A Robust Model Predictive Control using a feedforward structure for a hybrid hydraulic piezo actuator in camless internal combustion engines

This paper extends efficient, robust model predictive control (MPC) approaches for linear parameter varying (LPV) systems to tracking scenarios. A dual mode approach is used and future information about target changes is included in the optimisation tracking problem. The controller guarantees recursive feasibility by adding an artificial target as an extra degree of freedom. Convergence to admissible targets is ensured by constructing a robustly invariant set to track any admissible target. The efficacy of the proposed algorithm is demonstrated by MATLAB simulations.

Reconfiguration of flow-based networks with back-up components using robust economic MPC

A novel robust model predictive control strategy is presented for a class of discrete-time time-varying system with delays. The modified quadratic Lyapunov-Krasovskii functionals for uncertain discrete-time system with both states and input delays is proposed. A robust model predictive control law is derived such that the closed-loop system is asymptotically stable and a dasiaworst-casepsila infinite horizon performance objective is minimized. A feasible state feedback model predictive control law is obtained by using linear matrix inequalities approach. And then, a convex optimization problem is formulated to design the robust model predictive controller which minimizes the upper bound of the closed-loop performance objective. The robust model predictive controller design is illustrated with numerical examples.

This study proposes a layered hybrid control framework for automation and robotics systems that combines Tube-based Robust MPC (RMPC) as a safety envelope, adaptive parameter estimation to narrow online uncertainty, Sliding Mode Control (SMC) as a high-resilience fail-safe, and Reinforcement Learning (RL) as a performance booster through a warm-start/residual policy scheme that is always constrained by Control Barrier Functions (CBF). The study aims to achieve better tracking performance and computational efficiency without sacrificing safety and constraint compliance. The methodology includes constrained nonlinear plant modeling, design of tube invariance-based RMPC with constraint tightening, RLS/concurrent learning-based adaptive estimator, SMC for infeasibility/model drift conditions, and a CBF-based safety filter formulated as an online small QP. Evaluation is done through ablation studies on multi-domain benchmarks and HIL-style tests, with finite-horizon cost, ITAE, constraint violation rate, and computational latency (average and WCET) metrics. The results show that the RL+CBF configuration reduces the finite-horizon cost by about 15.7% compared to pure RMPC, maintaining a violation rate of ~0.35% (better than RMPC: 0.80%) and significantly below that of unfiltered RL (4.20%). The warm-start RL scheme accelerates the completion time of the MPC solver by ≈15–18% over various prediction horizons and reduces the WCET (26.4 → 23.3 ms), supporting real-time implementation. These findings confirm that the integration of RMPC–Adaptive–SMC–RL(+CBF) effectively bridges model-based optimality, online adaptivity, robust resilience, and learning from data in a single architecture that is safety-certified and feasible for real-world applications demanding high uptime.

This contribution deals with an adaptive sliding mode control for a hybrid actuator consisting of a piezo, a mechanicacal and a hydraulic part that can be used for camless engine motor applications. The control structure comprises a feedforward controller and a sliding mode controller. The general approach of this actuator is to use the advantages of both systems, the high precision of the piezoelectric actuator and the force of the hydraulic part. In fact, piezoelectric actuators (PEAs) are commonly used for precise positioning, despite PEAs present nonlinearities, such as hysteresis, saturations, and creep. A sliding mode control is proposed and for deriving the structure of such a controller a Lyapunov approach is used. An adaptive self-tuning algorithm is realised. The conceived sliding mode control takes the hydraulic actuator in a resonance operating point which corresponds to the rotational speed of the engine. When the engine speed changes, the sliding mode controller adapts its parameter in a way that the resonance frequency of the controlled hydraulic part of the actuator changes and corresponds to the working frequency of the engine. The resulting controller is therefor totally self-tuning and robust with respect to the model parameter variation. Asymptotic tracking is shown using Lyapunov approach. Moreover, the proposed technique avoids a switching function for the calculation of the equivalent signal of the sliding mode controller. In this way the chattering problem is completely avoided. Simulations with real data of a camless engine are presented.

