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Nowadays, advances in different fields of technology have increased demands for reliable controllers. Uncertainty, disturbances, and limitations in control inputs are inevitable with most systems. Hence, considering them in designing a practical controller seems indispensable to any system. We propose an adaptive, robust, and finite time control technique for both multi-input multi-output (MIMO) and single-input single-output (SISO) systems. In the design of the proposed control technique, due to the undeniable existence of disturbances and control input limitations, their effects are fully taken to account. On the basis of a finite time sliding mode strategy, controllers and disturbance observers are designed. Then, the stability and finite time convergence of the proposed control scheme and disturbance observer are proven via the Lyapunov stability theory. Eventually, to investigate the performance of the proposed method in real-world applications, a hardware-in-the-loop (HIL) test is carried out for the proposed control scheme. Through numerical simulation and the results of the HIL test, the high-effective performance of the proposed controller for uncertain chaotic systems was demonstrated. Moreover, the results of the HIL test showed that by implementing continuous functions in the design of the controller, chattering, which has detrimental effects on systems, will be reduced in practical applications. Numerical simulations and the results of the HIL test bench for the modified controller clearly confirmed the effective performance of the offered control technique for practical systems. Thereby applying the proposed control technique for complex nonlinear systems subject to control input limitations, disturbances, and time-varying uncertainties will be useful. •We propose an adaptive, robust, and finite time control technique for both multi-input multi-output (MIMO) and single-input single-output (SISO) systems.•The stability and finite time convergence of the proposed control scheme and disturbance observer are proven via the Lyapunov stability theory.•To investigate the performance of the proposed method in real-world applications, a hardware-in-the-loop (HIL) test is carried out for the proposed control scheme. [Display omitted]

The book summarizes theory and applications of data-driven model-free adaptive control (MFAC) which is different from the traditional adaptive control. The traditional unmodeled dynamics do not exist in MFAC framework. In addition, MFAC is suitable for many practical applications since it is easily implemented and has strong robustness. By reading this book, readers become familiar with MFAC in a short time, and can quickly carry out their independent research and applications.

Due to the nonlinear characteristics of the valves and the interactions between the controlled variables, designing a control system for coupled tanks is a difficult task. This paper deals with the comparative study between Fuzzy-PID and GA-PID controllers for decoupling level and flow rate control of two tank systems for beverage factories with minimum time optimal operation. In most process control industries, each process requires multiple control variables. Here two input two output (TITO) systems are considered highly interacting multivariable control systems. The decoupling control scheme (Pre-compensator (dynamic) decoupling) is used to reduce the correlation between the controlled and manipulated variables by diagonalizing the system. The two independent SISO systems are further controlled by different controllers so that the system can trace the set point and yield a good time response. Two radically different control approaches are presented and compared for this system’s dynamics, motivated by a desire to provide precise liquid-level control and regulate the flow rate. MATLAB /Simulink model and tuning algorithm (GA) are used for simulation. As the simulation result ensured, the GA-PID controller is the used for the specified system which is based on the transient and steady-state specifications. Quantitatively; the GA-PID controller has 39.167ms rise time, 8.50sec settling time, and -0.393% overshoot; whereas FLC-PID has 118.101ms rise time, 8.65sec settling time, and -0.033% overshoot. But in GA with PID controllers, the external disturbance tolerance capability of the proposed scheme, meaning robustness against external disturbance has a slight difference and FLC-PID has perfectly achieved the robustness. Depending on the result, FLC-PID has more result than GA-PID controller based on the set of specifications. Hence robustness is more important than time performances.

This paper proposes a novel model-free adaptive control approach to restrain system output fluctuation and overshoot (SOFO) for a class of discrete-time nonlinear single-input single-output systems. First, historical input/output sensor data is utilized to online construct an equivalent explicit data model for control design, making the controller model-free. Second, a novel compensation input term including a compensation gain and a signum function is proposed to soften system input, which in turn restrains the SOFO. The signum function is parameterized by incremental output. The controller and adaptive law of the compensation gain are designed according to the discrete-time Lyapunov stability theories. Last, numerical simulations validate that the proposed approach can restrain the SOFO successfully and reduce response time compared with the existing approaches.

