This paper addresses the robust model predictive control problem for a class of networked control systems with polytopic uncertainties and hard constraints, where the controller design is complicated by the joint presence of an energy harvesting sensor in the forward channel and the round-robin protocol in the backward channel. In such a setting, stochastic transmission behavior caused by random energy availability, together with fixed communication scheduling and immeasurable states, makes it difficult to guarantee recursive feasibility of the online optimization and mean-square stability of the closed-loop system. To capture these features, the mathematical expectation of a quadratic function depending on both the sensor energy level and the transmission order over an infinite horizon is constructed to formulate the optimization problem. In order to cope with the terminal constraint set and the immeasurability of system states, an auxiliary optimization problem with guaranteed solvability is developed by employing inequality analysis and slack-matrix techniques, through which a sub-optimal solution is obtained. Furthermore, sufficient conditions are derived to ensure the recursive feasibility of the proposed algorithm and the mean-square stability of the resulting closed-loop system with and without hard constraints. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed method.

In the context of new energy vehicles, it is common for the compressor to be directly powered by an electric motor. However, the demanding operational circumstances frequently lead to substantial variations in motor speed. This work presents a novel approach for the speed control system of air conditioning compressors, which involves the utilization of a virtual unmodeled dynamics robust predictive control with time-delay compensation. The initial step involves analyzing the conversion mechanism between the angular velocity of the compressor and the duty ratio, while considering the delay effects resulting from inertia. Subsequently, a state space model is constructed, incorporating unmodeled dynamics. In addition, in light of the uncertain attributes of the unmodeled dynamics, a signal processing function is formulated for the unmodeled dynamics through a time compensation data strategy and an interval differential compensation strategy. Furthermore, a robust predictive controller with time delay and unmodeled dynamics compensation techniques is presented. This controller integrates an unmodeled dynamics time compensation unit and a differential compensation unit, utilizing adaptive backstepping control design techniques. Namely, the control signal is compensated using the unmodeled dynamics signal processing function. The effectiveness and feasibility of the suggested technique are evaluated by simulation and real operation on an air conditioning system platform.

This study formulated an iterative learning-based predictive control strategy for asynchronous switching of multiphase batch processes with complex characteristics in the framework of a two-dimensional (2-D) system. First, we constructed a Fornasini-Marchesini comprehensive feedback error model, considering the state deviation and output error. Using this model, we developed a switching model considering the match and mismatch cases. Furthermore, an iterative learning-based predictive control mechanism was designed for asynchronous switching with a greater freedom of adjustment and fast learning ability in the batch direction. Second, the asymptotic and exponential stability were discussed based on the related methods and theories, and the system stability conditions were expressed in the form of linear matrix inequality (LMI). Following an online mechanism to determine the LMI conditions, we derived the real-time optimal gains of the control law, the maximum dwell period (Max-DT) for the mismatch case, and the minimum dwell period for the match case. The switching signal was transmitted in advance according to the Max-DT to ensure the stability of the system during switching. Finally, the effectiveness of the proposed method was confirmed by utilizing the injection molding process.

Asynchronous switching between the controller and the active subsystems in multiphase batch processes may cause the systems to be unstable around the switching instants. In view of this, an average dwell-time method-based iterative learning control (ILC) scheme is proposed in this paper. First, the multiphase process is represented as an equivalent closed-loop two-dimensional (2-D) switched system composed of stable and unstable subsystems, based on which new relevant concepts on the stability of the switched system are given. Second, using an average dwell-time method, the ILC law is designed to guarantee the system exponentially stable. Minimum running time for the stable subsystems and maximum running time for the unstable ones are obtained. Lastly, depending on the maximum time for the unstable subsystems, the idea of putting the controller switching step forward is proposed. In this way, the asynchronous switching is removed such that the unstable subsystem can be avoided. The case study on an injection molding process demonstrates the effectiveness and superiority of the proposed method in comparison with the existing 2D-MPC and one-dimensional traditional control methods.

