ABSTRACT This paper proposes a cascade multiple‐input–multiple‐output (MIMO) control framework for trajectory tracking of hydraulic robotic manipulators with multi‐closed‐loop kinematic chains. A dynamic modeling method integrating screw theory with reduced system dynamics is first developed to derive standard Lagrangian equations of motion for such manipulators. As large flow‐rate cartridge valves or combined flow control valves are commonly employed in these systems for heavy‐duty operation, a non‐affine nonlinear model is formulated to characterize the hydraulic actuation dynamics. A cascade control algorithm is then proposed, integrating a MIMO approximate nonlinear internal model with a feedback controller to achieve accurate trajectory tracking. The proposed approach tightly couples a computationally efficient screw‐theory‐based dynamic model of the multi‐closed‐loop mechanism with a MIMO approximate internal model control (AIMC) structure tailored to the system's non‐affine actuation characteristics. Whereas existing IMC implementations have predominantly focused on SISO or input‐affine systems, this paper develops a systematic methodology for deriving and implementing discrete‐time MIMO AIMC for non‐affine hydraulic systems, providing a principled framework for handling the inherent nonlinearities and model uncertainties of hydraulic manipulators with non‐affine valve actuation. Experimental results confirm that the proposed controller achieves high trajectory tracking performance on the hydraulic multi‐closed‐loop manipulator platform.

ABSTRACT In this paper, we consider the optimal control problem for an unknown continuous‐time nonlinear system, and present a framework that integrates model‐based and model‐free methods to solve it. Each approach offers distinct advantages: model‐based techniques provide offline synthesis and data efficiency, while model‐free procedures excel at accuracy and online learning capabilities. To harness the strengths of both approaches, we propose a control design that starts with a controller derived from an available linear model, and is subsequently augmented with a learned (model‐free) controller component. Specifically, we derive an optimality‐based condition that determines the switching instant from the purely model‐based controller to the composite controller, which includes both model‐based and model‐free components. Importantly, we dispense with the assumption of a known degree of misalignment between the model and the system; the switching condition accounts for real‐time model mismatch, ensuring that a more inaccurate model results in a faster switching time. Finally, to obtain the model‐free component in the composite controller, we employ an off‐policy reinforcement learning (RL) algorithm that uses trajectory data to learn the optimal control augmentation. Simulations demonstrate the efficacy of the proposed control framework.

ABSTRACT The optimal control with an ergodic cost function is investigated for a class of stochastic discrete‐time linear systems with unknown system coefficient matrices. We present an online value iteration‐based stochastic adaptive dynamic programming algorithm to obtain the approximation sequence of the optimal state feedback control gain and rigorously prove that the approximation sequence converges to the optimal state feedback control gain under some reasonable conditions. In the framework of direct adaptive control, the diminishing excitation technique is introduced for an unknown stochastic discrete‐time state space model, and we prove that the adaptive controller, which is given by real‐time computation, is optimal. In contrast to existing related results, our method does not need to collect enough data to solve the Bellman equation in each iteration and avoids the requirement to estimate means and variances of controllers or states. A numerical simulation is provided to evaluate the efficacy of the presented approach.

ABSTRACT This paper studies the nonlinear event‐triggered tracking control problem with sensor faults and full state constraints. First, an adaptive compensation law is constructed to handle sensor fault. Next, a first‐order filter is used to reduce the computational complexity caused by the derivation of the virtual control law. Finally, a novel dynamic event‐triggered mechanism (ETM) using an unscented Kalman filter (UKF) is designed to minimize the number of triggers. In addition, barrier Lyapunov functions (BLFs) are introduced to ensure state constraints. Simulation results confirm the effectiveness of the proposed scheme.

ABSTRACT This paper focuses on the problem of optimized fuzzy prescribed performance control for nonlinear strict‐feedback systems under denial‐of‐service (DoS) attacks. DoS attacks disrupt communication channels, leading to the output signal and states of the system unavailable. A switched fuzzy observer is adopted to reconstruct unmeasurable states under DoS attacks. Simultaneously, a simpler prescribed performance error transformation is constructed to constrain the tracking error within a prescribed boundary, which can improve the transient and steady‐state performance of the control system. To achieve optimized fuzzy prescribed performance control under DoS attacks, an event‐based secure optimal control strategy is proposed. Specifically, the controller at each backstepping layer is designed as the optimal solution for the corresponding subsystem, such that the entire backstepping control process is optimized. Due to the difficulty in directly solving optimal solutions, a reinforcement learning (RL) algorithm with an actor‐critic architecture is incorporated into the control design process to obtain approximate optimal controllers. In addition, a dynamic event‐triggering mechanism (DETM) is proposed to improve the utilization rate of the communication resource. Through Lyapunov stability analysis, it is proved that the tracking error always evolves within the performance envelope, and the consumed control resources are minimized. Meanwhile, the Zeno behavior can be effectively eliminated. Finally, the effectiveness of the proposed strategy is verified via a simulation example.

