Search

This study focuses on the learning and control issues of strict-feedback systems with full-state constraints. To achieve learning capability under constraints, transformation mapping is utilized to convert the original system with full-state constraints into a quasi-pure-feedback unconstrained system. Utilizing the system transformation technique, only a single neural network (NN) is required to identify the unknown dynamics within the transformed system. Combining the dynamic surface control design, a novel adaptive neural control scheme is developed to ensure that all closed-loop signals are uniformly bounded, and every system state remains within the predefined constraint range. In addition, the precise convergence of NN weights is further transformed into an exponential stability problem for a category of linear time-varying systems under persistent excitation conditions. Subsequently, the converged NN weights are efficiently stored and utilized to create a learning controller to achieve better control performance while abiding by the full-state constraints. The viability of this control strategy is demonstrated via simulations. [ABSTRACT FROM AUTHOR]

This paper studies learning from adaptive neural control of output-constrained strict-feedback uncertain nonlinear systems. To overcome the constraint restriction and achieve learning from the closed-loop control process, there are several significant steps. Firstly, a state transformation is introduced to convert the original constrained system output into an unconstrained one. Then an equivalent n-order affine nonlinear system is constructed based on the transformed unconstrained output state in norm form by the system transformation method. By combining dynamic surface control (DSC) technique, an adaptive neural control scheme is proposed for the transformed system. Then all closed-loop signals are uniformly ultimately bounded and the system output tracks the expected trajectory well with satisfying the constraint requirement. Secondly, the partial persistent excitation condition of the radial basis function neural network (RBF NN) could be verified to achieve. Therefore, the uncertain dynamics can be precisely approximated by RBF NN. Subsequently, the learning ability of RBF NN is achieved, and the knowledge acquired from the neural control process is stored in the form of constant neural networks (NNs). By reutilizing the knowledge, a novel learning controller is established to improve the control performance when facing the similar or same control task. The proposed learning control (LC) scheme can avoid repeating the online adaptation of neural weight estimates, which saves computing resources and improves transient performance. Meanwhile, the LC method significantly raises the tracking accuracy and the speed of error convergence while satisfying of the constraint condition simultaneously. Simulation studies demonstrate the efficiency of this proposed control scheme. • A useful constraint transformation is applied with self-defined boundaries. • Just one NN approximator is required, which simplifies control design complexity. • Learning is realized and unknown dynamics are accurately approximated by RBF NN. • A learning controller is established using the acquired knowledge from control. • No additional online adjustment of NN weights is required, which saves resources. [ABSTRACT FROM AUTHOR]

As is well known, traditional PID controllers are the most widely used type of controllers in industrial control due to their simplicity and other advantages. However, the existing research results on PID controllers lack a general theory for addressing the nonlinearity and uncertainty present in practical systems. In the era of intelligent control, it is highly valuable to design an intelligent PID controller with learning capability for complex unknown nonlinear systems. In this paper, we consider an extended form of PID controller based on deterministic learning (DL) for high-order nonlinear systems with unknown dynamics. The introduction of neural networks (NNs) provides an effective tool to compensate for the composite unknown nonlinearities in controlled systems, overcoming the limitations of traditional PID controllers relying solely on feedback mechanisms to combat unknown nonlinearities. Based on the Lyapunov theory, it not only ensures the stability and tracking control of the system but also simplifies the selection of controller parameters. Furthermore, based on the DL theory, the composite unknown nonlinear dynamics can be accurately compensated and approximated, and the acquired knowledge can be stored in a constant-value neural network. When encountering similar control tasks, the acquired knowledge can be quickly accessed to construct a knowledge-based extended PID controller, thereby improving the overall control performance. Simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]