This paper describes the design and successful implementation of a constrained model predictive controller with integral action for the control of a Twin Rotor MIMO System (TRMS). The integral action guarantees zero steady-state error in set-point tracking with robustness toward perturbations of the nominal system parameters. In addition to saturation constraints on the input variables, hard constraints are imposed on the controlled output variables, i.e., on pitch and yaw angular positions, to avoid collisions with obstacles. The model predictive controller was designed using a high-fidelity nonlinear model of the TRMS developed in previous work. As an intermediate step, exact linearized models of the TRMS are obtained and their closed-form expressions are reported. The controller was tested experimentally, also showing its effectiveness in ensuring actual collision avoidance by the TRMS when physical obstacles were present.

This paper proposes synthesis algorithms for the design of passive state- and output-feedback fault-tolerant controllers. Sufficient conditions for the existence and the construction of such fault-tolerant controllers are given in terms of linear matrix inequalities (LMIs) which can be solved efficiently. The state-feedback fault-tolerant controller consists of a family of state-feedback gains switched appropriately according to a stabilizing switching signal so that the closed-loop system satisfies a performance requirement expressed in terms of system L2 norm. Similarly, the output feedback controller consists of a family of full-order linear, time-invariant controllers switched according to a stabilizing signal that depends only on the controller states. Both approaches are passive in the sense that they do not rely on the detection and/or the estimation of the faults. The proposed approaches are tested on a nonlinear model of a quadcopter. Simulation results show that satisfactory stability, tracking, and disturbance rejection are maintained despite of time-varying actuator faults.

This paper presents a non-linear model predictive control approach for offset-free tracking and the rejection of piece-wise constant disturbances. The approach involves augmenting the system’s state vector with the integral of the tracking error, enabling the design of a non-linear model predictive controller for this augmented system. Nominal closed-loop stability is enforced thanks to a terminal equality constraint and proven by a Lyapunov argument. Compared to the existing offset-free approaches in the literature, our method offers greater simplicity, as it does not rely on linear approximations of the system to control. Furthermore, it eliminates the need to estimate disturbances, a task that is especially challenging with non-linear systems. Comprehensive simulations and experimental tests are conducted according to a non-linear, coupled, two-tank laboratory experiment, demonstrating the robustness and effectiveness of the proposed approach.

In this paper, we introduce Green Iterative Learning Control (Green ILC), an innovative hybrid control method that addresses the critical need for energy-efficient control in dynamic, repetitive-task environments. By integrating the iterative refinement capabilities of traditional Iterative Learning Control (ILC) with the optimization strengths of gradient descent, Green ILC achieves a balanced trade-off between tracking accuracy and energy consumption. This novel approach introduces a cost function that minimizes both tracking errors and control effort, enabling the system to adaptively optimize performance over iterations. Theoretical analysis and simulation results demonstrate that Green ILC not only achieves faster convergence but also provides significant energy savings compared with traditional ILC methods. Notably, Green ILC reduces energy consumption by prioritizing efficiency, making it particularly suitable for energy-intensive applications such as robotics, manufacturing, and process control. While a slight decrease in tracking accuracy is observed, this trade-off is acceptable for scenarios where energy efficiency is paramount. This work establishes Green ILC as a promising solution for modern industrial systems requiring robust and sustainable control strategies.

This paper proposes Social-only Particle Swarm Optimization-based Iterative Learning Control (SO-PSO-ILC) to address the limitations of conventional Iterative Learning Control (ILC) in model dependency and manual parameter tuning. The proposed method autonomously optimizes the learning gain using a social-only PSO variant. Comparative results on four distinct trajectories demonstrate superior performance: SO-PSO-ILC achieved a final RMSE of 0.0008 m in the linear path test and a precision 4.6 times higher than the baseline in the waveform path test. It also exhibits the fastest convergence rate, outperforming PSO-ILC in tracking accuracy and computational complexity while avoiding the convergence issues observed in WSA-ILC. The simulation results validate that swarm-optimized ILC provides a robust framework for repetitive tasks requiring high accuracy.