An investigation of optimal feedback controllers’ performance and robustness is carried out for vortex shedding behind a two-dimensional cylinder at low Reynolds numbers. To facilitate controller design, we present an efficient modelling approach in which we utilise the resolvent operator to recast the linearised Navier–Stokes equations into an input–output form from which frequency responses can be computed. The difficulty of applying modern control design techniques to high-dimensional flow systems is overcome by using low-order models identified from frequency responses. These low-order models are used to design optimal controllers using ${\\mathcal{H}}_{\\infty }$ loop shaping. Two distinct single-input single-output control arrangements are considered. In the first arrangement, a velocity sensor located in the wake drives a pair of body forces near the cylinder. Complete suppression of shedding is observed up to $Re=110$. Due to the convective nature of vortex shedding and the corresponding time delays, we observe a fundamental trade-off: the sensor should be close enough to the cylinder to avoid excessive time lag, but it should be kept sufficiently far from the cylinder to measure unstable modes developing downstream. These two conflicting requirements become more difficult to satisfy for larger Reynolds numbers. In the second arrangement, we consider a practical set-up with an actuator that oscillates the cylinder according to the lift measurement. The system is stabilised up to $Re=100$, and we demonstrate why the performance of the resulting feedback controllers deteriorates more rapidly with increasing Reynolds number. The challenges of designing robust controllers for each control set-up are also analysed and discussed.

In recent years, applications of inverse model-based control techniques have experienced significant growth in popularity and have been widely used in engineering applications, mainly in nonlinear control system design problems. In this study, a novel fuzzy internal model control (IMC) structure is presented for single-input-single-output (SISO) nonlinear systems. The proposed structure uses the forward and inverse dynamic Takagi–Sugeno (D-TS) fuzzy models of the nonlinear system within the IMC framework for the first time in literature. The proposed fuzzy IMC is obtained in a two-step procedure. A SISO nonlinear system is first approximated using a D-TS fuzzy system, of which the rule consequents are linearized subsystems derived from the nonlinear system. A novel approach is used to achieve the exact inversion of the SISO D-TS fuzzy model, which is then utilized as a control element within the IMC framework. In this way, the control design problem is simplified to the inversion problem of the SISO D-TS fuzzy system. The provided simulation examples illustrate the efficacy of the proposed control method. It is observed that SISO nonlinear systems effectively track the desired output trajectories and exhibit significant disturbance rejection performance by using the proposed control approach. Additionally, the results are compared with those of the proportional-integral-derivative control (PID) method, and it is shown that the proposed method exhibits better performance than the classical PID controller.

In this article, the notion of pinning control for directed networks of dynamical systems is introduced, where the nodes could be either single-input single-output (SISO) or multi-input multi-output (MIMO) dynamical systems, and could be non-identical and nonlinear in general but will be specified to be identical linear time-invariant (LTI) systems here in the study of network controllability. Both state and structural controllability problems will be discussed, illustrating how the network topology, node-system dynamics, external control inputs and inner dynamical interactions altogether affect the controllability of a general complex network of LTI systems, with necessary and sufficient conditions presented for both SISO and MIMO settings. To that end, the controllability of a special temporally switching directed network of linear time-varying (LTV) node systems will be addressed, leaving some more general networks and challenging issues to the end for research outlook.

This study presents a methodology employing Iterative Learning Control (ILC) to enhance the control performance of soft grippers equipped with multiple curvatures and variable stiffness. ILC is a learning-based control approach that progressively reduces errors in repetitive tasks, known for delivering superior performance in complex systems. In the context of the increasing utilization of robotic technology across various industries, the control technology of soft robots, especially soft grippers with multiple curvatures and variable stiffness, is a crucial issue. While prior research has focused on single-curvature and single-input single-output (SISO) systems, this study addresses the intricate control problem of multi-input multi-output (MIMO) soft gripper systems capable of multiple curvatures. It also proposes an enhanced design for soft grippers with multiple curvatures and variable stiffness while highlighting the potential of ILC for enhancing control performance.

In this paper, a novel data-driven control algorithm based on model-free adaptive control is presented, addressing general discrete-time single-input single-output nonlinear systems, approximated by an equivalent dynamic linearization model using pseudo-partial derivatives. The closed loop stability is proved, showing that the tracking error asymptotically vanishes. Moreover, the proposed approach has been applied to a 5 MW wind turbine, considering as control target the efficiency optimization issue when the turbine operates under medium wind speed conditions. Validation of the technique has been performed, testing the overall control system by simulation using the tool FAST developed by the National Renewable Energy Laboratory (NREL).

In this paper, a new output-feedback data-driven model predictive controller (MPC) is proposed for single-input single-output nonlinear dynamical systems. For this purpose, a new method for the mathematical modelling of such systems using input/output data collected from the system was developed. The proposed model is in the form of a set of linear algebraic relationships between the input and output samples, which is added as an equality constraint to the convex quadratic optimization problem used to calculate the control. The introduced model is nonlocal, meaning that it only needs to be created once at the start of the control system, and there is no need to update it using the most recent data collected from the closed-loop control system while it is running. The proposed data-driven MPC is used to control a nonlinear dynamical system, and it is shown that the closed-loop control system can track different reference inputs even at the presence of measurement noise.