A robust fuzzy predictive control (RFPC) based on Takagi-Sugeno (T-S) fuzzy model is proposed for systems with uncertainties, time-varying delays, unknown disturbances, as well as strong nonlinearity. First, the T-S fuzzy model is built by a number of linear submodels and nonlinear membership functions. Then, by introducing the output tracking error to this fuzzy model, the novel augmented state space model is presented to independently regulate the process state variables and output tracking error. The control law of the proposed RFPC is further designed based on this extended model, which can guarantee the process state to be fast convergent and make the process output track the set-point well. Moreover, it increases the ability of adjustment for the proposed controller. Utilizing Lyapunov-Krasovskii method, optimized control theory, and control method, the stable sufficient conditions are given for the designed control law to make sure the asymptotical stability of the nonlinear uncertain system with the time-varying delay and unknown disturbances. The gains of the controller can be obtained by solving these stabilized conditions in form of linear matrix inequality constraints. At last, a case study of continuous stirred tank reactor manifests that the proposed RFPC method can bear a larger range of time delay, overcome the uncertainties and unknown disturbances well, and have better tracking performance.

The leakage aperture cannot be easily identified, when an oil pipeline has small leaks. To address this issue, a leak aperture recognition method based on wavelet packet analysis (WPA) and a deep belief network (DBN) with independent component regression (ICR) is proposed. WPA is used to remove the noise in the collected sound velocity of the ultrasonic signal. Next, the denoised sound velocity of the ultrasonic signal is input into the deep belief network with independent component regression (DBN ICR ) to recognize different leak apertures. Because the optimization of the weights of the DBN with the gradient leads to a local optimum and a slow learning rate, ICR is used to replace the gradient fine‐tuning method in conventional DBN for improving the classification accuracy, and a Lyapunov function is constructed to prove the convergence of the DBN ICR learning process. By analyzing the acquired ultrasonic sound velocity of different leak apertures, the results show that the proposed method can quickly and effectively identify different leakage apertures.

In this study, a predictive-p control system is developed for the level process in an industrial coke fractionation tower. Such processes typically have integrating and nonlinear dynamics causing the performance of conventional control designs and tuning to be poor or to require significant effort in practice. The process model is derived using data of step-response test and control implementation is designed through a new state-space structure. The closed-loop control system contains both the improved predictive control and P control. The performance of the proposed control for regulatory/servo, disturbance rejection, and measurement noise problems are studied and the obtained results show that the control system is of simple implementation with more robustness and provides better responses than conventional predictive control.

A novel interval type-2 fuzzy controller architecture is proposed to resolve nonlinear control problems of vehicle active suspension systems. It integrates the Takagi-Sugeno (T-S) fuzzy model, interval type-2 fuzzy reasoning, the Wu-Mendel uncertainty bound method, and selected optimization algorithms together to construct the switching routes between generated linear model control surfaces. The stability analysis of the proposed approach is presented. The proposed method is implemented into a numerical example and a case study on a nonlinear half-vehicle active suspension system. The simulation results demonstrate the effectiveness and efficiency of the proposed approach.

An extended fuzzy logic system (EFLS) based on interval fuzzy membership functions is proposed for covering more uncertainty in practical applications. With the degree of uncertainty in fuzzy membership functions, interval fuzzy membership functions are self-generated to include uncertainties which occur from understanding linguistic knowledge and fuzzy rules in fuzzy methods. A novel adaptive strategy is designed to self-tune the interval fuzzy membership functions and to deduce the crisp outputs with feedback structure. An inverse kinematics modelling study based on a two-joint robotic arm has demonstrated that proposed EFLS outperforms conventional fuzzy methods.

Elicited from the least means squares optimal algorithm (LMS), an adaptive fuzzy logic controller (AFC) based on interval type-2 fuzzy sets is proposed for vehicle non-linear active suspension systems. The interval membership functions (IMF2s) are utilized in the AFC design to deal with not only non-linearity and uncertainty caused from irregular road inputs and immeasurable disturbance, but also the potential uncertainty of expertpsilas knowledge and experience. The adaptive strategy is designed to self-tune the active force between the lower bounds and upper bounds of interval fuzzy outputs. A case study based on a quarter active suspension model has demonstrated that the proposed type-2 fuzzy controller significantly outperforms conventional fuzzy controllers of an active suspension and a passive suspension.

This paper reviews computational-intelligence-involved approaches in active vehicle suspension control systems with a focus on the problems raised in practical implementations by their nonlinear and uncertain properties. After a brief introduction on active suspension models, the paper explores the state of the art in fuzzy inference systems, neural networks, genetic algorithms, and their combination for suspension control issues. Discussions and comments are provided based on the reviewed simulation and experimental results. The paper is concluded with remarks and future directions.