Research Papers

This paper proposes a novel real-time affordable solution to the trajectory tracking control problem for selfdriving cars subject to longitudinal and steering angular velocity constraints. To this end, we develop a dual-mode Model Predictive Control (MPC) solution starting from an input-output feedback linearized description of the vehicle kinematics. First, we derive the state-dependent input constraints acting on the linearized model and characterize their worst-case time-invariant inner approximation. Then, a dual-mode MPC is derived to be realtime affordable and ensuring, by design, constraints fulfillment, recursive feasibility, and uniformly ultimate boundedness of the tracking error in an ad-hoc built robust control invariant region. The approach’s effectiveness and performance are experimentally validated via laboratory experiments on a Quanser Qcar. The
obtained results show that the proposed solution is computationally affordable and with tracking capabilities that outperform two alternative control schemes.

Hands-on experiences in engineering education are highly valued by students. However, the high cost, large size, and non-portable nature of commercially available laboratory equipment often confine these experiences to lab courses, separating practical demonstrations from classroom teaching. Consequently, mechanical engineering students may experience a delay in practical engagement as lab sessions typically follow theoretical courses in subsequent semesters, a sequence that differs from mechatronics, electrical, and computer engineering programs. This study details the design and development of portable and cost-effective control lab equipment that enables in-class demonstrations of a proportional-integral-derivative (PID) controller for the trajectory and speed control of a DC motor using MATLAB Simulink, as well as disturbance control. The equipment, composed of a DC motor, beam, gears, crank, a mass, and propellers, introduces disturbances using either propellers or a rotating unbalanced mass. All parts of the equipment are 3D printed from polylactic acid (PLA). Furthermore, the beam holding the propellers can be attached to Quanser Qube lab equipment, which is widely used in control laboratories. The lab equipment we present is adaptable for demonstrations, classroom projects, or as an integral part of lab activities in various engineering disciplines.

Collaborative haptic training systems offer numerous benefits, including enhanced safety, streamlined training processes, and improved maneuverability. These systems typically involve an expert user (the trainer) and a novice user (the trainee) engaging in collaborative operations. One of the primary challenges in designing controllers for such systems is ensuring task stability and maintaining stable interaction between the operators and the system, while also achieving satisfactory task performance. However, existing control schemes often overlook the need for supervision and intervention by the trainer during the operation, along with a comprehensive stability analysis. This article aims to address the above issues for a system in which the trainee conducts the operation and the trainer is provided with the capability to intervene and modify the incorrect actions of the trainee. This is accomplished through the implementation of impedance controllers at each haptic interface and dynamic adjustment of the target impedance on both ends based on the trainer’s estimated impedance gain. The Input-to-State Stability approach and the small gain theorem are employed to analyze the stability of the closed-loop system. The effectiveness of the proposed approach is demonstrated through simulation and experimental results, showcasing the ability of the proposed scheme to enhance the collaborative training process and ensure stable interaction between the trainer and the trainee.

The present paper proposes the application of global and exact differentiators with dynamic gains based on higher-order sliding modes (HOSM) to the design of adaptive backstepping control for nonlinear uncertain systems of strict-feedback type. The use of this kind of differentiator in the closed-loop system allow us to guarantee global uniform stability (for any initial conditions) due to the variable nature of the dynamic gain. The dynamic gain can grow or decrease with the unmeasured state. In addition, asymptotic output tracking is also assured. In order to illustrate the results of the new theorem, the proposed controller is applied to a high-performance aircraft system, suppressing the wing-rock phenomenon usually observed for fast-speed flight conditions. Comparison results with a linear-inexact differentiator, a local HOSM differentiator with fixed gains and the proposed global and exact HOSM differentiator with dynamic gain shows the superiority of the latter over the former approaches. To demonstrate the practical efficacy of the proposed approach, we conclude with an experimental test featuring a DC motor and the novel differentiator-based backstepping control scheme."

This paper proposes an adaptive constrained control considering input saturation and prescribed performance constraints for uncertain two-degree-of-freedom helicopter systems with actuator faults. A devised performance function precisely indicates the time for the system to achieve stability and adjusts the convergence speed of its dynamic process through parameter modifications. The smoothing Gaussian error function is introduced to replace the saturated nonlinearity of each non-smooth actuator, the radial basis function neural network is employed for approximating the unknown nonlinear function, adaptive auxiliary parameters are introduced, and an adaptive controller is designed to address issues such as model uncertainty, input saturation, and actuator faults arising in practical applications. Furthermore, the Lyapunov direct method is employed to demonstrate the stability and boundedness of the helicopter system. Results obtained from simulations and experiments on the Quanser two-degree-of-freedom helicopter experimental platform confirm the effectiveness and feasibility of the proposed control method.

This study investigates a novel tracking control with prescribed performance for an uncertain nonlinear 2-DOF helicopter subject to actuator failure. The control design aims to address partial actuator faults, and to mitigate their effects, an adaptive auxiliary variable is introduced. Additionally, a neural network is employed to accurately approximate the system uncertainties. The prescribed performance is formulated as time-varying constraints, and the barrier Lyapunov function (BLF) technique is applied to achieve the constrained control. Finally, the stability analysis of the closed-loop system is provided, and the validity of the proposed control scheme is illustrated through a simulation study on the 2-DOF helicopter system.

This paper is centered on the adaptive asymptotic tracking control of multi-input multi-output uncertain strict-feedback nonlinear systems in the presence of both multiplicative and additive sensor faults. Through adaptive accommodation techniques, the proposed control scheme can dynamically compensate for sensor faults, thereby ensuring precise and stable control. The control scheme is devised using the backstepping design that incorporates positive integrable time-varying functions, thereby ensuring the boundedness of all signals in the closed-loop system and guaranteeing that the output tracking error asymptotically converges to zero. Additionally, extensive simulations and experimental evaluations conducted on the Quanser platform with 2-link flexible-joint robots demonstrate the efficacy of the proposed control scheme in mitigating the impact of sensor faults and achieving accurate tracking performance.

In this article, an observer-based adaptive non-singular fast-reaching terminal sliding mode control strategy is proposed to tackle the problem of actuator faults and uncertain disturbance in aerial robot systems. Firstly, a model of an aerial robot system is established through dynamic analysis. Next, an adaptive observer, combined with a fast adaptive fault estimation (FAFE) algorithm, is proposed to estimate system states and actuator failure and compensate for faults in a precise and prompt manner. In addition, a non-singular fast terminal sliding surface is defined, taking into account the fast convergence of the tracking errors in order to provide appropriate trajectory tracking results. Since the upper bounds of the disturbances caused by the manipulator of the system in practice are unknown, the control approach may benefit from the addition of an adaptive control strategy that can suppress the influence of uncertain disturbances. The Lyapunov stability theory demonstrates that tracking errors are able to converge stably and quickly. In the end, the contrast experiment is conducted to exhibit the effectiveness of the proposed control strategy. The results demonstrate quicker convergence and improved estimating accuracy.