Sarah Katherine Spurgeon

Driven by the robust control requirements of complex time-delay systems in industrial processes, this paper investigates a model-free adaptive integral sliding mode predictive control algorithm for a class of nonlinear unknown time-delay systems affected by external disturbances. A one-step forward prediction error is utilized to obtain information on the delay present in the system, which is then used within the output predictor. Then, an output predictor based optimal extended parameter estimator is designed to establish the dynamic linearization model. An integral sliding mode predictive control algorithm is then proposed, which provides robustness to the impacts of the unknown time-delay and the external disturbances. The convergence of the estimated parameters and the stability of the closed-loop system are proved mathematically. Finally, the effectiveness of the proposed method is demonstrated via both simulation and experimental testing.

In this paper, a variable structure observer based decentralized sliding mode control is proposed to control a class of nonlinear interconnected systems with matched and mismatched uncertainties. A dynamic observer is initially proposed to estimate the states of the interconnected system. Then a composite sliding surface is designed, and the stability of the sliding motion is analyzed based on the regular form of the interconnected system. Using the pre‐designed observer, a dynamic decentralized output feedback sliding mode control is presented to drive the interconnected system to the designed sliding surface in finite time, and then the sliding motion is maintained thereafter. The interconnections are employed in the control design to reduce the conservatism of the developed results. The bounds of the uncertainties, which are nonlinear and take a more general form, are relaxed. Finally, a numerical practical example is presented to illustrate the advantages of this strategy.

This paper investigates a dynamic output feedback adaptive integral sliding mode predictive control (AISMPC) approach based on a dual-loop Kalman filter (DLKF) for linear discrete-time systems. The considered system class is subjected to bounded disturbances and noise, with constraints on both the inputs and states. The DLKF introduces one loop to estimate the states and another loop to estimate the disturbances. The dynamic output feedback AISMPC consists of two parts: one component is an output feedback adaptive integral sliding mode control (AISMC) to handle disturbances, and the other component is an output feedback robust model predictive control (RMPC) to achieve optimal control with constraints. It is proved that the closed-loop system is stabilized in a neighborhood of the origin. Finally, numerical simulation tests are carried out to validate the effectiveness of the proposed approach.

Distributed-drive electric vehicles (DDEVs) offer strong agility but face challenges in achieving fast dynamic response and robust stability under demanding conditions. To address this, we propose a hierarchical motion control framework that decouples path-following from attitude stabilization. A fixed-time extended state observer (FTESO) is designed to mitigate disturbance estimation errors from model inaccuracies, while a fractional-order sliding mode control (FOSMC) ensures accurate trajectory tracking. A multiobjective optimization model incorporating dynamic constraints and actuator limits is formulated and transformed into a constrained quadratic programming (QP) problem. Experiments on a 1:5 scaled chassis demonstrator validate the approach, showing a 42% reduction in lateral tracking error and sub-5-ms response time, outperforming state-of-the-art methods.

This paper investigates privacy-preserving distributed cooperative control for multi-agent systems within the framework of differential privacy. In cooperative control, communication noise is inevitable and is usually regarded as a disturbance that impairs coordination. This work revisits such noise as a potential privacy-enhancing factor. A linear quadratic regulator (LQR)-based framework is proposed for agents communicating over noisy channels, \textcolor{black}{where the noise variance depends on the relative state differences between neighbouring agents.} The resulting controller achieves formation while protecting the reference signals from inference attacks. It is analytically proven that the inherent communication noise can guarantee bounded $(ε,δ)$-differential privacy without adding dedicated privacy noise, while the \textcolor{black}{system cooperative tracking error} remains bounded and convergent in both the mean-square and almost-sure sense.

In this paper, partial output consensus (POC) based on distributed robust model predictive control (DRMPC) is investigated for multi-rate chain interconnected processes. To accommodate potential differences in sensor sampling characteristics, ‘Consensus’ and ‘Nonconsensus’ outputs (i.e. those outputs with and without a consensus target) have different sampling periods. A fusion estimation strategy (FES) is initially designed, which can utilize multi-rate measured outputs to generate state estimates in real time. Using the results of this FES, a DRMPC is then proposed that can simultaneously stabilize all the outputs. The POC cost function and consensus constraint can ensure that all subsystems meet POC requirements. The effectiveness of the proposed approach is guaranteed theoretically and further demonstrated by simulations and experimental testing.

A decentralized static output feedback sliding mode control (DSOFSMC) is proposed for a class of nonlinear interconnected systems. A novel generalized regular form framework is first developed to specifically address interconnected systems that cannot be transformed into regular form. A nonlinear sliding surface is designed using only system output variables, and a sufficient condition that ensures the asymptotic stability of the sliding mode dynamics is presented. The proposed method guarantees finite-time convergence of the system to the composite sliding surface. Three simulation examples are provided to validate the effectiveness of the method, highlighting its robustness in handling uncertainties and interconnections while achieving strong system performance.