Constant-air-volume (CAV) air-conditioning systems consist mainly of two local processes: an air-handling process and a room temperature process. A robust model predictive control (RMPC) strategy was developed for CAV air-conditioning systems, which adopted two uncertain first-order plus time-delay models to describe the dynamics of the local processes and used a linear matrix inequality (LMI)-based optimisation technique to optimise the control law. This paper develops a new control design, which reformulates the prediction models by shifting the uncertainties of the first model into the second one, and then uses the reformulated prediction models in the RMPC strategy. This paper will show that compared with the original design, the new control design can enhance the feasibility of the optimisation of control law, reduce the computational burden of the optimisation and also remove the requirement of a sensor for supply air temperature in the original design. Practical applications: The new design method is a further development of a RMPC strategy presented in Xu et al.13 It inherits the benefits of the original control design for practical application, i.e. uncertainties and constraints can be dealt with simultaneously in the design and the robustness of the controlled system can be enhanced. The new design improves the optimisation feasibility, reduces the computational complexity and does not need to measure the supply air temperature. When the new design is adopted to replace the traditional PI control, there is no necessity to change the existent input structure of the PI control. Hence, the new design can be realised in practice easier than the original design.

Robust model predictive control and PID control of shell-and-tube heat exchangers

Piezoelectric actuator (PEA) is one of the key component in micro/nano manipulation equipment. However, the inherent hysteresis significantly limits its motion accuracy. In this paper, a Prandtl-Ishlinskii (PI) based feedforward and feedback compound control strategy is designed to eliminate the hysteretic nonlinearity of PEA, and thus linearizing the input/output relationship. An inverse hysteresis model is directly identified and validated using the open loop characteristics of the PEA. The inverse hysteresis model is used as the feedforward compensator. In order to further improve the performance of the PEA, a feedforward and feedback compound control strategy is also designed and tested. A PEA equipped with strain gauges is selected in the experimental investigation. Experimental results show that the hysteresis can be successfully compensated using only feedforward control, and better performance is achieved using the feedforward and feedback compound control.

We present a Robust Model Predictive Control (RMPC) approach for multi-carrier microgrids, i.e., microgrids based on gas and electricity. The microgrid that we consider includes thermal loads, electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power plants. Moreover, the system under control is affected by several external disturbances, e.g., uncertainty in renewable energy generation, electrical and thermal demand. The goal of the controller is to minimize the overall economical cost and the energy exchange with the main grid, while guaranteeing comfort. Whereas several RMPC methods have been developed for electrical or thermal microgrids, little or no attention has been devoted to robust control of multi-carrier microgrids. Therefore, we consider a novel RMPC algorithm that can improve the performance with respect to classical deterministic Model Predictive Control (Det-MPC) controllers in the context of multi-carrier microgrids. The RMPC method relies on the box-uncertainty-set robust optimization, where uncertain parameters are assumed to take their values from different intervals independently. The RMPC approach is able to successfully satisfy the constraints even in the presence of the mentioned disturbances. Simulations of a realistic residential case study show the benefits of the proposed approach with respect to Det-MPC controllers.

ABSTRACTIn this work, synthesis of robust distributed model predictive control (MPC) is presented for a class of linear systems subject to structured time-varying uncertainties. By decomposing a global system into smaller dimensional subsystems, a set of distributed MPC controllers, instead of a centralised controller, are designed. To ensure the robust stability of the closed-loop system with respect to model uncertainties, distributed state feedback laws are obtained by solving a min–max optimisation problem. The design of robust distributed MPC is then transformed into solving a minimisation optimisation problem with linear matrix inequality constraints. An iterative online algorithm with adjustable maximum iteration is proposed to coordinate the distributed controllers to achieve a global performance. The simulation results show the effectiveness of the proposed robust distributed MPC algorithm.

In this paper, robust position control of a direct drive using a state space model predictive control (MPC) algorithm is presented. The proposed controller consists of a state feedback regulator and a feedforward controller. Their gains are obtained by minimizing a cost function that is a sum of the position tracking errors and the control cost over some user defined time horizons. The effects of the controller parameters on the dynamic performance and the robustness of the direct drive are investigated. To provide good estimates of the state variables in the presence of load disturbance, a new observer based on the receding horizon concept is also formulated. Experimental results are presented to demonstrate the effectiveness of the approach.

