disturbances on the nominal vehicle model, which is referred to as the mismatched lumped disturbance. Then, a disturbance rejection control law is constructed based on the disturbance observer. Following that, a straightforward anti-windup modification is proposed to handle potential input constraints by using the tracking back calculation technique. Specifically, the difference between saturated and unsaturated control input signals is utilized to create a feedback signal that addresses the disturbance observer input, thereby alleviating system windup during actuator saturation events. Furthermore, the stability of the overall closed-loop system is established in term of the Lyapunov stability theorem, demonstrating that the tracking error is ultimately bounded. Compared with some existing anti-windup control schemes, the suggested approach offers intuitive design guidelines, resulting in a simple controller that can be easily implemented. Finally, the effectiveness and robustness of the proposed control scheme are verified through simulation results.

This article investigates anti-attack stabilization with passivity problem of uncertain singular semi-Markov jump systems (singular S-MJSs) with exogenous disturbance and delay. An ingenious non-fragile observer-based neural sliding mode control (SMC) scheme is put forward to solve the problem. First, considering unmeasured states, a distinctive non-fragile and decoupled observer, which does not contain the control input or any auxiliary sliding mode compensator design as in existing observer-based SMC approaches, is established such that the disadvantages of sliding mode switching in observers in existing literature can be avoided. Then, “only one sliding surface” design and a new system analysis route are presented, and the derived sliding surface is accessibly designed. Next, a new version of stochastic admissibility and passivity sufficient condition is given, and a related algorithm via an optimization problem is proposed to determine the controller gain and the observer gain by linear matrix inequalities (LMIs). Further, a novel observer-based anti-attack neural SMC law, which utilizes a neural network-based approach to approximate actuator attack, is proposed to stabilize the singular S-MJSs against actuator attack. Finally, simulation and comparison results are presented, which demonstrate the effectiveness and superiority of our method.

Considering an unpowered missile diving to intercept a maneuvering target, a novel fixed-time guidance and attitude control algorithm is proposed. First, the second-order derivatives of the aerodynamic angles are directly linked to the fin deflections, which can avoid the tracking of missile body angle rate instruction in the traditional methods. Second, a novel fixed-time time-varying parameter nonsingular terminal sliding mode guidance law is proposed, which can avoid control input singularity without switching to the polynomial form when the error approaches the origin. Third, a new global time-varying sliding mode with a predefined convergence time and analytical solution is developed to design the attitude controller. The system states are proved to be fixed-time convergence based on the Lyapunov stability theory. Six-degree-of-freedom simulations are conducted to validate the superiority of the proposed algorithm in terms of miss distance, impact angle error, interception time and control energy.

This article investigates the merging control for underactuated connected and automated vehicle (CAV) systems with mismatched uncertainty. The objective is to guarantee the safe and prescribed dynamic performance of the system during multilane merging. For CAV multidimensional motions, a strongly coupled vehicle dynamics with time-varying uncertainties is constructed. For the nominal system, the control objectives are designed as system constraints, with equality constraints guaranteeing merging and time-varying inequality constraints guaranteeing dynamic performance bounds. Based on the diffeomorphism method and bounded constraint, the system constraints are reconstructed to a unified representation. Combining system constraints and underactuated structure, the nominal controller is derived based on constraint-following. For the uncertain system, the uncertainty is decomposed into matched and mismatched portions by orthogonal decomposition. The adaptive robust controller is designed based only on matched uncertainty. Through Lyapunov minimax analysis, the control renders the errors uniformly bounded and uniformly ultimately bounded. Simulation results show that merging and platooning are effectively achieved with the proposed control. The system has excellent transient and steady state performance even in the presence of time-varying uncertainties.

In this article, the resilient event-triggered control issue for switched linear systems with the bounded disturbance is explored under the aperiodic denial-of-service (DoS) attacks. Unlike the discussions on asynchronous behavior caused by network transmission/DoS attacks, configure a copy of predictor at controller module to avert the negative impacts of asynchronous switching. Subsequently, by establishing a resilient event-triggered strategy (RETS) to eliminate invalid sampling during DoS attacks intervals, the waste of network resources is reduced. Besides, a sufficient condition is derived to ensure the input-to-state stability (ISS) for the close-loop switched systems under DoS attacks. The results indicate that the switched systems can resist a certain degree of DoS attacks under the average dwell time (ADT) constrained by DoS attacks frequency and duration. In addition, Zeno behavior is eliminated by calculating the lower bound of the triggering intervals. Finally, the availability of theoretical approach is verified through a numerical simulation.

The parameter estimation problem for the nonlinear closed-loop systems with moving average noise is considered in this article. For purpose of overcoming the difficulty that the dynamic linear module and the static nonlinear module in nonlinear closed-loop systems lead to identification complexity issues, the unknown parameters from both linear and nonlinear modules are included in a parameter vector by use of the key term separation technique. Furthermore, an sliding window maximum likelihood least squares iterative algorithm and an sliding window maximum likelihood gradient iterative algorithm are derived to estimate the unknown parameters. The numerical simulation indicates the efficiency of the proposed algorithms.