ABSTRACT This article considers the robust sparse identification problem of linear parameter‐varying (LPV) systems within the framework of the variational Bayesian (VB) algorithm. Considering that the order of the LPV model and the dependencies between the parameters and the scheduling variable are unknown, an over‐parameterization approach is adopted to construct the model representation. As a result, this leads to model redundancy and sparsity. To address this issue, the spike‐and‐slab (SS) prior is introduced to characterize the model parameters, enabling the acquisition of a sparse solution and thereby determining the true orders and structure of the LPV model. Subsequently, an auxiliary model is utilized to estimate the noise‐free output within the information vector. Additionally, a Student's t distribution is employed to handle outliers in the measured output, leading to a robust sparse identification algorithm. Finally, the proposed identification algorithm is effectively validated through a simulation example and the cascaded tank system.

ABSTRACT The precise trajectory tracking is one of the most important topics in the climb and descent operations for aircraft in the dense terminal airspace. It facilitates the accurate execution of the planned trajectory, maintenance of safety separation, and improvement of airspace utilization. This paper aims to solve the aircraft trajectory tracking problem by proposing a class of modeling and learning‐based control methods. A nonlinear, time‐varying aircraft model is constructed under repetitive horizontal wind and non‐repetitive turbulence, which is further converted into a linear form through a virtual input transformation. By resorting to the repetitive execution of trajectory tracking tasks in aircraft climb and descent operations, an adaptive iterative learning control (ILC) method is developed. Then, the ILC convergence analysis is constructed based on a composite energy function. As a result, precise trajectory tracking can be achieved under both repetitive and non‐repetitive disturbances as the iteration number increases. A simulation based on the Continuous Climb Operation and Continuous Descent Operation procedures of Guangzhou Baiyun International Airport is conducted and the results verify the feasibility and efficacy of the proposed algorithm.

ABSTRACT This paper proposes an output‐feedback tracking controller for non‐minimum phase nonlinear systems with unknown uncertainties and external disturbances, where not all states are measurable, and the zero dynamics are unstable. The approach combines a backstepping‐based stabilizing state‐feedback law with a cascade extended high‐gain observer (EHGO): A fast observer estimates the measured output and its derivatives to generate a virtual output, while a slower observer estimates the unmeasured controlled output, its derivatives, and the lumped disturbance and uncertainty. For relative‐degree‐two systems, the controller exhibits a PID‐like control. This feature is rarely achieved for non‐minimum phase systems. Simulations of a wafer‐handling robot modeled as a flexible‐joint robot demonstrate accurate tracking, rapid disturbance rejection, smooth inputs, and improved robustness compared to existing methods.

ABSTRACT The well‐known offline robust model predictive control (RMPC) effectively addresses uncertain parameters for autonomous vehicle systems. However, it requires memory to save the variable and more computation time to search the lookup table to find the best values of the weighting matrices. To address these issues, this article introduces a new offline RMPC approach for enhancing the steering control of autonomous vehicles (AVs) using linear matrix inequality (LMI) optimization that can eliminate storing variables and lookup tables. In order to effectively handle the potential uncertainties associated with the vehicle system parameters, such as total vehicle mass, front and rear cornering stiffness, and varying vehicle speeds by confining them within a defined polytope. By utilizing the polytopic uncertainty method, the offline RMPC is designed to handle a wide range of parameter variations within the defined polytope. Specifically, by defining the upper bound of the cost function as a quadratic form, an offline RMPC method is directly designed, resulting in a significant reduction in the computation burden. Moreover, optimal control is designed by solving an optimization problem based on LMI, allowing the use of efficient convex optimization algorithms. Input and output constraints are enforced to guarantee the safe operation of the AV system in complex and dynamic environments. Finally, the simulation results are presented and compared with the existing studies to demonstrate the effectiveness of our approach. Additionally, co‐simulation based on Matlab and Simulink and CarSim software were also conducted to further verify the proposed method. The results show that all state variables satisfy the enforced constraints, and both the path‐tracking trajectory performance and execution time are better than those of recent research.

ABSTRACT A sliding mode–based adaptive control law is proposed for a class of diffusion processes featuring a spatially‐varying uncertain diffusivity and equipped with several point‐wise actuators located at the two boundaries of the spatial domain as well as in its interior. The system is additionally perturbed by matched disturbances which are assumed to be uniformly bounded along with their time derivatives. The corresponding constant bounds are unknown , thus motivating the use of an adaptive control strategy. To achieve global asymptotic stability of the origin in the ‐sense, a control law consisting of a proportional and discontinuous term is proposed. The gain of the discontinuous term is continuously adjusted according to a gradient‐based adaptation algorithm. The stability and convergence analysis is Lyapunov‐based and it constructively yields simple tuning conditions for the controller gain parameters. Simulation results are finally discussed to support the theoretical findings.