Research Papers

This paper presents an analysis of state feedback control and state estimation applied to the two degrees-of-freedom (2-DOF) helicopter platform Quanser Aero 2 to achieve trajectory tracking control and stabilization. It involves establishing a dynamic model of the Quanser Aero 2 platform using the Euler-Lagrange equations of motion to obtain a MIMO system with four equations of state and two inputs. Then, the design of two state feedback controllers using full and reduced order observers, respectively, to stabilize the Aero 2 system. In practice, not all system states can be directly measured, which affects the control system. Observers can estimate these unmeasured states based on the available measurements and the system's model. Therefore, controllers with observers are implemented in realtime on the Quanser Aero 2 platform and compared to an optimal state feedback control, namely, the linear quadratic regulator (LQR). Experimental results are presented and compared to analyze the performance of 2-DOF helicopter control via state feedback and observer estimation.

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.

Sit-to-stand (STS) and sit-to-walk (STW) are vital daily tasks that affect mobility, independence, fall risk, and quality of life. Therefore, it is important to aid older adults or movement impaired individuals in performing sitting to standing transitions. As such, wearable soft robotic exosuits hold potential in assisting these movements as they can overcome the limitations posed by traditional mechanical exoskeletons. Soft exosuits work in parallel with user’s muscles, are lightweight, compliant, comfortable, and can provide variable stiffness and torque actuation. This reduces the metabolic cost of movement along with the risk of joint misalignment and musculoskeletal injury, as commonly experienced with exoskeletons. Hence, this review aims to provide an overview of the existing and recent soft robotic exosuits that aid STS and STW transitions. A general description of each device is presented while categorizing the exosuits as active and passive, and subdividing as expansive, tensile, and hybrid based on their actuation technology. The core technologies governing soft exosuits are discussed with focus on the physical human–robot interface (HRI), actuation methods, sensors, movement intention detection, and control architecture. The benefits and limitations of each type of exosuit are presented along with future prospectives and areas of improvement for these core technologies. This includes maximizing HRI stiffness while maintaining comfort and designing high hip and knee torque actuators to meet the requirements of STS and STW. Additionally, early and autonomous intention detection should be explored with advanced mid-level control architectures for estimating user-targeted joint torques, based on movement phases, execution strategies, anthropometry, and biomechanics.

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.

Industrial applications encompass a wide range of fields, including chemical plants, biomedical engineering, vibration control, and mechanical systems, among others. Frequently, controller tuning is performed using a process model as a basis. Determining such a model can be a time-consuming and challenging task, requiring proper identification. Nonlinear processes often have a limited number of relevant operating points. Autotuning methods that collect pertinent process information at specific operating points and determine controller parameters automatically streamline the controller design process by eliminating the need for process modelling. This paper presents a novel relay based autotuning methodology for Proportional Integral Derivative (PID) controllers highly suitable for Industry 5.0 applications that is easily implemented. The methodology is successfully validated on two numerical examples consisting of a highly oscillatory process and a process with large time delay. Additionally, two experimental validations are presented for a mass-spring-damper system and a 2-tank process.

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.

This paper presents the open-source stochastic model predictive control framework GRAMPC-S for nonlinear uncertain systems with chance constraints. It provides several uncertainty propagation methods to predict stochastic moments of the system state and can consider unknown parts of the system dynamics using Gaussian process regression. These methods are used to reformulate a stochastic MPC formulation as a deterministic one that is solved with GRAMPC. The performance of the presented framework is evaluated using examples from a wide range of technical areas. The experimental evaluation shows that GRAMPC-S can be used in practice for the control of nonlinear uncertain systems with sampling times in the millisecond range.

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."