In the context of an optimal control analysis of sediment transport, an initial bed profile for a rectangular channel that reduces sediment deposition while ensuring the stability of velocity and water depths has been identified. This optimization effectively balances the reduction of sediment accumulation and the maintenance of stable hydrodynamic conditions.

This paper presents an observer-based optimal LQR controller utilizing a Nash differential game approach for design and optimize the observer gains. The proposed method addresses systems with unknown model uncertainties by formulating the problem within a differential game framework. An observer is designed to estimate the system states while ensuring robustness against uncertainties, eliminating the need for solving Linear Matrix Inequalities (LMIs). The control strategy is derived using an optimal LQR approach via utilizing the proposed observer. This method effectively handles unknown initial conditions, enhances system performance, and provides a more resilient estimation and control framework.

The simulated curves of trajectory tracking and stability control with a external disturbance on the output of φ.

A continuous nonlinear model predictive control (NMPC) framework is developed for hypersonic reentry vehicles to track the nominal trajectory. Implementability is improved through integration of a feasibility governor for terminal set constraints and linear matrix inequality (LMI) for terminal costs.

Optimal Adaptive Back-Stepping SMC via TS Fuzzy Model for Uncertain Nonlinear Systems.

This study explores the existence of mild solutions and optimal control strategies for fractional-order semilinear control systems using resolvent operators. By leveraging advanced mathematical tools such as Banach's Fixed Point Theorem, Gronwall's Inequality, and semigroup theory, we establish sufficient conditions for solution uniqueness. The findings contribute to the theoretical and practical advancements in control theory, with applications spanning engineering, biological models, and other scientific disciplines.

This article introduces a novel sensitivity-based algorithm for nonlinear distributed model predictive control. The algorithm requires only local computations with one neighbor-to-neighbor communication step per iteration and exhibits a linear order of convergence under suitable conditions. Based on this result, stability for the model predictive control scheme with a fixed number of algorithm iterations is shown which is critical for real-time applications.

This paper investigates the switched nonlinear systems subject to mismatched disturbance and uncertain nonlinear function. The inverse optimization control method is employed to achieve the optimal performance of the system. Then, the predefined-time inverse optimal controller is designed based on the adaptive and fuzzy control theory, which can ensure the closed-loop systems are practically predefined-time stable while minimizing the cost function. Finally, the effectiveness and practicality of the paper are verified through a practical example.

This article addresses the parameter estimation problems of bilinear output-error systems, and the auxiliary model identification idea and the particle filtering technique are adopted to overcome the identification obstacle resulting from the unknown true outputs. Then a particle filtering-based forgetting factor stochastic gradient algorithm is proposed for the identification of bilinear output-error systems. To enhance the convergence rate and accuracy of parameter estimation, we expand the scalar innovation to an innovation vector and develop a particle filtering-based multi-innovation forgetting factor stochastic gradient algorithm. Finally, a numerical example and a practical continuous stirred tank reactor process are provided to show that the discussed methods are work well. The results indicate that the proposed algorithms are effective for identifying the bilinear output-error systems and can generate more accurate parameter estimates than the auxiliary model-based forgetting factor stochastic gradient algorithm.

This study proposes a fault-tolerant sliding-mode control strategy for discrete-time switched singular systems with time delays under arbitrary switching signals. By designing a sliding-mode surface and using Lyapunov-Krasovskii functions with Wirtinger-based inequalities, the approach ensures asymptotic admissibility and $$$ {H}_{\infty } $$$ performance via linear matrix inequalities. A fault-tolerant sliding-mode control law is synthesized to maintain system stability within a bounded sliding-mode region and to maintain them thereafter. Numerical examples validate the method’s effectiveness and highlight its advantages over existing approaches.

This paper investigates the linear minimum mean square error estimation for the two-dimensional Markovian jump linear system with time-correlated multiplicative noises and energy harvesting sensor. Two novel types of estimators with Markov jump parameters are designed by introducing several new terms, including the estimates for the product of multiplicative noise and state as well as the corresponding error covariance. The boundedness of the filtering error covariance with transition probability and probability distributions of the Markov chains is analyzed under natural assumptions.

Flowchart of the CCOA.

A novel Learning-based MPC framework utilizing Neural ODEs to address model mismatch in nonlinear systems. The approach combines residual modeling with first-principles uncertainty bounds, effectively decoupling safety and performance considerations. The online dynamic learning framework continuously refines model accuracy while maintaining recursive feasibility and stability with reduced memory requirements.

Technical Roadmap.