Passenger comfort and vehicle stability are key aspects that must be guaranteed on ground vehicles, and semi-active suspensions have offered an outstanding solution to meet these opposite objectives. This contribution describes a novel autoregressive with exogenous input (ARX) model-based predictive control strategy handled by a driver block applied on a semi-active vehicle suspension to improve passenger comfort and road holding when compared against a passive vehicle suspension system and another more complex control designs reported in the literature. The ARX model employs a driver block to reduce the computational load of the closed-loop semi-active suspension. In addition, the controller’s formulation and the case study consider the actuator’s physical constraints to achieve more realistic results. This case-study includes a one-quarter semi-active suspension with two degrees-of-freedom, and the numerical data comes from a real magnetorheological damper characterization. The results, in frequency-domain and time-domain, are measured based on specific performance criteria. A substantial improvement against a passive suspension is quantified and discussed. For a broader perspective of the findings, the results are compared against another reported work. This research effort could be the basis of further studies to achieve more robust solutions such as adaptive/optimal predictive controllers to improve vehicle’s comfort and stability.

Many industrial processes include MIMO (multiple-input, multiple-output) systems that are difficult to control by standard commercial controllers. This paper describes a MIMO case of a class of SISO-APC (single-input, single-output adaptive predictive controller) based upon an ARX (autoregressive with exogenous variable) model. This class of SISO-APC based on ARX models has been successfully and extensively used in many industrial applications. This approach aims to minimize the barriers between the theory of predictive adaptive control and its application in the industrial environment. The proposed MIMO-APC (MIMO adaptive predictive controller) performance is validated with two simulated processes: a quadrotor drone and the quadruple tank process. In the first experiment the proposed MIMO APC shows ISE-IAE-ITAE performance indices improvements of up to 25%, 25.4% and 38.9%, respectively. For the quadruple tank process the water levels in the lower tanks follow closely the set points, with the exception of a 13% overshoot in tank 1 for the minimum phase behavior response. The controller responses show significant performance improvements when compared with previously published MIMO control strategies.

Hybrid systems are those that inherently combine discrete and continuous dynamics. This paper considers the hybrid system model to be an extension of the discrete automata associating a continuous evolution with each discrete state. This model is called the hybrid automaton. In this work, we achieve a mathematical formulation of the steady state and we show a way to obtain the initial conditions region to reach a specific limit cycle for a class of uncoupled and coupled continuous-linear hybrid systems. The continuous-linear term is used in the sense of the system theory and, in this sense, continuous-linear hybrid automata will be defined. Thus, some properties and theorems that govern the hybrid automata dynamic behavior to evaluate a limit cycle existence have been established; this content is explained under a theoretical framework.