A novel approach for performance improvement in Model Predictive Safety Certification with output tracking

When controlling difficult industrial processes characterised by open-loop instability and/or poor damping, predictive functional control (PFC) practitioners often face two critical design challenges. The first one arises due to the intrinsic simplicity of the PFC control algorithm that, instead of optimising the future control trajectory in real time like other predictive controllers, simply assumes constant control action, thereby producing unreliable model predictions in the long range and thus inconsistent closed-loop performance. The second issue is related to the controller tuning, which may become ambiguous and unsystematic due to the existence of an inconsistent relationship between the controller parameters and closed-loop behaviour. This paper presents a dual-loop control strategy that aims at mitigating both weaknesses simultaneously by combining the concepts of pre-stabilisation and relative tuning within the framework of predictive functional control. Two challenging industrial case studies have been analysed through computer simulations that successfully validate the efficacy of the proposal under various real world scenarios.

Modeling and control of PEMFC based on least squares support vector machines

A Thermorisk framework for the analysis of energy systems by combining risk and exergy analysis

Predictive functional control (PFC) is a cheap and simplified model predictive controller, which competes with PID in price and performance. While the tuning process in PFC for simple dynamics is well established and straightforward, it becomes far more ambiguous and often less effective for processes exhibiting challenging behaviour, such as poor damping, instability and/or non-minimum phase characteristics. In this paper, we present a relative PFC algorithm that, when implemented with pre-stabilised prediction dynamics if needed, simplifies performance tuning to merely adjusting one parameter. Furthermore, it provides far superior closed-loop control in practical scenarios, where the conventional PFC and PID fail to perform, as demonstrated with three simulation case studies.

This paper presents an efficient application of a model based predictive control in parallel mechanisms. A predictive functional control strategy based on a simplified dynamic model is implemented. Experimental results are shown for the H4 robot, a fully parallel structure providing 3 degrees of freedom (dof) in translation and 1 dof in rotation. Predictive functional control, computed torque control and PID control strategies are compared in complex machining tasks trajectories. The tracking performances are enlightened.

Different predictive control strategies have been validated on a DC motor. ICascaded predictive control, which consists of a cascaded loop where the traditional servo algorithms are replaced by PFCs, could enhance the cycle time. Predictive Functional Control alone is simpler to tune and can exhibit comparable performances, except that the controller is more sensitive to nonlinear phenomena such as dry friction, which were not taken into account into the model and generate a static error.

Novel high-safety aeroengine performance predictive control method based on adaptive tracking weight

Asymptotic output tracking in a class of causal nonminimum-phase uncertain nonlinear systems is addressed via higher order sliding modes. Local asymptotic stability of the output tracking-error dynamics is provided. An output reference profile and an external disturbance forcing the internal dynamics are defined by an unknown linear exosystem. Its characteristic polynomial (which is used in the output-tracking control algorithm) is identified online by using a higher order sliding mode parameter observer. A numerical example illustrates efficiency of the developed higher order sliding mode (HOSM) control algorithm.

The hydroelectric energy is one of the most important renewable energy in the world. It does not encounter the problem of population displacement and is not as expensive as solar or wind energy. However, the hydro electrical generating units are usually isolated from the grid network; thus, they require control to maintain of constant the power for any working conditions. The simulation model of hydropower plant was constructed based on mathematical equations that summarize the behavior of the hydropower plant. The simulation model of power plant is useful in stability studies. This paper, presents the approach of Generalized Predictive Control (GPC) is applied to a multivariable model of the part turbine/generator of hydropower plant. In this study, the standard multivariable (GPC) algorithm is presented. It is then applied to achieve sets points tracking of the outputs of the plant. A Multi Input Multi Output (MIMO) model is used for control purposes. A comparative study is carried out using the named controller’s multivariable Linear Quadratic Gaussian (LQG) and multivariable (GPC) Controls. The performance of the proposed controller is illustrated by a simulation example of hydropower plant. Encouraging results are obtained that motivate for further investigations.