Control systems with digital communication between sensors, controllers and actuators are called Networked Control Systems (NCSs). In general, NCSs encounter with some problems such as packet dropouts and network induced delays. When plant uncertainty is added to the aforementioned problems, the design of the robust controller that is able to guarantee the stability becomes more complex. In this paper, a method based on Robust Model Predictive Control (RMPC) is proposed to overcome model uncertainty together with unknown delay caused in NCSs. The previous RMPC methods, called Normal RMPC, was proposed to compensate delay or model uncertainty, individually. Hereby, we propose a method, named Improved RMPC, to compensate the effects of delay and model uncertainty on NCSs, simultaneously. The proposed method is based on uncertainty polytope and LMIs (Linear Matrix Inequlities). Also, an experimental evaluation of time-delays in MODBUS networked control systems is proposed. The simulation results show the ability of the proposed method. For comparison, the Normal RMPC is applied as well. The results show that the Normal RMPC has an acceptable performance for time delay compensation, but its performance gets destroyed or even get unstable when the model uncertainty is also considered.

In this paper, a synthesis of model predictive control (MPC) algorithm is presented for uncertain systems subject to structured time‐varying uncertainties and actuator saturation. The system matrices are not exactly known, but are affine functions of a time varying parameter vector. To deal with the nonlinear actuator saturation, a saturated linear feedback control law is expressed into a convex hull of a group of auxiliary linear feedback laws. At each time instant, a state feedback law is designed to ensure the robust stability of the closed‐loop system. The robust MPC controller design problem is formulated into solving a minimization problem of a worst‐case performance index with respect to model uncertainties. The design of controller is then cast into solving a feasibility of linear matrix inequality (LMI) optimization problem. Then, the result is further extended to saturation dependent robust MPC approach by introducing additional variables. A saturation dependent quadratic function is used to reduce the conservatism of controller design. To show the effectiveness, the proposed robust MPC algorithms are applied to a continuous‐time stirred tank reactor (CSTR) process.

Robustness in identification.- Comments on model validation as set membership identification.- SM identification of model sets for robust control design from data.- Robust identification and the rejection of outliers.- Semi-parametric methods for system identification.- Modal robust state estimator with deterministic specification of uncertainty.- The role of experimental conditions in model validation for control.- Modeling and validation of nonlinear feedback systems.- Towards a harmonic blending of deterministic and stochastic frameworks in information processing.- Suboptimal conditional estimators for restricted complexity set membership identification.- Worst-case simulation of uncertain systems.- On performance limitations in estimation.- Design criteria for uncertain models with structured and unstructured uncertainties.- Robustness and performance in adaptive filtering.- Nonlinear identification based on unreliable priors and data, with application to robot localization.- Robust model predictive control: A survey.- Intrinsic performance limits of linear feedback control.- Puzzles in systems and control.- Robust regional pole placement: An affine approximation.- On achieving L p (?p) performance with global internal stability for linear plants with saturating actuators.- Control under structural constraints: An input-output approach.- Multi-objective MIMO optimal control design without zero interpolation.- Robustness synthesis in l 1: A globally optimal solution.- Optimal control of distributed arrays with spatial invariance.- Numerical search of stable or unstable element in matrix or polynomial families: A unified approach to robustness analysis and stabilization.- A convex approach to a class of minimum norm problems.- Quantified inequalities and robust control.- Dynamic programming for robust control: Old ideas and recent developments.- Robustness of receding horizon control for nonlinear discrete-time systems.- Nonlinear representations and passivity conditions in discrete time.- Robust ripple-free output regulation and tracking under structured or unstructured parameter uncertainties.- An experimental study of performance and fault-tolerance of a hybrid free-flight control scheme.

In this study, an approach to design robust distributed model predictive control (MPC) is proposed for polytopic uncertain networked control systems with time delays. To reduce the computational complexity and improve the flexibility, the entire system is decomposed into multiple smaller dimensional subsystems. For each subsystem, the proposed robust distributed MPC algorithm requires solving multiple linear matrix inequality optimisation problems to minimise an upper bound on a robust performance objective. An augmented polytopic uncertainty description is invoked to handle the input delays. The conservativeness of distributed MPC algorithm is reduced by utilising a sequence of feedback control laws. An iterative on‐line algorithm for robust distributed MPC is developed to coordinate the distributed MPC controllers. Convergence and robust stability of the proposed distributed MPC are investigated. A numerical example is carried out to demonstrate the effectiveness of the proposed algorithm.