Optimal Preview Control in Dynamic Systems
Summary
Optimal preview control enables dynamic systems to anticipate future reference signals or disturbances by incorporating a finite horizon of preview information into the control law. By augmenting the system state with known future trajectories, a preview controller combines state feedback and feedforward actions to minimise a global performance index, typically quadratic in states and inputs. This approach generalises classical control schemes by leveraging previewed data to improve tracking accuracy, disturbance rejection and robustness. Central to its formulation is the construction of an augmented error system that embeds future reference terms, enabling the design of control gains via Riccati equations, linear matrix inequalities or dynamic programming. Applications span aerospace trajectory optimisation, precision motion control in manufacturing and networked control systems where communication delays and packet losses can be mitigated by preview knowledge of scheduled signals. Recent advances address nonlinearities, time‐varying parameters and stochastic disturbances, extending preview control to a broad class of practical systems with guaranteed stability and performance bounds.
Research from Nature Portfolio
No recent Nature Portfolio content available.
Research from all publishers
Recent work has extended preview control to uncertain discrete‐time systems subject to polytopic uncertainties by integrating a repetitive control structure with guaranteed‐cost design. This method uses an augmented system with forward‐difference operators and formulates the synthesis problem as an LMI feasibility test, yielding controllers that ensure asymptotic stability and specified performance levels. Another line of research explored optimal preview tracking for linear discrete‐time periodic systems. By applying a lifting technique to transform periodic dynamics into an equivalent time‐invariant representation, controllers were derived that combine integrator, state feedback and preview feedforward to enhance tracking accuracy under periodic coefficient variations. Foundational studies have also addressed continuous‐time systems with state and input delays, transforming delayed dynamics into nondelayed augmented systems through signal transformations and designing preview controllers that converge to the optimal law as the preview horizon vanishes. These contributions underscore the versatility of preview control across discrete and continuous domains, highlighting robust design via LMI methods and improved transient response in the presence of uncertainties and periodic variations.
Optimal Preview Control in Dynamic Systems publication trend
The graph below shows the total number of articles in optimal preview control in dynamic systems across all publications each year (not limited to Nature Index journals).
Technical terms
Preview control: A control strategy that incorporates known future values of reference or disturbance signals into the control law to improve performance.
Augmented error system: A reformulated system that embeds tracking error and preview information into an extended state vector for controller synthesis.
Linear matrix inequality (LMI): A convex optimisation framework used to derive controller gains ensuring stability and performance criteria.
Riccati equation: A differential or difference equation whose solution yields optimal state feedback gains in quadratic cost minimisation.
References
- Robust Guaranteed-Cost Preview Repetitive Control for Polytopic Uncertain Discrete-Time Systems †. Algorithms (2019).
- Optimal Preview Control for Linear Discrete‐Time Periodic Systems. Mathematical Problems in Engineering (2019).
- Application of the Preview Control Method to the Optimal Tracking Control Problem for Continuous‐Time Systems with Time‐Delay. Mathematical Problems in Engineering (2015).
About these summaries
This Nature Research Intelligence Topic summary is created with the cited references and a large language model. We take care to ground generated text with facts, and have systems in place to gain human feedback on the overall quality of the process in line with our AI principles. We strive to create accurate and useful summaries for people unfamiliar with the research topic and that supports this goal. These pages are a beta release and will be updated as we learn how best to help people gain value from a research topic summary.
Turn complex research questions into confident strategic decisions
When you're under pressure to set direction, justify investment, or understand your competitive position, you need more than raw data — you need trusted insights you can act on.
Benchmark your performance against global peers using robust, methodologically sound analysis.
Combine quantitative metrics with qualitative expert insight to uncover strengths, gaps and emerging opportunities.
Gain tailored, decision-ready recommendations aligned to your strategic priorities.
Talk to us to learn more about our data dashboards and bespoke strategy reports.
Grow research skills, confidence and careers with training built for every stage of the research lifecycle.
Developed with Nature Portfolio journal Editors and internationally renowned experts. Discover three ways to learn:
Self-paced, online courses in convenient bite-sized units, covering key skills across scientific writing, publishing, grant writing, data analysis, and more.
Expert trainer-led workshops with hands-on exercises and real-time feedback across core research skills, delivered via interactive group sessions.
Editor-led workshops combining core principles in writing and publishing, personalised 1:1 feedback from Nature Portfolio Editors and hands-on exercises.
Explore course catalogues and workshop agendas, enquire about the options or request institutional pricing.