Planning and control for autonomous vehicles usually are hierarchically separated. However, increasing performance demands and operating in highly dynamic environments requires a frequent re‐evaluation of the planning and tight integration of control and planning to guarantee safety, performance, and reliability. We propose an integrated hierarchical predictive control and planning approach to tackle this challenge. The planner and controller are based on repeated solutions of moving horizon optimal control problems. To increase flexibility and feasibility, the planner can choose different low‐layer controller modes for increased flexibility and performance instead of using a single controller with a large safety margin for collision avoidance under uncertainty. Planning is based on simplified system dynamics and safety, yet flexible operation is ensured by constraint tightening based on a mixed‐integer linear programming formulation. A cyclic horizon tube‐based model predictive controller guarantees constraint satisfaction for different control modes and disturbances. Examples of different modes are slow‐speed movement with high precision and fast‐speed movements with large uncertainty bounds. Allowing for different control modes reduces conservatism, while the hierarchical decomposition of the problem reduces the computational cost and enables real‐time implementation. We derive conditions for recursive feasibility to ensure constraint satisfaction and obstacle avoidance to guarantee safety and compatibility between the layers and modes. Simulation results illustrate the efficiency and applicability of the proposed hierarchical strategy.

In this paper, an improved predictive functional control (PFC) scheme for permanent magnet synchronous motor (PMSM) control system is proposed, on account of the standard PFC method cannot provides a satisfying disturbance rejection performance in the case of strong disturbances. The PFC-based method is first introduced in the control design of speed loop, since the good tracking and robustness properties of the PFC heavily depend on the accuracy of the internal model of the plant. However, in orthodox design of prediction model based control method, disturbances are not considered in the prediction model as well as the control design. A minimumorder observer (MOO) is introduced to estimate the disturbances, which structure is simple and can be realized at a low computational load. This paper adopted the MOO to observe the load torque, and the observations are then fed back into PFC model to rebuild it when considering the influence of perturbation. Therefore, an improved PFC strategy with torque compensation, called the PFC+MOO method, is presented. The validity of the proposed method was tested via simulation and experiments. Excellent results were obtained with respect to the speed trajectory tracking, stability, and disturbance rejection.

Predictive functional control (PFC) applied to an unstable system. An industrial application

Because the predictive models do not match the practical process, the traditional predictive functional control could not acquire better performance. Through real-time database acquires the process parameters, the characteristic model of process can be obtained using the identification method. The characteristic model, as the predictive model of predictive functional control, downlink the control system. Consider the predictive model came from the on-line identification, it can match practical process. The experimental results prove that using the method we can acquire better performance. On-line identification uses NLJ method which can improve the identification speed.

In heating, ventilating and air-conditioning (HVAC) systems, there exist severe nonlinearity, time-varying nature, disturbances and uncertainties. A new predictive functional control based on Takagi-Sugeno (T-S) fuzzy model was proposed to control HVAC systems. The T-S fuzzy model of stabilized controlled process was obtained using the least squares method, then on the basis of global linear predictive model from T-S fuzzy model, the process was controlled by the predictive functional controller. Especially the feedback regulation part was developed to compensate uncertainties of fuzzy predictive model. Finally simulation test results in HVAC systems control applications showed that the proposed fuzzy model predictive functional control improves tracking effect and robustness. Compared with the conventional PID controller, this control strategy has the advantages of less overshoot and shorter setting time, etc.

This paper considers a class of predictive functional control problems for nonlinear systems based on multi-LPV models. First, according to the piece-wise embedding principle, multiple Linear Parameters-Varying (LPV) models have been applied to approximate describe the nonlinear system. For each LPV model, a predictive functional controller based on a linear combination of known basis functions is designed. Then a switching law between each controller is designed, to guarantee the stability of the whole system. Compared with traditional state feedback controller, the predictive functional controller has better control effect and robustness. Therefore, the proposed algorithm has less computation and less conservation. Finally, a simulation example is given to verify the effectiveness of the proposed algorithm.