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This paper investigates the secure optimal tracking control problem for cyber-physical systems, in which the controller-actuator channel is jammed by denial-of-service attacks. Without leveraging model knowledge, learning-based tracking control algorithms are proposed by utilizing the measured input-output data. The impact of malicious attacks on tracking performance is discussed. More specifically, an augmented system consisted of the system model and the reference model is derived. Then the states of the augmented system are rewritten by utilizing the input, output, and reference trajectory, following which both the Bellman equation and algebraic Riccati equation are given. The conditions of existence and uniqueness of the solution to the algebraic Riccati equation are derived. Furthermore, both policy iteration and value iteration learning-based tracking control algorithms are provided for cyber-physical systems against denial-of-service attacks, and the convergence of the algorithms is also proved. Finally, a DC motor system and a numerical example are given to illustrate the effectiveness of the proposed control algorithms. [ABSTRACT FROM AUTHOR]

To solve the control problem of time-varying state-scale nonlinear systems whose initial state is not affected by settling time, fixed-time convergence algorithms are proposed for first-order systems and higher-order systems in this paper. First, a scalar model is used to illustrate how the time-varying feedback parameter can guarantee that the system achieves asymptotic stability while achieving finite-time convergence, and it is proved that the settling time obtained in this paper is only related to the prescribed boundary. This allows us to design the settling time with an appropriate parameter based on the prescribed boundary. To exhibit the effectiveness and extensibility of the proposed algorithm for first-order scalar systems, the results are subsequently extended to general higher-order systems based on the backstepping method. By introducing numerical simulation results, this paper verifies that the proposed algorithm will make the system achieve asymptotic stability and its output can converge to a given boundary, regardless of the system's initial states. [ABSTRACT FROM AUTHOR]

This paper applies iterative learning control to point-to-point tracking problems with a general networked structure. The data is quantised and transmitted through restricted communication channels from the controller to the actuator. Combining a logarithmic quantizer with an encoding and decoding mechanism to quantise the input signals reduces the influence of the quantisation error. New design algorithms are developed with conditions for convergence of the tracking error and an extension to fault-tolerant performance under actuator failures. A numerical-based case study demonstrates the application of the new designs, which includes a comparison with another ILC law and the relative merits of the encoding and decoding schemes. [ABSTRACT FROM AUTHOR]